assignment on digital image processing

Department of Computer Science and Engineering

Cs474/674 image processing and interpretation (fall 2023).

• M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision , Cengage Learning, 2015.
• S. Birchfield, Image Processing and Analysis , Cengage Learning, 2018.

Prerequisites

Course outline (tentative), exams and assignments, course policies, useful information.

• Math Review Material (from textbook)

Sample Exams

• Course Overview
• PGM Image File Format
• Introduction to Image Processing
• Math Review
• Fourier Transform (see also Fourier Transform Pairs 1 and Fourier Transform Pairs 2)
• Midterm Review
• Fast Fourier Transform (FFT)
• Convolution
• Sampling and Aliasing
• Image Restoration
• Image Compression (also, see Image Compression Techniques and Survey )
• Final Review
• Short Time Fourier Transform (STFT) (study chapters 1 and 2 from Wavelet Tutorial )
• No time to cover this but here is the link if you are interested in learning more about wavelets: Wavelets (also, see notes part 1 , and part 2 )
• Multiresolution Analysis

Homework Assignments

• Homework 7 (Wavelets - except problems 7.13 and 7.14) (7.9, p 521 >> 6.44, p 525) (7.12, 7.13, p 522 >> 6.36, 6.37, p 524) (7.14, p 522 >> 6.38, p 524) (7.16, p 522 >> 6.40, p 524) (7.19, p 523 >> 6.41, p 525) (7.21, p 523 >> 6.43, p 525) (7.24, p 523 >> 6.45, p 525) Solutions

Programming Assignments

• Pogramming Assignment 3 (Due date (extended): 11/20/2023) powerpoint file fft.c , documentation , Rect_128.txt
• Project 6 (Due date: 12/14/98) (download data) ( penny_head.gif and penny_tails.gif ) ( nickel_head.gif and nickel_tails.gif ) ( dime_head.gif and dime_tails.gif ) ( quarter_head.gif and quarter_tails.gif ) coins1.gif $0.36 coins2.gif $0.36 (same as coins1 - different lighting) coins3.gif $0.51 coins4.gif $0.51 (same as coins3 - different scale) coins5.gif $0.66 coins6.gif $0.65 (occlusion) coins8.gif $0.47 coins9.gif $0.50 coins10.gif $0.11 coins11.gif $0.11 (same as 10 - different scale) coins16.gif $0.36 coins17.gif $0.60 (different viewpoint)

Sample Presentation Topics (Graduate Students Only)

Presentation guidelines.

University of Utah

School of computing, digital image processing.

Fall Semester 2018

WEB 2230   MW 1:25-2:45

Instructor: Thomas C. Henderson

Overview of Course

Course objectives, prerequisites, course description, software used to support class.

• Required Materials
• Projects and Assignments

Class Schedule and Assignments

Grading information, assignment due time, assignment late policy, individual work, registration.

• American Disabilities Act
• Link to Course Info and Docs
• Link to Programming Tips
• Link to Code
• Link to Lectures
• Lab Report Format
• Link to Testing and Data
• Link to Problems and handin summary
• Link to Solutions
• College Guidelines

Survey Digital Image Processing basics :

• Image Representation Basics
• Enhancement
• Morphological Operations
• Convolutional Networks
• Deep Learning
• Segmentation: Features
• Object Representation and Description
• Object Recognition

Prerequisites: Full major status in Computer Science or Computer Engineering.

We will work on the problems and solutions of digital image processing. 

Students must develop codes in Matlab. 

Suggested Materials

We will use:

Digital Image Processing, Gonzalez and Woods, 4th Edition, (not required, but on reserve)

Assignments

There are 2 major types of assignments:

• Problem Assignments: Matlab functions must be developed and delivered through handin.
• Quizzes: Weekly (short) quizzes will be held during the semester.

Class Syllabus

The lectures will cover the text on the following schedule (may vary some during semester to accommodate progress):  

The lectures and assignments will cover the text as we progress through the semester.  Assignments will usually be handed out on Monday and due on a Wednesday after the material is covered.

Instructor  

Instructor:

Thomas C. Henderson , Professor

[email protected]

801-581-3601

801-585-3743

Office Hours (2871 WEB): By appointment.

Office Hours :

The grading distribution will be as follows:

• Problems:                                        70%
• In-Class Tests and Participation:    30%

You are expected to make a good effort on all assignments and in-class discussion based on a careful reading of the assigned material.  I will assign a grade based on how reasonable your solution is given the difficulty of the assignment, the time required, and the style and content of the solution.  My goal is to look at all your work, and to assign a grade based on your participation, effort and results.  It's better to ask questions before and during an assignment, than to try and understand what went wrong after it's due.  The proportions given above delineate how I intend to apportion the weight of the various work in the course.

Unless otherwise stated in an assignment, all assignments will be due by classtime on the assignment due date.   You should handin all functions developed for the assignment.  The time that we use for an assignment is the submit time.  

Policy and Appeals Procedure

See Academic misconduct page as well as the Code of Student Rights and Responsibilities, SoC policies page, or the Class Schedule for more details.

Appeals of Grades and other Academic Actions

If a student believes that an academic action is arbitrary or capricious he/she should discuss the action with the involved faculty member and attempt to resolve.  If unable to resolve, the student may appeal the action in accordance with the following procedure:

• Appeal to Department Chair who should be notified in writing within 40 working days; chair must notify student of a decision with 15 days.  If faculty member or student disagrees with decision, then,
• Appeal to Academic Appeals Committee (see flyers posted in MEB and EMCB for members of committee).  See II Section D, Code of Student Rights and Responsibilities for details on Academic Appeals Committee hearings.

No late work is accepted. 

The purpose of the assignments is to improve your skills at solving problems and demonstrating that you understand the class material. Collaboration with other class members is acceptable in understanding problems or software tools. For any individual assignments or work turned in, you must do your own work. Using someone else's work or giving someone else your work is considered plagiarism and will be dealt with using standard College and University procedures (i.e., failure of assignment and class). The SoC policy states: "As defined in the University Code of Student Rights and Responsibilities, academic misconduct includes, but is not limited to, cheating, misrepresenting one's work, inappropriately collaborating, plagiarism, and fabrication or falsification of information. It also includes facilitating academic misconduct by intentionally helping or attempting to help another student to commit an act of academic misconduct. A primary example of academic misconduct would be submitting as one's own, work that is copied from an outside source." (See cheating_policy.pdf and SoC_ack_form.pdf in Link to Class Info and Docs; also see university web page for the full academic calendar Academic misconduct page ).  

See university web page for the full academic calendar ( Calendar web page ).   See the university web page for a copy of the withdraw guidelines as well, or see the Student Code .

American with Disabilities Act ( ADA )

The University conforms to all standards of the ADA . If you wish to qualify for exemptions under this act, notify the Center for Disabled Students Services, 160 Union .  The University of Utah seeks to provide equal access to its programs, services and activities for people with disabilities.  If you will need accommodations in the class, reasonable prior notice needs to be given to the Center for Disability Services, 162 Olpin Union Building, 581-5020 (V/TDD).  CDS will work with you and the instructor to make arrangements for accommodations. All written information in this course can be made available in alternative format with prior notification to the Center for Disability   Services .

Digital Image Processing

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout, books and references.

• Digital Image Processing by Rafael C Gonzalez & Richard E Woods, 3rd Edition
• Fundamentals of Digital Image Processing by Anil K Jain
• Digital Image Processing by William K Pratt

Instructor bio

Prof. Prabir Kumar Biswas

Course certificate.

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Introduction to Digital Image Processing

Digital image processing basics.

• What is a Pixel?

Image Conversion

• MATLAB | RGB image representation
• How to Convert RGB Image to Binary Image Using MATLAB?
• YIQ Color Model in Computer Graphics
• How to Convert YIQ Image to RGB Image Using MATLAB?
• How to Convert RGB Image to YIQ Image using MATLAB?
• MATLAB | RGB image to grayscale image conversion
• MATLAB | Change the color of background pixels by OTSU Thresholding
• How to Converting RGB Image to HSI Image in MATLAB?
• How to Convert HSI Image to RGB Image in MATLAB?
• How to Partially Colored Gray Image in MATLAB?
• HSV Color Model in Computer Graphics
• How to Color Slicing Using HSV Color Space in MATLAB?

Image Filtering Techniques

• Spatial Filtering and its Types
• Frequency Domain Filters and its Types
• How to Remove Salt and Pepper Noise from Image Using MATLAB?
• How to Decide Window Size for a Moving Average Filter in MATLAB?
• Noise Models in Digital Image Processing
• How to Apply Median Filter For RGB Image in MATLAB?
• How to Linear Filtering Without Using Imfilter Function in MATLAB?
• Noise addition using in-built Matlab function
• Adaptive Filtering - Local Noise Filter in MATLAB
• Difference between Low pass filter and High pass filter
• MATLAB - Butterworth Lowpass Filter in Image Processing
• MATLAB - Ideal Lowpass Filter in Image Processing
• MATLAB | Converting a Grayscale Image to Binary Image using Thresholding
• Laplacian of Gaussian Filter in MATLAB
• What is Upsampling in MATLAB?
• Upsampling in Frequency Domain in MATLAB
• Convolution Shape (full/same/valid) in MATLAB
• Linear Convolution using C and MATLAB

Histogram Equalization

• Histogram Equalization in Digital Image Processing
• Histogram Equalization Without Using histeq() Function in MATLAB
• MATLAB | Display histogram of a grayscale Image
• What Color Histogram Equalization in MATLAB?
• Histogram of an Image

Object Identification and Edge Detection

• Functions in MATLAB
• Program to determine the quadrant of the cartesian plane
• How To Identifying Objects Based On Label in MATLAB?
• What is Image shading in MATLAB?
• Edge detection using in-built function in MATLAB
• Digital Image Processing Algorithms using MATLAB
• MATLAB - Image Edge Detection using Sobel Operator from Scratch
• Image Complement in Matlab
• Image Sharpening Using Laplacian Filter and High Boost Filtering in MATLAB

PhotoShop Effects in MATLAB

• What is Swirl Effect in MATLAB?
• What is Oil Painting in MATLAB?
• Cone Effect in MATLAB
• What is Glassy Effect in MATLAB?
• What is Tiling Effect in MATLAB?

Image Geometry, Optical Illusion and Image Transformation

• Matlab program to rotate an image 180 degrees clockwise without using function
• Image Resizing in Matlab
• Nearest-Neighbor Interpolation Algorithm in MATLAB
• Black and White Optical illusion in MATLAB
• MATLAB | Complement colors in a Binary image
• Discrete Cosine Transform (Algorithm and Program)
• 2-D Inverse Cosine Transform in MATLAB
• MATLAB - Intensity Transformation Operations on Images
• Fast Fourier Transformation for polynomial multiplication
• Gray Scale to Pseudo Color Transformation in MATLAB
• Piece-wise Linear Transformation
• Balance Contrast Enhancement Technique in MATLAB

Morphologiocal Image Processing, Compression and Files

• Boundary Extraction of image using MATLAB
• MATLAB: Connected Component Labeling without Using bwlabel or bwconncomp Functions
• Morphological operations in MATLAB
• Matlab | Erosion of an Image
• Auto Cropping- Based on Labeling the Connected Components using MATLAB
• Run Length Encoding & Decoding in MATLAB
• Lossless Predictive Coding in MATLAB
• Extract bit planes from an Image in Matlab
• How to Read Text File Backwards Using MATLAB?
• MATLAB - Read Words in a File in Reverse Order
• How to Read Image File or Complex Image File in MATLAB?

Image Coding, Comparison and Texture Features

• Digital Watermarking and its Types
• How To Hide Message or Image Inside An Image In MATLAB?
• How to Match a Template in MATLAB?
• Grey Level Co-occurrence Matrix in MATLAB
• MATLAB - Texture Measures from GLCM

Difference Between

• Difference Between RGB, CMYK, HSV, and YIQ Color Models
• Difference between Dilation and Erosion

Digital Image Processing means processing digital image by means of a digital computer. We can also say that it is a use of computer algorithms, in order to get enhanced image either to extract some useful information. 

 Digital image processing is the use of algorithms and mathematical models to process and analyze digital images. The goal of digital image processing is to enhance the quality of images, extract meaningful information from images, and automate image-based tasks.

The basic steps involved in digital image processing are:

• Image acquisition: This involves capturing an image using a digital camera or scanner, or importing an existing image into a computer.
• Image enhancement: This involves improving the visual quality of an image, such as increasing contrast, reducing noise, and removing artifacts.
• Image restoration: This involves removing degradation from an image, such as blurring, noise, and distortion.
• Image segmentation: This involves dividing an image into regions or segments, each of which corresponds to a specific object or feature in the image.
• Image representation and description: This involves representing an image in a way that can be analyzed and manipulated by a computer, and describing the features of an image in a compact and meaningful way.
• Image analysis: This involves using algorithms and mathematical models to extract information from an image, such as recognizing objects, detecting patterns, and quantifying features.
• Image synthesis and compression: This involves generating new images or compressing existing images to reduce storage and transmission requirements.
• Digital image processing is widely used in a variety of applications, including medical imaging, remote sensing, computer vision, and multimedia.

Image processing mainly include the following steps:

1.Importing the image via image acquisition tools;  2.Analysing and manipulating the image;  3.Output in which result can be altered image or a report which is based on analysing that image.

What is an image?

An image is defined as a two-dimensional function, F(x,y) , where x and y are spatial coordinates, and the amplitude of F at any pair of coordinates (x,y) is called the intensity of that image at that point. When x,y, and amplitude values of F are finite, we call it a digital image .  In other words, an image can be defined by a two-dimensional array specifically arranged in rows and columns.  Digital Image is composed of a finite number of elements, each of which elements have a particular value at a particular location.These elements are referred to as picture elements,image elements,and pixels .A Pixel is most widely used to denote the elements of a Digital Image.

Types of an image

• BINARY IMAGE – The binary image as its name suggests, contain only two pixel elements i.e 0 & 1,where 0 refers to black and 1 refers to white. This image is also known as Monochrome.
• BLACK AND WHITE IMAGE – The image which consist of only black and white color is called BLACK AND WHITE IMAGE.
• 8 bit COLOR FORMAT – It is the most famous image format.It has 256 different shades of colors in it and commonly known as Grayscale Image. In this format, 0 stands for Black, and 255 stands for white, and 127 stands for gray.
• 16 bit COLOR FORMAT – It is a color image format. It has 65,536 different colors in it.It is also known as High Color Format. In this format the distribution of color is not as same as Grayscale image.

A 16 bit format is actually divided into three further formats which are Red, Green and Blue. That famous RGB format.   

Image as a Matrix

As we know, images are represented in rows and columns we have the following syntax in which images are represented:   

The right side of this equation is digital image by definition. Every element of this matrix is called image element , picture element , or pixel.   

DIGITAL IMAGE REPRESENTATION IN MATLAB:

In MATLAB the start index is from 1 instead of 0. Therefore, f(1,1) = f(0,0).  henceforth the two representation of image are identical, except for the shift in origin.  In MATLAB, matrices are stored in a variable i.e X,x,input_image , and so on. The variables must be a letter as same as other programming languages. 

PHASES OF IMAGE PROCESSING:

1. ACQUISITION – It could be as simple as being given an image which is in digital form. The main work involves:  a) Scaling  b) Color conversion(RGB to Gray or vice-versa)  2. IMAGE ENHANCEMENT – It is amongst the simplest and most appealing in areas of Image Processing it is also used to extract some hidden details from an image and is subjective.  3. IMAGE RESTORATION – It also deals with appealing of an image but it is objective(Restoration is based on mathematical or probabilistic model or image degradation).  4. COLOR IMAGE PROCESSING – It deals with pseudocolor and full color image processing color models are applicable to digital image processing.  5. WAVELETS AND MULTI-RESOLUTION PROCESSING – It is foundation of representing images in various degrees.  6. IMAGE COMPRESSION -It involves in developing some functions to perform this operation. It mainly deals with image size or resolution.  7. MORPHOLOGICAL PROCESSING -It deals with tools for extracting image components that are useful in the representation & description of shape.  8. SEGMENTATION PROCEDURE -It includes partitioning an image into its constituent parts or objects. Autonomous segmentation is the most difficult task in Image Processing.  9. REPRESENTATION & DESCRIPTION -It follows output of segmentation stage, choosing a representation is only the part of solution for transforming raw data into processed data.  10. OBJECT DETECTION AND RECOGNITION -It is a process that assigns a label to an object based on its descriptor. 

OVERLAPPING FIELDS WITH IMAGE PROCESSING

According to block 1 ,if input is an image and we get out image as a output, then it is termed as Digital Image Processing.  According to block 2 ,if input is an image and we get some kind of information or description as a output, then it is termed as Computer Vision.  According to block 3 ,if input is some description or code and we get image as an output, then it is termed as Computer Graphics.  According to block 4 ,if input is description or some keywords or some code and we get description or some keywords as a output,then it is termed as Artificial Intelligence 

Advantages of Digital Image Processing:

• Improved image quality: Digital image processing algorithms can improve the visual quality of images, making them clearer, sharper, and more informative.
• Automated image-based tasks: Digital image processing can automate many image-based tasks, such as object recognition, pattern detection, and measurement.
• Increased efficiency: Digital image processing algorithms can process images much faster than humans, making it possible to analyze large amounts of data in a short amount of time.
• Increased accuracy: Digital image processing algorithms can provide more accurate results than humans, especially for tasks that require precise measurements or quantitative analysis.

Disadvantages of Digital Image Processing:

• High computational cost: Some digital image processing algorithms are computationally intensive and require significant computational resources.
• Limited interpretability: Some digital image processing algorithms may produce results that are difficult for humans to interpret, especially for complex or sophisticated algorithms.
• Dependence on quality of input: The quality of the output of digital image processing algorithms is highly dependent on the quality of the input images. Poor quality input images can result in poor quality output.
• Limitations of algorithms: Digital image processing algorithms have limitations, such as the difficulty of recognizing objects in cluttered or poorly lit scenes, or the inability to recognize objects with significant deformations or occlusions.
• Dependence on good training data: The performance of many digital image processing algorithms is dependent on the quality of the training data used to develop the algorithms. Poor quality training data can result in poor performance of the algorit  

Digital Image Processing (Rafael c. gonzalez)

 Reference books:

“Digital Image Processing” by Rafael C. Gonzalez and Richard E. Woods. “Computer Vision: Algorithms and Applications” by Richard Szeliski. “Digital Image Processing Using MATLAB” by Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins.

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Image Processing: Techniques, Types, & Applications [2023]

Deep learning has revolutionized the world of computer vision—the ability for machines to “see” and interpret the world around them.

In particular, Convolutional Neural Networks (CNNs) were designed to process image data more efficiently than traditional Multi-Layer Perceptrons (MLP).

Since images contain a consistent pattern spanning several pixels, processing them one pixel at a time—as MLPs do—is inefficient.

This is why CNNs that process images in patches or windows are now the de-facto choice for image processing tasks.

But let’s start from the beginning—

‍ Here’s what we’ll cover:

What is Image Processing?

• How Machines “See” Images?

Phases of Image Processing

Image processing techniques.

Turn images, PDFs, or free-form text into structured insights

Digital Image processing is the class of methods that deal with manipulating digital images through the use of computer algorithms. It is an essential preprocessing step in many applications, such as face recognition, object detection, and image compression.

Image processing is done to enhance an existing image or to sift out important information from it. This is important in several Deep Learning-based Computer Vision applications, where such preprocessing can dramatically boost the performance of a model. Manipulating images, for example, adding or removing objects to images, is another application, especially in the entertainment industry.

This paper addresses a medical image segmentation problem, where the authors used image inpainting in their preprocessing pipeline for the removal of artifacts from dermoscopy images. Examples of this operation are shown below.

The authors achieved a 3% boost in performance with this simple preprocessing procedure which is a considerable enhancement, especially in a biomedical application where the accuracy of diagnosis is crucial for AI systems. The quantitative results obtained with and without preprocessing for the lesion segmentation problem in three different datasets are shown below.

Types of Images / How Machines “See” Images?

Digital images are interpreted as 2D or 3D matrices by a computer, where each value or pixel in the matrix represents the amplitude, known as the “intensity” of the pixel. Typically, we are used to dealing with 8-bit images, wherein the amplitude value ranges from 0 to 255.

Thus, a computer “sees” digital images as a function: I(x, y) or I(x, y, z) , where “ I ” is the pixel intensity and (x, y) or (x, y, z) represent the coordinates (for binary/grayscale or RGB images respectively) of the pixel in the image.

Computers deal with different “types” of images based on their function representations. Let us look into them next.

1. Binary Image

Images that have only two unique values of pixel intensity- 0 (representing black) and 1 (representing white) are called binary images. Such images are generally used to highlight a discriminating portion of a colored image. For example, it is commonly used for image segmentation, as shown below.

2. Grayscale Image

Grayscale or 8-bit images are composed of 256 unique colors, where a pixel intensity of 0 represents the black color and pixel intensity of 255 represents the white color. All the other 254 values in between are the different shades of gray.

An example of an RGB image converted to its grayscale version is shown below. Notice that the shape of the histogram remains the same for the RGB and grayscale images.

3. RGB Color Image

The images we are used to in the modern world are RGB or colored images which are 16-bit matrices to computers. That is, 65,536 different colors are possible for each pixel. “RGB” represents the Red, Green, and Blue “channels” of an image.

Up until now, we had images with only one channel. That is, two coordinates could have defined the location of any value of a matrix. Now, three equal-sized matrices (called channels), each having values ranging from 0 to 255, are stacked on top of each other, and thus we require three unique coordinates to specify the value of a matrix element.

Thus, a pixel in an RGB image will be of color black when the pixel value is (0, 0, 0) and white when it is (255, 255, 255). Any combination of numbers in between gives rise to all the different colors existing in nature. For example, (255, 0, 0) is the color red (since only the red channel is activated for this pixel). Similarly, (0, 255, 0) is green and (0, 0, 255) is blue.

An example of an RGB image split into its channel components is shown below. Notice that the shapes of the histograms for each of the channels are different.

4. RGBA Image

RGBA images are colored RGB images with an extra channel known as “alpha” that depicts the opacity of the RGB image. Opacity ranges from a value of 0% to 100% and is essentially a “see-through” property.

Opacity in physics depicts the amount of light that passes through an object. For instance, cellophane paper is transparent (100% opacity), frosted glass is translucent, and wood is opaque. The alpha channel in RGBA images tries to mimic this property. An example of this is shown below.

The fundamental steps in any typical Digital Image Processing pipeline are as follows:

1. Image Acquisition

The image is captured by a camera and digitized (if the camera output is not digitized automatically) using an analogue-to-digital converter for further processing in a computer.

2. Image Enhancement

In this step, the acquired image is manipulated to meet the requirements of the specific task for which the image will be used. Such techniques are primarily aimed at highlighting the hidden or important details in an image, like contrast and brightness adjustment, etc. Image enhancement is highly subjective in nature.

3. Image Restoration

This step deals with improving the appearance of an image and is an objective operation since the degradation of an image can be attributed to a mathematical or probabilistic model. For example, removing noise or blur from images.

4. Color Image Processing

This step aims at handling the processing of colored images (16-bit RGB or RGBA images), for example, peforming color correction or color modeling in images.

5. Wavelets and Multi-Resolution Processing

Wavelets are the building blocks for representing images in various degrees of resolution. Images subdivision successively into smaller regions for data compression and for pyramidal representation.

6. Image Compression

For transferring images to other devices or due to computational storage constraints, images need to be compressed and cannot be kept at their original size. This is also important in displaying images over the internet; for example, on Google, a small thumbnail of an image is a highly compressed version of the original. Only when you click on the image is it shown in the original resolution. This process saves bandwidth on the servers.

7. Morphological Processing

Image components that are useful in the representation and description of shape need to be extracted for further processing or downstream tasks. Morphological Processing provides the tools (which are essentially mathematical operations) to accomplish this. For example, erosion and dilation operations are used to sharpen and blur the edges of objects in an image, respectively.

8. Image Segmentation

This step involves partitioning an image into different key parts to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation allows for computers to put attention on the more important parts of the image, discarding the rest, which enables automated systems to have improved performance.

9. Representation and Description

Image segmentation procedures are generally followed by this step, where the task for representation is to decide whether the segmented region should be depicted as a boundary or a complete region. Description deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.

10. Object Detection and Recognition

After the objects are segmented from an image and the representation and description phases are complete, the automated system needs to assign a label to the object—to let the human users know what object has been detected, for example, “vehicle” or “person”, etc.

11. Knowledge Base

Knowledge may be as simple as the bounding box coordinates for an object of interest that has been found in the image, along with the object label assigned to it. Anything that will help in solving the problem for the specific task at hand can be encoded into the knowledge base.

Automate repetitive tasks and complex processes with AI

Image processing can be used to improve the quality of an image, remove undesired objects from an image, or even create new images from scratch. For example, image processing can be used to remove the background from an image of a person, leaving only the subject in the foreground.

Image processing is a vast and complex field, with many different algorithms and techniques that can be used to achieve different results. In this section, we will focus on some of the most common image processing tasks and how they are performed.

Task 1: Image Enhancement

One of the most common image processing tasks is an image enhancement, or improving the quality of an image. It has crucial applications in Computer Vision tasks, Remote Sensing, and surveillance. One common approach is adjusting the image's contrast and brightness. 

Contrast is the difference in brightness between the lightest and darkest areas of an image. By increasing the contrast, the overall brightness of an image can be increased, making it easier to see. Brightness is the overall lightness or darkness of an image. By increasing the brightness, an image can be made lighter, making it easier to see. Both contrast and brightness can be adjusted automatically by most image editing software, or they can be adjusted manually.

However, adjusting the contrast and brightness of an image are elementary operations. Sometimes an image with perfect contrast and brightness, when upscaled, becomes blurry due to lower pixel per square inch (pixel density). To address this issue, a relatively new and much more advanced concept of Image Super-Resolution is used, wherein a high-resolution image is obtained from its low-resolution counterpart(s). Deep Learning techniques are popularly used to accomplish this.

For example, the earliest example of using Deep Learning to address the Super-Resolution problem is the SRCNN model, where a low-resolution image is first upscaled using traditional Bicubic Interpolation and then used as the input to a CNN model. The non-linear mapping in the CNN extracts overlapping patches from the input image, and a convolution layer is fitted over the extracted patches to obtain the reconstructed high-resolution image. The model framework is depicted visually below.

An example of the results obtained by the SRCNN model compared to its contemporaries is shown below.

Task 2: Image Restoration

The quality of images could degrade for several reasons, especially photos from the era when cloud storage was not so commonplace. For example, images scanned from hard copies taken with old instant cameras often acquire scratches on them.

Image Restoration is particularly fascinating because advanced techniques in this area could potentially restore damaged historical documents. Powerful Deep Learning-based image restoration algorithms may be able to reveal large chunks of missing information from torn documents.

Image inpainting, for example, falls under this category, and it is the process of filling in the missing pixels in an image. This can be done by using a texture synthesis algorithm, which synthesizes new textures to fill in the missing pixels. However, Deep Learning-based models are the de facto choice due to their pattern recognition capabilities.

An example of an image painting framework (based on the U-Net autoencoder) was proposed in this paper that uses a two-step approach to the problem: a coarse estimation step and a refinement step. The main feature of this network is the Coherent Semantic Attention (CSA) layer that fills the occluded regions in the input images through iterative optimization. The architecture of the proposed model is shown below.

Some example results obtained by the authors and other competing models are shown below.

Task 3: Image Segmentation

Image segmentation is the process of partitioning an image into multiple segments or regions. Each segment represents a different object in the image, and image segmentation is often used as a preprocessing step for object detection.

There are many different algorithms that can be used for image segmentation, but one of the most common approaches is to use thresholding. Binary thresholding, for example, is the process of converting an image into a binary image, where each pixel is either black or white. The threshold value is chosen such that all pixels with a brightness level below the threshold are turned black, and all pixels with a brightness level above the threshold are turned white. This results in the objects in the image being segmented, as they are now represented by distinct black and white regions.

In multi-level thresholding, as the name suggests, different parts of an image are converted to different shades of gray depending on the number of levels. This paper , for example, used multi-level thresholding for medical imaging —specifically for brain MRI segmentation, an example of which is shown below.

Modern techniques use automated image segmentation algorithms using deep learning for both binary and multi-label segmentation problems. For example, the PFNet or Positioning and Focus Network is a CNN-based model that addresses the camouflaged object segmentation problem. It consists of two key modules—the positioning module (PM) designed for object detection (that mimics predators that try to identify a coarse position of the prey); and the focus module (FM) designed to perform the identification process in predation for refining the initial segmentation results by focusing on the ambiguous regions. The architecture of the PFNet model is shown below.

The results obtained by the PFNet model outperformed contemporary state-of-the-art models, examples of which are shown below.

Task 4: Object Detection

Object Detection is the task of identifying objects in an image and is often used in applications such as security and surveillance. Many different algorithms can be used for object detection, but the most common approach is to use Deep Learning models, specifically Convolutional Neural Networks (CNNs).

CNNs are a type of Artificial Neural Network that were specifically designed for image processing tasks since the convolution operation in their core helps the computer “see” patches of an image at once instead of having to deal with one pixel at a time. CNNs trained for object detection will output a bounding box (as shown in the illustration above) depicting the location where the object is detected in the image along with its class label.

An example of such a network is the popular Faster R-CNN ( R egion-based C onvolutional N eural N etwork) model, which is an end-to-end trainable, fully convolutional network. The Faster R-CNN model alternates between fine-tuning for the region proposal task (predicting regions in the image where an object might be present) and then fine-tuning for object detection (detecting what object is present) while keeping the proposals fixed. The architecture and some examples of region proposals are shown below.

Task 5: Image Compression

Image compression is the process of reducing the file size of an image while still trying to preserve the quality of the image. This is done to save storage space, especially to run Image Processing algorithms on mobile and edge devices, or to reduce the bandwidth required to transmit the image.

Traditional approaches use lossy compression algorithms, which work by reducing the quality of the image slightly in order to achieve a smaller file size. JPEG file format, for example, uses the Discrete Cosine Transform for image compression.

Modern approaches to image compression involve the use of Deep Learning for encoding images into a lower-dimensional feature space and then recovering that on the receiver’s side using a decoding network. Such models are called autoencoders , which consist of an encoding branch that learns an efficient encoding scheme and a decoder branch that tries to revive the image loss-free from the encoded features.

For example, this paper proposed a variable rate image compression framework using a conditional autoencoder. The conditional autoencoder is conditioned on the Lagrange multiplier, i.e., the network takes the Lagrange multiplier as input and produces a latent representation whose rate depends on the input value. The authors also train the network with mixed quantization bin sizes for fine-tuning the rate of compression. Their framework is depicted below.

The authors obtained superior results compared to popular methods like JPEG, both by reducing the bits per pixel and in reconstruction quality. An example of this is shown below.

Task 6: Image Manipulation

Image manipulation is the process of altering an image to change its appearance. This may be desired for several reasons, such as removing an unwanted object from an image or adding an object that is not present in the image. Graphic designers often do this to create posters, films, etc.

An example of Image Manipulation is Neural Style Transfer , which is a technique that utilizes Deep Learning models to adapt an image to the style of another. For example, a regular image could be transferred to the style of “Starry Night” by van Gogh. Neural Style Transfer also enables AI to generate art .

An example of such a model is the one proposed in this paper that is able to transfer arbitrary new styles in real-time (other approaches often take much longer inference times) using an autoencoder-based framework. The authors proposed an adaptive instance normalization (AdaIN) layer that adjusts the mean and variance of the content input (the image that needs to be changed) to match those of the style input (image whose style is to be adopted). The AdaIN output is then decoded back to the image space to get the final style transferred image. An overview of the framework is shown below.

Examples of images transferred to other artistic styles are shown below and compared to existing state-of-the-art methods.

Task 7: Image Generation

Synthesis of new images is another important task in image processing, especially in Deep Learning algorithms which require large quantities of labeled data to train. Image generation methods typically use Generative Adversarial Networks (GANs) which is another unique neural network architecture .

GANs consist of two separate models: the generator, which generates the synthetic images, and the discriminator, which tries to distinguish synthetic images from real images. The generator tries to synthesize images that look realistic to fool the discriminator, and the discriminator trains to better critique whether an image is synthetic or real. This adversarial game allows the generator to produce photo-realistic images after several iterations, which can then be used to train other Deep Learning models.

Task 8: Image-to-Image Translation

Image-to-Image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. For example, a free-hand sketch can be drawn as an input to get a realistic image of the object depicted in the sketch as the output, as shown below.

‍ Pix2pix is a popular model in this domain that uses a conditional GAN (cGAN) model for general purpose image-to-image translation, i.e., several problems in image processing like semantic segmentation, sketch-to-image translation, and colorizing images, are all solved by the same network. cGANs involve the conditional generation of images by a generator model. For example, image generation can be conditioned on a class label to generate images specific to that class.

Pix2pix consists of a U-Net generator network and a PatchGAN discriminator network, which takes in NxN patches of an image to predict whether it is real or fake, unlike traditional GAN models. The authors argue that such a discriminator enforces more constraints that encourage sharp high-frequency detail. Examples of results obtained by the pix2pix model on image-to-map and map-to-image tasks are shown below.

Key Takeaways

The information technology era we live in has made visual data widely available. However, a lot of processing is required for them to be transferred over the internet or for purposes like information extraction, predictive modeling, etc.

The advancement of deep learning technology gave rise to CNN models, which were specifically designed for processing images. Since then, several advanced models have been developed that cater to specific tasks in the Image Processing niche. We looked at some of the most critical techniques in Image Processing and popular Deep Learning-based methods that address these problems, from image compression and enhancement to image synthesis.

Recent research is focused on reducing the need for ground truth labels for complex tasks like object detection, semantic segmentation, etc., by employing concepts like Semi-Supervised Learning and Self-Supervised Learning , which makes models more suitable for broad practical applications.

If you’re interested in learning more about computer vision, deep learning, and neural networks, have a look at these articles:

• Deep Learning 101: Introduction [Pros, Cons & Uses]
• What Is Computer Vision? [Basic Tasks & Techniques]
• Convolutional Neural Networks: Architectures, Types & Examples

Rohit Kundu is a Ph.D. student in the Electrical and Computer Engineering department of the University of California, Riverside. He is a researcher in the Vision-Language domain of AI and published several papers in top-tier conferences and notable peer-reviewed journals.

“Collecting user feedback and using human-in-the-loop methods for quality control are crucial for improving Al models over time and ensuring their reliability and safety. Capturing data on the inputs, outputs, user actions, and corrections can help filter and refine the dataset for fine-tuning and developing secure ML solutions.”

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Homework assignments in ECE447 Digital Image Processing Fall 2015

robot010/Digital-Image-Processing

Folders and files, repository files navigation, digital-image-processing.

Traditional image processing implementation

Instructor: Marvin Doyley

Introduction

This course is a required image processing course at University of Rochester.

Course Assignment

Itensity Transformation.

• Histogram, Sampling and Aliasing .
• Assignment , My Implementation

Spatial Filtering.

• Median Filter, Low-Pass Filter, Fourier Transform and Gibbs Phenomenon

Fourier Domain Filtering

• Gaussian Low-pass, High-Pass, Band-Pass Filter; Butterworth Low-Pass, High-Pass Filters .
• Assignment ， My Implementation

Image Restoration and Reconstruction

• Image contamination with various filtering
• Radon and Inverse Radon transformation
• Reconstruct original image from contaminated image

Morphological Image Processing

• Image dilation, erosion, opening and closing
• Assignment , My Implementation .

Image Registration

• Affine Transformation, Projective Transformation

Final Project: Image Registration for High Dynamic Range Image Generation

• This project is done with Hao Xie and Tiecheng Su
• Generated High Dynamic Range Image from a series of low dynamic range images by doing global image registration , image de-ghosting , and multi-resolution image integration .
• Performed median threshold bitmap algorithm to detect ghosting area and highlight the ghosting area by using morphological image processing and cluster labelling.
• Calculated the contribution of each image to the final image based on three parameters: contrast, saturation and exposedness. Obtained three weight maps containing the weight of every pixel of each image.
• Blended the images together by multi-resolution image blending algorithm, generated the final result by collapsing the Laplacian pyramid of original images and Gaussian pyramid of weight maps. Rating final result by comparing with commercial software Photomatix®.
• Jupyter Notebook 100.0%

Course Name: Digital Image Processing

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Course abstract

Digital image processing deals with processing of images which are digital in nature. Study of the subject is motivated by three major applications. The first application is in improvement of pictorial information for human perception i.e. enhancing the quality of the image so that the image will have a better look. The second is for autonomous machine applications which have wider applications in industries, particularly for quality control in assembly automation and many similar applications. Another major application area is in efficient storage and transmission of images. This course will introduce various image processing techniques, algorithms and their applications.

Course Instructor

Prof. Prabir Kumar Biswas

Teaching assistant(s),  course duration : jul-oct 2021,   view course,  syllabus,  enrollment : 20-may-2021 to 02-aug-2021,  exam registration : 17-jun-2021 to 17-sep-2021,  exam date : 23-oct-2021,   course statistics will be published shortly, certificate eligible, certified category count, successfully completed, participation.

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• Assignment Score = Average of best 8 out of 12 assignments.
• Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score Note:We have taken best assignment score from both July 2020 and July 2021 courses

ABHIJIT CHANDRA 91%

Jadavpur University

KOTHA MANOHAR 90%

MATRUSRI ENGINEERING COLLEGE

POOJA PATHAK 90%

Bharat Electronics Limited

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SNEHAL SUNIL GAIKWAD 86%

DR. BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY

DIVYA VYAS 86%

MADHAV INSTITUTE OF TECHNOLOGY & SCIENCE

USHNISH SARKAR 85%

HOMI BHABHA NATIONAL INSTITUTE

KARTHICK D 85%

G PRASANNA KUMAR 84%

Vishnu Institute of Technology

VINEESH MODI 84%

SHIKHAR GUPTA 83%

AKANKSHA 82%

INSTITUTE OF RESEARCH DEVELOPMENT & TRAINING, U.P. KANPUR

SAKAMBHARI MAHAPATRA 81%

VEER SURENDRA SAI UNIVERSITY OF TECHNOLOGY, BURLA

ANKIT SONI 81%

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SREEPARNA GANGULY 80%

INDIAN INSTITUTE OF INFORMATION TECHNOLOGY KALYANI

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SAMUDA PRATHIMA 80%

R.M.K. COLLEGE OF ENGINEERING AND TECHNOLOGY

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MIKAEL HABTESELASSIE ASSEFA 80%

G D GOENKA UNIVERSITY

NANCY GUPTA 79%

ASHUTOSH NAGAR 79%

UNIVERSITY DEPARTMENTS, RAJASTHAN TECHNICAL UNIVERSITY

ALOK BARWAL 79%

CHANDIGARH UNIVERSITY MOHALI PUNJAB

SHUBHAM JAIN 79%

TANISHQ GUPTA 79%

MOORTHI M 79%

SAVEETHA ENGINEERING COLLEGE

PRADEEPKUMAR G 79%

KPR INSTITUTE OF ENGINEERING AND TECHNOLOGY

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Total enrollment: 4187, registration statistics, total registration : 753, assignment statistics, score distribution graph - legend, assignment score: distribution of average scores garnered by students per assignment., exam score : distribution of the final exam score of students., final score : distribution of the combined score of assignments and final exam, based on the score logic..

Assignment 5: Image Processing

• Loading digital images
• Image enhancement
• Image segmentation

Prerequisites (Before you start)

• Read Section 12 Notes .

Tuesday 18/12/2018

Joining to Assignment Repository

Refer to this sheet to know your group number :

• Go to the Assignment Page .
• Joint Group or make another group.
• Wait till your repository created.

Requirements

• Load color RGB image.
• Display the image on the screen.
• Convert the image to a grayscale image and display it.
• Get the histogram of the grayscale image and display it.
• Apply histogram equalization on the grayscale image and display the result.
• Get the histogram of the image after histogram equalization. Are they similar?
• Segment the grayscale image using image thresholding and display the result.
• Report all in a Markdown file.

Important Notes

• You are allowed to discuss task problems with your mates. But code must be on your own.
• You can get code lines from internet and include them in your own code and you must cite the source.
• Sharing few code lines of your own with your classmates is allowed for identifying and fixing bugs, it is not allowed to see others solution before submitting.
• Report must include summary about your implementation, sample results and issues that you faced and how you fixed it.
• You must mention any kind of contribution of other mates.

How to ask for help?

You can ask me to review your code, give an advice and fixing bugs. It is so easy, you have just to commit your buggy code and push it to github then mention me in the a comment and I will review the code.

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Electrical Engineering and Systems Science > Image and Video Processing

Title: splice -- streamlining digital pathology image processing.

Abstract: Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of Whole Slide Images (WSIs), there's a growing demand for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this paper, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a "collage" of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting non-redundant representative features. We evaluated SPLICE for search and match applications, demonstrating improved accuracy, reduced computation time, and storage requirements compared to existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%. This reduction enables numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.

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