color models

Extra video:  IIT kharagpur

 

basic relationship between pixels

 

Relationship Between Pixels : Dr Manjusha Deshmukh

detailed explanation with examples on 4 neighbors, diagonal neighbors, 8 neighbors, connectivity/adjacency.Basic relationship between pixel in digital image …

Distance Measures in Image Processing : Dr Manjusha Deshmukh

This Video Explain different distance measures in detail with solved example. It explain Euclidean distance, chessboard distance and city block distance with…

Unit 1 (part1)

Unit 1

Digital Image Processing – Introduction to Digital Image Processing

Subject – Image Processing and Machine VisionVideo Name – Digital Image ProcessingChapter – Introduction to Digital Image ProcessingFaculty – Prof. Vaibhav P…

Origins of Digital Image Processing – Introduction to Digital Image Processing

Subject – Image Processing and Machine VisionVideo Name – Origins of Digital Image ProcessingChapter – Introduction to Digital Image ProcessingFaculty – Prof…

Applications of Digital Image Processing – Introduction to Digital Image Processing

Subject – Image Processing and Machine VisionVideo Name – Applications of Digital Image ProcessingChapter – Introduction to Digital Image ProcessingFaculty -…

Fundamental Steps in Digital Image Processing – Introduction to Digital Image Processing

Subject – Image Processing and Machine VisionVideo Name – Fundamental Steps in Digital Image ProcessingChapter – Introduction to Digital Image ProcessingFacu…

Components of Image Processing System – Introduction to Digital Image Processing

Components of Image Processing System Video Lecture from Introduction to Digital Image Processing Chapter of Digital Image Processing Subject for all Enginee…

Structure of Human Eye – Digital Image Fundamentals – Image Processing and Machine Vision

Subject – Image Processing and Machine VisionVideo Name – Structure of Human EyeChapter – Digital Image FundamentalsFaculty – Prof. Vaibhav PanditWatch the v…

Image Formation in The Eye – Digital Image Fundamentals – Image Processing and Machine Vision

Subject – Image Processing and Machine VisionVideo Name – Image Formation in The EyeChapter – Digital Image FundamentalsFaculty – Prof. Vaibhav PanditWatch t…

Image Sensing and Image Acquisition – Digital Image Fundamentals – Image Processing & Machine Vision

Subject – Image Processing and Machine VisionVideo Name – Image Sensing and Image AcquisitionChapter – Digital Image FundamentalsFaculty – Prof. Vaibhav Pand…

Image Sampling and Quantization – Digital Image Fundamentals – Image Processing and Machine Vision

Subject – Image Processing and Machine VisionVideo Name – Image Sampling and QuantizationChapter – Digital Image FundamentalsFaculty – Prof. Vaibhav PanditWa…

Classification of Digital Images – Digital Image Fundamentals – Digital Image Processing

Classification of Digital Images Video Lecture from Digital Image Fundamentals Chapter of Digital Image Processing Subject for all Engineering Students.Acces…

Types of Images – Digital Image Fundamentals – Image Processing and Machine Vision

Subject – Image Processing and Machine VisionVideo Name – Types of ImagesChapter – Digital Image FundamentalsFaculty – Prof. Vaibhav PanditWatch the video le…

Image File Formats – Digital Image Fundamentals – Image Processing

Subject – Image ProcessingVideo Name – Image File FormatsChapter – Digital Image FundamentalsFaculty – Prof. Vaibhav PanditWatch the video lecture on the Top…

Image Resolution – Digital Image Fundamentals – Image Processing and Machine Vision

Subject – Image Processing and Machine VisionVideo Name – Image ResolutionChapter – Digital Image FundamentalsFaculty – Prof. Vaibhav PanditWatch the video l…

Sampling and Quantisation of Digital image

 

Digital Image Processing Lab Syllabus

 Course Title: Digital Image Processing Lab 

Semester VI 

Course Code BTITL608 

Course Type Mandatory 

Pre-requisite OOP with C++ 

L – T – P 0 – 0 – 2 Stream 

Core Credits 1 

Lab Experiments List: Study of “Matlab/Scilab or any other open source Image Processing Toolbox” and experiments as per the syllabus, to be decided by the concerned faculty.

Digital Image Processing Syllabus

 Course Title: Digital Image Processing 

Semester VI 

Course Code BTITC604 

Course Type Elective 

Pre-requisite

L – T – P - 3 – 0 – 0 

Stream Core Credits 3

Course Objectives: 1. To cover the fundamentals and mathematical models in digital image and video processing. 2. To develop time and frequency domain techniques for image enhancement. 3. To expose the students to current technologies and issues in image and video processing. 4. To develop image and video processing applications in practice. 

Course Outcomes: At the end of this course, students will be able to: 1. Understand theory and models in Image and Video Processing. 2. Interpret and analyze 2D signals in frequency domain through image transforms. 3. Apply quantitative models of image and video processing for various engineering applications. 4. Develop innovative design for practical applications in various fields. 

Course Content: 

UNIT I Image fundamentals: Image acquisition, sampling and quantization, image resolution, basic relationship between pixels, color images, RGB, HSI and other models. 

UNIT II Two dimensional transforms: 2D-Discrete fourier transform, discrete cosine transform, Walsh Hadamard transform, Haar transform, KL transform, and discrete wavelet transform. 

UNIT III Spatial domain Processing: Point processing such as digital negative, contrast stretching, thresholding, gray level slicing, bit plane slicing, log transform and power law transform, neighbourhood processing such as averaging filters, order statistics filters, high pass filters and high boost filters, histogram equalization and histogram specification, frequency domain such as DFT for filtering, ideal, Gaussian and butterwort filters for smoothening and sharpening, and homomorphic filters. 

UNIT IV Image segmentation and morphology: Point, line and edge detection, edge linking using Hough transform and graph theoretic approach, thresholding, and region based segmentation, dilation, erosion, opening, closing, hit or miss transform, thinning and thickening, and boundary extraction on binary images. 

UNIT V Degradation model, noise models, estimation of degradation function by modelling, restoration using Weiner filters and inverse filters. 

UNIT VI Video formation, perception and representation: Digital video sampling, video frame classifications, I, P and B frames, notation, ITU-RBT 601 digital video formats, digital video quality measure, video capture and display: principle of colour video camera, video camera, digital video, sampling of video signals: required sampling rates, sampling in two dimensions and three dimensions, progressive virus interlaced scans, two dimensional motion estimation, block matching algorithms. 

Text Books: 1. Gonzales and Woods, "Digital Image Processing", Pearson Education, India, Third Edition. 2. Anil K.Jain, "Fundamentals of Image Processing", Prentice Hall of India, First Edition, 1989. 

Reference Books: 1. Ze-Nian Li and Mark S. Drew, "Fundamentals of Multimedia", PHI 2011. 2. Murat Tekalp, "Digital Video Processing", Pearson, 2010. 3. John W. Woods, "Multidimensional Signal, Image and Video Processing", Academic Press 2012. 6. A.I.Bovik, "Handbook on Image and Video Processing", Academic Press.