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BCA Image Processing

bcasemester 7Elective
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Image Processing

Subject Code: CACS404

Course Title: Image Processing

Course No: CACS404

Nature of Course: Theory & Practical

Full Marks: 60+20+20

Pass Marks: 24+8+8

Credit Hours: 3

Course Description

This course presents introduction to several topics on image processing techniques and their applications. It also explores the students to real-world applications of image processing.

Course Objective

Upon completion of this course, students should be able to 1. Explain the basic concepts of digital image processing and various image transforms. 2. Develop a broad range of image processing techniques and their applications. 3. To familiarize the with the image enhancement, image restoration and image segmentation techniques.

Course Contents

Unit 1. Fundamental of Image processing   8 Hrs.

Image representation, basic relationship between pixels, elements of DIP system, elements of visual perception-simple image formation model, Sampling and Quantization, Color fundamentals and models, File Formats, Image operations, Brightness, contrast, hue, saturation, Mach band effect

Unit 2. Image Enhancement   12 Hrs.

Image Transforms, Fourier Transform and Discrete Fourier Transform, Fast Fourier Transform, Cosine Transform, Frequency domain image enhancement, low pass filtering, high pass filtering, homomorphic filter, Gaussian filter, Spatial domain image enhancement, point processing, contrast stretching, clipping and thresholding, digital negative, intensity level slicing, Histogram processing: equalization, modification, Spatial filtering – averaging, Smoothing and sharpening, median filtering, spatial low, high and band pass filters

Unit 3. Image Restoration   9 Hrs.

Image Restoration, Image degradation model, Noise modeling, Blur, Inverse filtering- removal of blur caused by uniform linear motion, Weiner filtering, Morphological operation, erosion and dilation

Unit 4. Image coding and compression   9 Hrs.

Need for compression, redundancy, pixel coding, run length coding, Huffmancoding, Elements of information theory, Error free compression, Lossy compression, Image compression standards- JPEG& MPEG, wavelet based image compression

Unit 5. Image segmentation and feature extraction   10 Hrs.

Image Segmentation: Thresholding, Region based segmentation, edges, line and curve detection, edge operators, Image Features and Extraction, Types of features, feature extraction, Texture, Feature reduction algorithms, Image classification, clustering techniques, Case Studies in Image Security, Steganography and Digital watermarking, Visual effects, Case studies in Medical Imaging and remote sensing

Text Books

  1. Gonzalez Rafel C, Digital Image Processing, Pearson Education, 2009.
  2. S.Sridhar, Digital Image Processing, Oxford University Press, 2011

Reference Books

  1. Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, Second Edition, Thompson Learning, 2007

Lab Works

Laboratory work should be done covering all the topics listed above and a small project work should be carried out using the concept learnt in this course using software like matlab, python.