{"id":7014,"date":"2019-12-14T16:49:17","date_gmt":"2019-12-14T16:49:17","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/digital-image-processing-eie-7th-sem-syllabus-for-vtu-be-2017-scheme\/"},"modified":"2019-12-14T16:49:17","modified_gmt":"2019-12-14T16:49:17","slug":"digital-image-processing-eie-7th-sem-syllabus-for-vtu-be-2017-scheme","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/digital-image-processing-eie-7th-sem-syllabus-for-vtu-be-2017-scheme\/","title":{"rendered":"Digital Image Processing EIE 7th Sem Syllabus for VTU BE 2017 Scheme"},"content":{"rendered":"

Digital Image Processing detail syllabus for Electronics & Instrumentation Engineering (Eie), 2017 scheme is taken from VTU<\/a> official website and presented for VTU students. The course code (17EI72), and for exam duration, Teaching Hr\/week, Practical Hr\/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.<\/p>\n

For all other eie 7th sem syllabus for be 2017 scheme vtu you can visit EIE 7th Sem syllabus for BE 2017 Scheme VTU Subjects<\/a>. The detail syllabus for digital image processing is as follows.<\/p>\n

Module 1 <\/h4>\n

Introduction: Background, Examples of fields that use DIP, Fundamental steps in Digital Image Processing (DIP), Components of DIP system, Image sensing and acquisition, A simple image formation model, Image sampling and quantization. Basic relationship between pixels, Color image processing fundamentals and models. Text: Chapter 1, 2.3, 2.4, .2.5\n<\/p>\n

Module 2 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Module 3 <\/h4>\n

Image Enhancement In Frequency Domain: Background, 2D-Discrete Fourier Transform and its inverse, Basic properties of the 2D-Discrete Fourier Transform, Basics of filtering in the frequency domain. Image smoothing using frequency domain filters: Ideal low pass filters, Butterworth low-pass filters, Gaussian low-pass filters; Image sharpening using frequency domain filters, Ideal high-pass filters, Butterworth high-pass filters, Gaussian high-pass filters, Homomorphic filtering. Text: 4.1, 4.2, 4.5.5, 4.6, 4.7,4.8, 4.9\n<\/p>\n

Module 4 <\/h4>\n

Image Restoration: Model of the Image degradation\/restoration process, Noise models, Restoration using spatial filtering: Mean filters, Order statistic filters – Median filter, Min and Max filters, Midpoint filter. Image Compression: Fundamentals, Image compression models, Basic compression methods – Huffman coding, Arithmetic coding, LZW coding, Run-length coding. Text: 5.1, 5.2, 5.3.1, 5.3.2, 8.1, 8.2.1, 8.2.3, 8.2.4, 8.2.5\n<\/p>\n

Module 5 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Course Outcomes:<\/h4>\n

After studying this course, students will be able to:<\/p>\n

    \n
  1. Describe the fundamentals of Image Processing and Image transform techniques.<\/li>\n
  2. Apply image enhancement technique in frequency and spatial domain<\/li>\n
  3. Analyze and implement restoration and color models.<\/li>\n
  4. Develop and analyze image compression techniques.<\/li>\n
  5. Apply segmentation algorithms for general image.<\/li>\n
  6. Develop image processing algorithms for real-world problems.<\/li>\n<\/ol>\n

    Question paper pattern:<\/h4>\n