{"id":54985,"date":"2023-08-27T04:03:23","date_gmt":"2023-08-27T04:03:23","guid":{"rendered":"https:\/\/www.inspirenignite.com\/anna-university\/ccs338-computer-vision-syllabus-for-eie-2021-regulation-professional-elective-viii\/"},"modified":"2023-08-27T04:03:23","modified_gmt":"2023-08-27T04:03:23","slug":"ccs338-computer-vision-syllabus-for-eie-2021-regulation-professional-elective-viii","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/anna-university\/ccs338-computer-vision-syllabus-for-eie-2021-regulation-professional-elective-viii\/","title":{"rendered":"CCS338: Computer Vision syllabus for EIE 2021 regulation (Professional Elective-VIII)"},"content":{"rendered":"

Computer Vision detailed syllabus for Electronics & Instrumentation Engineering (EIE) for 2021 regulation curriculum has been taken from the Anna Universities<\/a> official website and presented for the EIE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below. <\/p>\n

For Electronics & Instrumentation Engineering 6th Sem scheme and its subjects, do visit EIE 6th Sem 2021 regulation scheme<\/a>. For Professional Elective-VIII scheme and its subjects refer to EIE Professional Elective-VIII syllabus scheme<\/a>. The detailed syllabus of computer vision is as follows. <\/p>\n

Course Objectives:<\/h4>\n

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Unit I<\/h4>\n

INTRODUCTION TO IMAGE FORMATION AND PROCESSING
\nComputer Vision – Geometric primitives and transformations – Photometric image formation – The digital camera – Point operators – Linear filtering – More neighborhood operators – Fourier transforms – Pyramids and wavelets – Geometric transformations – Global optimization.\n<\/p>\n

Unit II<\/h4>\n

FEATURE DETECTION, MATCHING AND SEGMENTATION
\nPoints and patches – Edges – Lines – Segmentation – Active contours – Split and merge – Mean shift and mode finding – Normalized cuts – Graph cuts and energy-based methods.\n<\/p>\n

Unit III<\/h4>\n

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Unit IV<\/h4>\n

3D RECONSTRUCTION
\nShape from X – Active rangefinding – Surface representations – Point-based representations-Volumetric representations – Model-based reconstruction – Recovering texture maps and albedosos.\n<\/p>\n

Unit V<\/h4>\n

IMAGE-BASED RENDERING AND RECOGNITION
\nView interpolation Layered depth images – Light fields and Lumigraphs – Environment mattes -Video-based rendering-Object detection – Face recognition – Instance recognition – Category recognition – Context and scene understanding- Recognition databases and test sets.\n<\/p>\n

Laboratory Experiments:<\/h4>\n<\/p>\n

Software needed:<\/i>
\nOpenCV computer vision Library for OpenCV in Python \/ PyCharm or C++ \/ Visual Studio or or equivalent<\/p>\n