Computer Vision and Deep Learning detailed syllabus for Robotics & Automation Engineering (Robotics) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the Robotics 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.
For Robotics & Automation Engineering 5th Sem scheme and its subjects, do visit Robotics 5th Sem 2021 regulation scheme. For Professional Elective-IV scheme and its subjects refer to Robotics Professional Elective-IV syllabus scheme. The detailed syllabus of computer vision and deep learning is as follows.
Course Objectives:
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Unit I
IMAGE FORMATION AND CAMERA CALIBRATION 9
Basics: Sampling Theorem – Numerical Differentiation – Singular Value Decomposition Introduction to Vision, Terminologies of Fields, Comparison of Biological and Computer Vision, Projective Geometry Basics, Modelling of Geometric Image Formation, Modelling of Camera Distortion, Camera Calibration, Methods of Camera Calibration, Estimation of Projection Matrix, Experimental Performance Assessment in Computer Vision.
Unit II
3-D STRUCTURE AND MOTION 9
Computational Stereopsis – Geometry, Parameters – Correspondence Problem, Epipolar Geometry, Essential Matrix and Fundamental Matrix, Eight Point Algorithm – Reconstruction by T riangulation, Visual Motion – Motion Field of Rigid Objects – Optical Flow – Estimation of Motion Field – 3D Structure and Motion from Sparse and Dense Motion Fields – Motion Based Segmentation – Image Processing.
Unit III
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Unit IV
INTRODUCTION TO NEURAL NETWORKS 8
Introduction to Neural Networks, Philosophy and Types of Networks, Back propagation, Numerical Problems for Back Propagation, Multi-Layer Perceptrons, Numerical Problems Based on Perceptron, Conventional Neural Networks vs. Deep Learning in the Context of Computer Vision, Loss Function, Optimization, Higher-Level Representations, Image Features, Stochastic Gradient Descent
Unit V
DEEP LEARNING 10
Convolutional Neural Networks – Convolution, Pooling, Activation Functions, Initialization, Dropout, Batch Normalization, Deep Learning Hardware – CPU, GPU and TPU -Tuning Neural Networks, Best Practices, Training Neural Networks, Update Rules, Ensembles, Data Augmentation, Transfer Learning, Popular CNN Architectures for Image Classification – Alexnet, VGG, Resnet, , Inception, CNN Architectures for Object Detection – RCNN and Types – Yolo -Semantic Segmentation – FCN, Instance Segmentation – Mask RCNN – Deep Learning frameworks.
Course Outcomes:
Upon successful completion of the course, students should be able to:
- Process and practice the basic images.
- Develop the 3-Dimensional structures and motions.
- Model the visual serving for robotic applications
- Acquire and practice the basic neural networks.
- Develop and train the deep learning networks for image processing.
Text Books:
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Reference Books:
- Rafael C. Gonzales, Richard.E.Woods, “Digital Image Processing”, 3rd edition, Gatesmark Publishing, Tenessee 2020.
- Emanuele Trucco, Alessandro Verri, “Introductory Techniques for 3D Computer Vision”, Prentice Hall, 1998.
- Ian Goodfellow and YoshuaBengio and Aaron Courville, “Deep Learning”, First Edition, MIT Press, 2018.
- Forsyth and Ponce, “Computer Vision: A Modern Approach”, 2nd edition Pearson, Harlow Uk 2015.
For detailed syllabus of all the other subjects of Robotics & Automation Engineering 5th Sem, visit Robotics 5th Sem subject syllabuses for 2021 regulation.
For all Robotics & Automation Engineering results, visit Anna University Robotics all semester results direct link.