PTCCS349 Syllabus - Image And Video Analytics - 2023 Regulation Anna University

PTCCS349 Syllabus - Image And Video Analytics - 2023 Regulation Anna University

PTCCS349

IMAGE AND VIDEO ANALYTICS

 L T P C

2 0 2 3

COURSE OBJECTIVES:
• To understand the basics of image processing techniques for computer vision.
• To learn the techniques used for image pre-processing.
• To discuss the various object detection techniques.
• To understand the various Object recognition mechanisms.
• To elaborate on the video analytics techniques.

UNIT I

INTRODUCTION

6

Computer Vision – Image representation and image analysis tasks - Image representations – digitization – properties – color images – Data structures for Image Analysis - Levels of image data representation - Traditional and Hierarchical image data structures.

UNIT II

IMAGE PRE-PROCESSING

6

Local pre-processing - Image smoothing - Edge detectors - Zero-crossings of the second derivative- Scale in image processing - Canny edge detection - Parametric edge models - Edges in multi- speralct images - Local pre-processing in the frequency domain - Line detection by local pre- processing operators - Image restoration.

UNIT III

OBJECT DETECTION USING MACHINE LEARNING

6

Object detection– Object detection methods – Deep Learning framework for Object detection– bounding box approach-Intersection over Union (IoU) –Deep Learning Architectures-R-CNN-Faster R-CNN-You Only Look Once(YOLO)-Salient features-Loss Functions-YOLO architectures

UNIT IV

FACE RECOGNITION AND GESTURE RECOGNITION

6

Face Recognition-Introduction-Applications of Face Recognition-Process of Face Recognition- DeepFace solution by Facebook-FaceNet for Face Recognition- Implementation using FaceNet- Gesture Recognition.

UNIT V

VIDEO ANALYTICS

6

Video Processing – use cases of video analytics-Vanishing Gradient and exploding gradient problem- RestNet architecture-RestNet and skip connections-Inception Network-GoogleNet architecture- Improvement in Inception v2-Video analytics-RestNet and Inception v3.

30 PERIODS

LIST OF EXERCISES: 30 PERIODS
1. Write a program that computes the T-pyramid of an image.
2. Write a program that derives the quad tree representation of an image using the homogeneity criterion of equal intensity
3. Develop programs for the following geometric transforms:
   (a) Rotation
   (b) Change of scale
   (c) Skewing
   (d) Affine transform calculated from three pairs of corresponding points
   (e) Bilinear transform calculated from four pairs of corresponding points.
4. Develop a program to implement Object Detection and Recognition
5. Develop a program for motion analysis using moving edges, and apply it to your image sequences.
6. Develop a program for Facial Detection and Recognition
7. Write a program for event detection in video surveillance system

TOTAL: 60 PERIODS

COURSE OUTCOMES: At the end of this course, the students will be able to:
CO1: Understand the basics of image processing techniques for computer vision and video analysis.
CO2: Explain the techniques used for image pre-processing.
CO3: Develop various object detection techniques.
CO4: Understand the various face recognition mechanisms.
CO5: Elaborate on deep learning-based video analytics.

TEXT BOOKS:
1. Milan Sonka, Vaclav Hlavac, Roger Boyle, “Image Processing, Analysis, and Machine Vision”, 4nd edition, Thomson Learning, 2013.
2. Vaibhav Verdhan,(2021, Computer Vision Using Deep Learning Neural Network Architectures with Python and Keras,Apress 2021(UNIT-III,IV and V)

REFERENCES:
1. Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer Verlag London
2. Limited,2011.
3. Caifeng Shan, FatihPorikli, Tao Xiang, Shaogang Gong, “Video Analytics for Business Intelligence”, Springer, 2012.
4. D. A. Forsyth, J. Ponce, “Computer Vision: A Modern Approach”, Pearson Education, 2003.
5. E. R. Davies, (2012), “Computer & Machine Vision”, Fourth Edition, Academic Press.

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