Computer Vision and Image Processing
Description
Advanced techniques for visual perception, image analysis, and computer vision applications using traditional and deep learning approaches.
Learning Objectives
Learners will master image processing fundamentals and filtering techniques, implement object detection and recognition systems, develop image segmentation and analysis algorithms, apply computer vision to real-world applications like autonomous vehicles and medical imaging, and integrate computer vision systems with AI applications.
Topics (8)
Computer vision applications in autonomous vehicles including lane detection, traffic sign recognition, pedestrian and vehicle detection, depth estimation, and sensor fusion for autonomous navigation systems.
Foundation concepts in image processing including image representation, filtering operations, edge detection, morphological operations, histogram analysis, and geometric transformations for image enhancement and preprocessing.
Feature detection and description techniques including corner detection, blob detection, SIFT, SURF, ORB, HOG features, and feature matching algorithms for object recognition and image retrieval.
Object detection and recognition techniques including template matching, Haar cascades, HOG+SVM, and modern deep learning approaches like YOLO, R-CNN variants, and SSD for real-time object detection and classification.
Image segmentation methods including thresholding, region growing, watershed algorithm, active contours, semantic segmentation with deep learning, and instance segmentation for detailed image analysis.
Facial recognition technologies including face detection, facial landmark detection, face recognition algorithms, biometric systems, and privacy considerations in facial recognition applications.
Medical image processing techniques including DICOM image handling, medical image enhancement, segmentation of anatomical structures, computer-aided diagnosis, and AI applications in radiology and pathology.
3D computer vision and AR/VR applications including camera calibration, stereo vision, 3D reconstruction, SLAM (Simultaneous Localization and Mapping), and augmented reality development frameworks.