Computer Vision Fundamentals

Explore digital images and pixels, their types, formats, and resolution, and learn to filter and enhance grayscale and color images using 8 bits per channel.

Explore binary, grayscale, color, and multispectral image types used in computer vision, and learn how JPEG, PNG, and TIFF formats store and compress them.

Learn image filtering and enhancement to improve quality with kernels and pixel transformations. Use smoothing, sharpening, and edge detection, and apply contour stretching, histogram equalization, and gamma correction.

Explore image features, corner detection, and feature extraction to create descriptors for matching, registration, and stitching, noting scale, rotation, translation, affine, and illumination invariance, with Harris, Tomasi, and FAST detectors.

Explore image descriptors and feature matching, including hog, lbp, cnn descriptors, and flann or ransac, to describe regions, compare images, and verify faces.

Explore the basics of image classification and object detection and localization, including training and testing data, classifiers, and accuracy, with k-nearest neighbors, support vector machines, decision trees, and random forests.

Explore object detection and localization, identifying and locating objects in images with bounding boxes, labels, and scores. Learn region proposals, region classification, non maximum suppression, and intersection over union.

Explore image segmentation by dividing an image into meaningful regions using thresholding, region-based, edge-based, and semantic segmentation to locate objects and understand scenes.

Discover thresholding and region-based segmentation in image analysis, including global and adaptive thresholding, Otsu's method, region growing, and region splitting and merging with examples like document binarization.

Explore semantic segmentation and scene understanding through encoder-decoder CNNs with skip connections, and Deeplab and Mask r-cnn for atrous convolution and multi-scale boundary refinement.

Learn optical flow and motion estimation for analysis and tracking, using vector fields, block matching, differential methods like Lucas-kanade and Schunck, and feature-based approaches such as SIFT, SURF, and Xcetera.

Explore object tracking methods such as Kalman filter, mean shift, and particle filter to understand motion-based applications like activity recognition, gesture recognition, and video stabilization.

Explore depth perception and stereoscopic vision, including monocular and binocular cues, motion cues, and structure from motion for 3D reconstruction and related medical imaging, 3D movies, and augmented reality applications.

Explore structure from motion (SFM) and 3D reconstruction by using multiple photographs from different angles to create a 3D scene model, using feature matching, camera pose estimation, and bundle adjustment.

Explore face recognition and biometrics within computer vision, including locating and aligning faces, verifying identities, and applications in augmented reality, virtual reality, and security.

Explore how surveillance systems use computer vision to monitor activities, detect motion, recognize actions, locate objects and people, and analyze traffic patterns for safety and logistics.

Discover how augmented reality and virtual reality fuse real and virtual worlds using AR glasses, VR headsets, smartphone overlays, markers, and controllers to enhance education, entertainment, tourism, and healthcare.

Examine privacy and data protection, bias and fairness in computer vision algorithms, and the ethical use of technology, including consent, anonymity, and data ownership across security, healthcare, and social media.

Assess data bias, algorithm bias, impact bias, and accountability bias in computer vision to improve accuracy and fairness across law enforcement, education, and retail applications.

Assess the purpose, intention, and morality behind computer vision applications. Explore ethical risks like deception, exploitation, and discrimination, and consider regulation across entertainment, healthcare, and security domains.

Explore future trends in computer vision and how deep learning enhances vision tasks, including self-supervised learning, generative adversarial networks, explainable AI, and emerging applications in robotics and autonomous systems.

Explore how computer vision enables robotics and autonomous systems, from autonomous vehicles and drones to humanoid robots that perceive roads, traffic, pedestrians, enabling tasks without human intervention.

Explore emerging computer vision applications across social good, health, environment, and art, from disease detection in crops and wildlife monitoring to disaster response and personalized experiences.