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CNNs are a class of deep neural networks, most commonly applied to analyzing visual imagery. They use convolutional layers to automatically and adaptively learn spatial hierarchies of features from images.

Object Recognition is a process that identifies specific objects within an image or video. Techniques such as R-CNN, YOLO, and SSD use deep learning to achieve high accuracy in detection.

Histogram Equalization is a method for image processing that adjusts image intensities to enhance contrast. The technique spreads out the most frequent intensity values over the whole histogram.

Normalization is a preprocessing step to change the range of pixel intensity values. Common methods include scaling between 0-1, standardization, and mean normalization.

Template Matching is a technique in digital image processing for finding parts of an image which match a template image. Techniques include normalized cross-correlation and square difference matching.

A Depth Map is a 2D representation of the 3D distance between the scene and the viewer. This spatial data can be acquired through techniques like stereo vision or depth sensing cameras.

Depth Perception is the ability to perceive the world in three dimensions. Techniques such as stereo vision, structured light, and time-of-flight cameras are used to measure depth in computer vision.

Shape Analysis is the process of studying geometrical structures within digital images, and it involves techniques like contour detection, Hough Transform, and Morphological Operations.

Feature Matching is a method of finding similar features between different images. Techniques such as SIFT, SURF, and ORB are used to find and match these features, facilitating tasks like object and motion recognition.

Semantic Segmentation is the process of partitioning an image into semantically meaningful parts, and classifying each part into one of the pre-determined classes. Techniques include FCN (Fully Convolutional Networks) and U-Net.

Image Segmentation divides an image into regions or objects to simplify and/or change its representation. Techniques include Thresholding, Region Growing, and Watershed segmentation.

Optical Flow is the pattern of apparent motion of image objects between two consecutive frames caused by the movement of object or camera. Techniques like Lucas-Kanade and Horn-Schunck are used to analyze this motion.

Edge Detection is a technique used to identify the boundaries within images. It works by detecting discontinuities in brightness and is used for image segmentation and data extraction. Techniques: Sobel filter, Canny Edge Detector, Laplacian of Gaussian.

Biometric Recognition refers to the automated recognition of individuals based on their behavioral and biological characteristics. Techniques include facial recognition, fingerprint analysis, and iris scanning.