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A technique used to identify the boundaries of objects within images. It works by detecting discontinuities in brightness and is often used in preprocessing steps for image analysis.

The process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. It's used for understanding what's in a given image at a pixel level.

An interactive experience where information and virtual objects are overlaid on the real world. In computer vision, it involves tracking and augmenting the user's environment in real-time.

Involves reducing the amount of resources required to describe a large set of data accurately. When performing analysis of complex data, one of the major problems stems from the number of variables involved.

A computer vision technique that detects human figures in images and videos and estimates the pose of the subject. It relies on identifying the positions of certain key points on the individual's body.

The process of partitioning a digital image into multiple segments to simplify or change the representation of an image into something easier to analyze.

A feature descriptor used for object detection in computer vision and image processing. The technique counts occurrences of gradient orientation in localized portions of an image.

A class of deep neural networks, most commonly applied to analyzing visual imagery. They use a mathematical operation called convolution and have layers that act as filters for features in images.

The visual ability to perceive the world in three dimensions (3D) and the distance of an object. In computer vision, it is achieved using techniques like stereo vision, structured light, and time-of-flight sensors.

A method of identifying or verifying the identity of an individual using their face. It captures, analyzes, and compares patterns based on the person's facial details.

A technology that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.

The reuse of a pre-trained model on a new problem. It is an effective technique when the new problem has insufficient data to train a model from scratch.

The electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text. It is widely used as a form of data entry from printed data records.

A technique that uses convolutional neural networks to apply the style of one image to another. It can transfer artistic styles onto photographs or other images.