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Qué (quién) es visual recognition - definición

COMPUTERIZED INFORMATION EXTRACTION FROM IMAGES

Computer Vision; Image recognition; Computer vision systems; Image Recognition Techniques; Computational vision; Image understanding; Image Understanding; Image Recognition; Image classification; Computational Vision; Texture recognition; History of computer vision; Visual recognition software; Applications of computer vision; Computer vision intelligence; Computer visual intelligence; Image classifier; Shape recognition; Visual recognition; Image identification; Classification of images; Image-based artificial intelligence; Military applications of computer vision

Visual object recognition (animal test)

PSYCHOLOGICAL ABILITY TO PERCEIVE AN OBJECT'S PHYSICAL PROPERTIES AND APPLY SEMANTIC ATTRIBUTES TO IT

User:Psyc4600/Group6; Object Recognition in Cognitive Neuroscience; Visual Object Recognition in Cognitive Neuroscience; Cognitive Neuroscience of Visual Object Recognition; Object constancy; Visual object recognition; Cognitive neuroscience of visual object recognition; Visual object recognition (animal test); Object recognition memory

Visual object recognition refers to the ability to identify the objects in view based on visual input. One important signature of visual object recognition is "object invariance", or the ability to identify objects across changes in the detailed context in which objects are viewed, including changes in illumination, object pose, and background context.

https://en.wikipedia.org/wiki/Visual_object_recognition_(animal_test)

Audio-visual speech recognition

Audio visual speech recognition; AVSR; Avsr; Audiovisual speech recognition; Visual speech recognition

Audio visual speech recognition (AVSR) is a technique that uses image processing capabilities in lip reading to aid speech recognition systems in recognizing undeterministic phones or giving preponderance among near probability decisions.

https://en.wikipedia.org/wiki/Audio-visual_speech_recognition

pattern recognition

BRANCH OF MACHINE LEARNING

Pattern Recognition; Pattern detection; Pattern recognition, visual; Machine pattern recognition; Pattern analysis; Pattern-recognition; Pattern Recognition and Learning; Pattern recognition and learning; Pattern recognition (machine learning); Algorithms for pattern recognition; List of algorithms for pattern recognition; Automated pattern recognition; Automatic pattern recognition; Statistical pattern recognition; Applications of pattern recognition; Fuzzy pattern recognition; List of pattern recognition algorithms

<artificial intelligence, data processing> A branch of artificial intelligence concerned with the classification or description of observations. Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. The classification or description scheme is usually based on the availability of a set of patterns that have already been classified or described. This set of patterns is termed the training set and the resulting learning strategy is characterised as supervised. Learning can also be unsupervised, in the sense that the system is not given an a priori labelling of patterns, instead it establishes the classes itself based on the statistical regularities of the patterns. The classification or description scheme usually uses one of the following approaches: statistical (or {decision theoretic}), syntactic (or structural), or neural. Statistical pattern recognition is based on statistical characterisations of patterns, assuming that the patterns are generated by a probabilistic system. Structural pattern recognition is based on the structural interrelationships of features. Neural pattern recognition employs the neural computing paradigm that has emerged with neural networks. (1995-09-22)

Wikipedia

Computer vision

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, or medical scanning devices. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems.

Sub-domains of computer vision include scene reconstruction, object detection, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration.

Adopting computer vision technology might be painstaking for organizations as there is no single point solution for it. There are very few companies that provide a unified and distributed platform or an Operating System where computer vision applications can be easily deployed and managed.

https://en.wikipedia.org/wiki/Computer vision

Ejemplos de uso de visual recognition

1. Soldiers and aircrew still have to rely largely on visual recognition of enemy and friendly forces, the audit office says.

2. All 115 passengers and six crew died, most burnt beyond visual recognition, when the plane, with neither pilot in control, spiralled down in a death dive into a mountainous area about 40 km north of Athens.