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Feature detection is a process by which the nervous system sorts or filters complex natural stimuli in order to extract behaviorally relevant cues that have a high probability of being associated with important objects or organisms in their environment, as opposed to irrelevant background or noise.
Those feature detectors will then stimulate or inhibit different letter detectors, which will finally stimulate or inhibit different word detectors. Some words can be activated through these stimulations. However, the fact that there is no meaning to the combination of letters can inhibit these words which were previously activated. [13]
According to Treisman, the first stage of the feature integration theory is the preattentive stage. During this stage, different parts of the brain automatically gather information about basic features (colors, shape, movement) that are found in the visual field. The idea that features are automatically separated appears counterintuitive.
Feature detection (nervous system), a biological process for interpreting sensory input Orientation column, also known as a "feature detection column" Feature detection (computer vision), methods for finding parts of an image relevant to a computational task; Feature detection (web development), determining whether a computing environment has ...
As a result of the biological plausibility criticism of the template matching model, feature detection models began to rise. In a feature detection model, the image is first perceived in its basic individual elements before it is recognized as a whole object. For example, when we are presented with the letter A, we would first see a short ...
Bottom-up processing, which is the processing of information that depends on input from the environment, [8] explains how one utilizes feature detectors to process characteristics of the stimuli and differentiate a target from its distractors. [7] This draw of visual attention towards the target due to bottom-up processes is known as "saliency."
An example is provided by Hinton and Salakhutdinov [24] where the encoder uses raw data (e.g., image) as input and produces feature or representation as output and the decoder uses the extracted feature from the encoder as input and reconstructs the original input raw data as output. The encoder and decoder are constructed by stacking multiple ...
For example, it was found that lesions to the perirhinal cortex in rats causes impairments in object recognition especially with an increase in feature ambiguity. [37] Neonatal aspiration lesions of the amygdaloid complex in monkeys appear to have resulted in a greater object memory loss than early hippocampal lesions.