Visual perceptionVisual perception is the ability to interpret the surrounding environment through photopic vision (daytime vision), color vision, scotopic vision (night vision), and mesopic vision (twilight vision), using light in the visible spectrum reflected by objects in the environment. This is different from visual acuity, which refers to how clearly a person sees (for example "20/20 vision"). A person can have problems with visual perceptual processing even if they have 20/20 vision.
Visual processingVisual processing is a term that is used to refer to the brain's ability to use and interpret visual information from the world around us. The process of converting light energy into a meaningful image is a complex process that is facilitated by numerous brain structures and higher level cognitive processes. On an anatomical level, light energy first enters the eye through the cornea, where the light is bent. After passing through the cornea, light passes through the pupil and then lens of the eye, where it is bent to a greater degree and focused upon the retina.
Visual cortexThe visual cortex of the brain is the area of the cerebral cortex that processes visual information. It is located in the occipital lobe. Sensory input originating from the eyes travels through the lateral geniculate nucleus in the thalamus and then reaches the visual cortex. The area of the visual cortex that receives the sensory input from the lateral geniculate nucleus is the primary visual cortex, also known as visual area 1 (V1), Brodmann area 17, or the striate cortex.
Visual systemThe visual system comprises the sensory organ (the eye) and parts of the central nervous system (the retina containing photoreceptor cells, the optic nerve, the optic tract and the visual cortex) which gives organisms the sense of sight (the ability to detect and process visible light) as well as enabling the formation of several non-image photo response functions. It detects and interprets information from the optical spectrum perceptible to that species to "build a representation" of the surrounding environment.
Visual agnosiaVisual agnosia is an impairment in recognition of visually presented objects. It is not due to a deficit in vision (acuity, visual field, and scanning), language, memory, or intellect. While cortical blindness results from lesions to primary visual cortex, visual agnosia is often due to damage to more anterior cortex such as the posterior occipital and/or temporal lobe(s) in the brain.[2] There are two types of visual agnosia: apperceptive agnosia and associative agnosia. Recognition of visual objects occurs at two primary levels.
Object recognition (cognitive science)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. Neuropsychological evidence affirms that there are four specific stages identified in the process of object recognition.
Visual impairmentVisual impairment, also known as vision impairment, is a medical definition primarily measured based on an individual's better eye visual acuity; in the absence of treatment such as corrective eyewear, assistive devices, and medical treatment– visual impairment may cause the individual difficulties with normal daily tasks including reading and walking. Low vision is a functional definition of visual impairment that is chronic, uncorrectable with treatment or conventional corrective lenses, and impacts daily living.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
Activity recognitionActivity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology.
Feature engineeringFeature engineering or feature extraction or feature discovery is the process of extracting features (characteristics, properties, attributes) from raw data. Due to deep learning networks, such as convolutional neural networks, that are able to learn it by itself, domain-specific- based feature engineering has become obsolete for vision and speech processing.
