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Automatic extraction of geometric lip features with application to multi-modal speaker identification

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Knowledge extraction

Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, s) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema.

Information extraction

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction Due to the difficulty of the problem, current approaches to IE (as of 2010) focus on narrowly restricted domains.

Automatic summarization

Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data. Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document.

Terminology extraction

Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers, web services, recommender systems, etc.

Visual cortex

The 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 system

The 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.

Mouth

In animal anatomy, the mouth, also known as the oral cavity, or in Latin cavum oris, is the opening through which many animals take in food and issue vocal sounds. It is also the cavity lying at the upper end of the alimentary canal, bounded on the outside by the lips and inside by the pharynx. In tetrapods, it contains the tongue and, except for some like birds, teeth. This cavity is also known as the buccal cavity, from the Latin bucca ("cheek").

Visual perception

Visual 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.

Adobe RGB color space

The Adobe RGB (1998) color space or opRGB is a color space developed by Adobe Inc. in 1998. It was designed to encompass most of the colors achievable on CMYK color printers, but by using RGB primary colors on a device such as a computer display. The Adobe RGB (1998) color space encompasses roughly 50% of the visible colors specified by the CIELAB color space – improving upon the gamut of the sRGB color space, primarily in cyan-green hues. It was subsequently standardized by the IEC as IEC 61966-2-5:1999 with a name opRGB (optional RGB color space) and is used in HDMI.

Color management

In digital imaging systems, color management (or colour management) is the controlled conversion between the color representations of various devices, such as s, digital cameras, monitors, TV screens, film printers, computer printers, offset presses, and corresponding media. The primary goal of color management is to obtain a good match across color devices; for example, the colors of one frame of a video should appear the same on a computer LCD monitor, on a plasma TV screen, and as a printed poster.