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Today, images and image sequences (videos) make up about 80% of all corporate and public unstructured big data. As growth of unstructured data increases, analytical systems must assimilate and interpret images and videos as well as they interpret structured data such as text and numbers. An image is a set of signals sensed by the human eye and processed by the visual cortex in the brain creating a vivid experience of a scene that is instantly associated with concepts and objects previously perceived and recorded in one's memory. To a computer, images are either a raster image or a vector image. Simply put, raster images are a sequence of pixels with discreet numerical values for color; vector images are a set of color-annotated polygons. To perform analytics on images or videos, the geometric encoding must be transformed into constructs depicting physical features, objects and movement represented by the image or video. This chapter explores text, images, and video analytics in fog computing.

A brain-computer interface, is a direct communication pathway between a human or animal brain and an external device. Work is being done to identify objects, images, videos and their colour compositions. When humans watch the surrounding environment, visual data is processed by the brain, and it is possible to reconstruct the same on the screen with some appreciable accuracy by analysing the physiological data. This data is acquired by using one of the non-invasive techniques like electroencephalography (EEG) in Brain Computer Interface (BCI). The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. The prototype discussed in this paper works on features learning and classification centric techniques using support vector machine. The suggested pipeline, ensures successful navigation of a robot in four directions in real time with accuracy of 93%.

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