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Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...
Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s, at Bell Laboratories, the Jet Propulsion Laboratory, Massachusetts Institute of Technology, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone ...
MoCap pre-processing 660 action samples 8 PhaseSpace Motion Capture, 2 Stereo Cameras, 4 Quad Cameras, 6 accelerometers, 4 microphones Action classification 2013 [125] Ofli, F. et al. THUMOS Dataset Large video dataset for action classification. Actions classified and labeled. 45M frames of video Video, images, text Classification, action detection
Nikon EXPEED, a system on a chip including an image processor, video processor, digital signal processor (DSP) and a 32-bit microcontroller controlling the chip. An image processor, also known as an image processing engine, image processing unit (IPU), or image signal processor (ISP), is a type of media processor or specialized digital signal processor (DSP) used for image processing, in ...
The median filter is a non-linear digital filtering technique, often used to remove noise from an image, [1] signal, [2] and video. [3] Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image).
The original spectral data can be enriched by adding the contextual information carried by the neighbour pixels, or even replaced in some occasions. This kind of pre-processing methods are widely used in textured image recognition. The typical approaches include mean values, variances, texture description, etc.
The complexity of the pre-processing needed before differencing varies with the type of image, but is essential to ensure good subtraction of static features. This is commonly used in fields such as time-domain astronomy (known primarily as difference imaging) to find objects that fluctuate in brightness or move.
In more general fields of data processing, such as digital signal processing, it is referred to as dynamic range expansion. [1] The purpose of dynamic range expansion in the various applications is usually to bring the image, or other type of signal, into a range that is more familiar or normal to the senses, hence the term normalization.