By Jing Tian, Li Chen
Due to expanding strength in real-world functions resembling visible communications, machine assisted biomedical imaging, and video surveillance, photo and video interpretations became a space of becoming interest.
Intelligent snapshot and Video Interpretation: Algorithms and functions covers all points of picture and video research from low-level early visions to high-level popularity. This e-book highlights how those innovations became appropriate and may turn out to be a precious software for researchers, execs, and graduate scholars operating or learning the fields of imaging and video processing.
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Additional info for Intelligent Image and Video Interpretation: Algorithms and Applications
Example text
In both cases of double quantization, periodicity of the artifacts is introduced in to histograms. This artifact would be used as evidence of double compression and hence tampering. In temporal analysis, addition or deletion of frames from a video sequence and re-encoding the resulting sequence, results in a large motion error between consecutive P-frames of the video, since they originated from different GOPs. Moreover, this increased motion error would be periodic, occurring throughout each of the group of pictures following the frame deletion or addition.
Here the algorithm uses Support Vector Machine (SVM) for the classification of the tampered and authentic videos. SVM (VN, 1995) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation (Singh, Vatsa, & Noore, 2006). In fact, SVM is a non linear classifier that performs classification tasks by constructing hyper planes in a multi dimensional space and separates the data points in different classes. This algorithm (Upadhyay, Singh, Vatsa, and Singh, 2007) is performed in two stages: (1) SVM training and (2) Tamper detection and classification, using SVM.
Application of audio-visual speech recognition is possible but not limited to the field of education, business and lifestyle. In terms of education, integration of audio and visual aspects allows continuous child behaviour monitor that can be utilized to design educational system meant for optimising infant attention span on pre-school education (Westwood, 2007). Similar system can be applied to child that require special attention such as the deaf/dumb and down syndrome or slow learner children.