Download e-book for kindle: Algorithmic Introduction to Coding Theory by Sudan M.

Download e-book for kindle: Algorithmic Introduction to Coding Theory by Sudan M.

By Sudan M.

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Addison-Wesley, Harlow, England, 1999. [5] K. Brown, A rich diet: Data-rich multimedia has a lot in store for archiving and storage companies, Broadband Week, March 5, 2001. [6] B. Prabhakaran, Multimedia Database Management Systems, Kluwer, Norwell, MA, 1997. [7] S. Santini, and R. Jain, Image databases are not databases with images”, Proc. 9th International Conference on Image Analysis and Processing (ICIAP '97), Florence, Italy, September 17-19, 1997. S. Subrahmanian, Principles of Multimedia Database Systems, Morgan Kaufmann, San Francisco, CA, 1998.

2] allow user-defined templates of semantics in image sequences. Brand et al. [27] use coupled HMMs to model complex actions in Tai Chi movies. Recent work in segmentation and classification of audio streams includes [7,8,9,10,11,12,31,32]. Naphade and Huang [7] used hidden Markov models (HMMs) for representing the probability density functions of auditory features computed over a time series. Zhang and Kuo [12] used features based on heuristics for audio classification. HMMs have been successfully applied in speech recognition.

Therefore, unlike textual data, generating content-based video data indexes automatically is much harder. The process of building indexes for video programs can be divided into three main steps: 1. Parsing: temporal segmentation of the video contents into smaller units. 2. Abstraction: extracting or building a representative subset of video data from the original video. 3. Content analysis: extracting visual features from representative video frames. Existing work on video indexing can be classified in three categories [11]: 1.

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Algorithmic Introduction to Coding Theory by Sudan M.

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