Intelligence Semantics

Get Advances in Signal Processing and Intelligent Recognition PDF

By Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay

ISBN-10: 3319049593

ISBN-13: 9783319049595

ISBN-10: 3319049607

ISBN-13: 9783319049601

This edited quantity encompasses a number of refereed and revised papers initially provided on the overseas Symposium on sign Processing and clever attractiveness platforms (SIRS-2014), March 13-15, 2014, Trivandrum, India. this system committee obtained 134 submissions from eleven international locations. every one paper used to be peer reviewed by means of not less than 3 or extra self sufficient referees of this system committee and the fifty two papers have been eventually chosen. The papers supply stimulating insights into trend attractiveness, computer studying and Knowledge-Based structures sign and Speech Processing photograph and Video Processing cellular Computing and functions and computing device imaginative and prescient. The ebook is directed to the researchers and scientists engaged in a variety of box of sign processing and comparable components.

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Figure 6(c) and Figure 6(d) shows the results when using min at local level and sum at global aggregator level. Here also, no difference in observed in performance with distance or similarity measures. Figure 6(e) and Figure 6(f) shows the results when using min measure at local level followed by mean at global aggregator level. In this case however, the distance measure clearly outperforms the similarity measure both in terms of recognition accuracy, in the size and range of k values. Figure 6(g) Aggregate Metric Networks for Nearest Neighbour Classifiers 19 and Figure 6(h) shows the results when using sum measure at local level and min at global aggregator level.

Digital Picture Processing. Academic Press, New York (1982) 2. The Reproduction of Colour. WileyIS and T Series in Imaging Science and Technology, Chichester (2004) 3. Digital Video and HDTV: Algorithms and Interfaces, p. 203. Morgan Kaufmann 4. : The irises of the gasp peninsula. Bulletin of the American Iris Society 59, 2–5 (1935) 5. : The species problem in iris. Annals of the Missouri Botanical Garden 23(3), 457–509 (1936) 6. : Similarity measures. Scholarpedia 2(12), 4116 (2007) 7. : Comprehensive survey on distance/similarity measures between probability density functions.

2013). This method uses simple least squares estimation for face representation and imposes l1 regularization to obtain sparse noise term. The dense face coefficients are used to reconstruct face using corresponding subspace models. Second, the sparse noise coefficients are effectively used to reconstruct occlusion map which is used to handle large contiguous block occlusion. The class of the test face is identified based on minimum residual error obtained using test face, reconstructed face and occlusion map.

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Advances in Signal Processing and Intelligent Recognition Systems by Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay


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