Artificial Intelligence and Signal Processing: International by Ali Movaghar, Mansour Jamzad, Hossein Asadi

By Ali Movaghar, Mansour Jamzad, Hossein Asadi

This e-book constitutes the refereed lawsuits of the overseas Symposium, on synthetic Intelligence and sign Processing, AISP 2013, held in Tehran, Iran, in December 2013. The 35 complete papers offered have been rigorously reviewed and chosen from 106 submissions. they're prepared in topical sections akin to picture processing, laptop imaginative and prescient, clinical picture processing, sign processing, speech processing, normal language processing, platforms and AI functions, robotics.

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Additional resources for Artificial Intelligence and Signal Processing: International Symposium, AISP 2013, Tehran, Iran, December 25-26, 2013, Revised Selected Papers

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Emotion recognition system based on facial expression. S. ir Abstract. This paper proposes some novel approaches based on spatial homogeneous regions to improve hyperspectral remotely sensed landscape’s classification. Our proposed approaches are investigation of three segmentation techniques (watershed segmentation, hierarchical segmentation, partial clustering) in hyperspectral image and combination of spectral and spatial information in classification with majority vote rule. Proposed methods are compared with pixel-wise SVM and the ECHO, EMP and ML classification for University of Pavia and Indiana datasets.

Keywords: Multi-focus Á Image fusion Object recognition Á Wavelet Á Segmentation Á Feature extraction Á 1 Introduction In sensor networks, every camera can observe scene and to be recorded either still images or video sequences. Therefore, the processing of output information is related to image processing and machine vision subjects [1]. The need for image fusion is increasing mainly due to the increased number and variety of image acquisition techniques [2]. A prominent feature of visual sensors or cameras is the great amount of generated data, thus requires more local processing resources to deliver only the useful information represented in a conceptualized level [1].

Therefore, this approach may be not well adapted for hyperspectral data. Segmentation of a hyperspectral image is a challenging task. The research groups of Linden and Huang used results from a multiscale segmentation to define a spectral-spatial feature for every pixel [10, 11]. These are efficient, but computationally demanding approaches. The main objective of this paper was to further develop methods for classification of hyperspectral data using both spectral and spatial information. We have proposed and developed some general strategies for hyperspectral data classification.

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