Read e-book online Neural Information Processing: 20th International PDF

By Toshikazu Samura, Yutaka Sakai, Hatsuo Hayashi (auth.), Minho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil (eds.)

ISBN-10: 3642420508

ISBN-13: 9783642420504

ISBN-10: 3642420516

ISBN-13: 9783642420511

The 3 quantity set LNCS 8226, LNCS 8227, and LNCS 8228 constitutes the court cases of the 20 th foreign convention on Neural info Processing, ICONIP 2013, held in Daegu, Korea, in November 2013. The a hundred and eighty complete and seventy five poster papers offered including four prolonged abstracts have been rigorously reviewed and chosen from various submissions. those papers disguise all significant themes of theoretical study, empirical learn and functions of neural details processing examine. the explicit issues lined are as follows: cognitive technology and synthetic intelligence; studying idea, algorithms and architectures; computational neuroscience and mind imaging; imaginative and prescient, speech and sign processing; regulate, robotics and applied sciences and novel methods and applications.

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Read or Download Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part III PDF

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Extra resources for Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part III

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Example of the shape of function Z(φi ) a definite width Δt, during which φi is updated according to the value of Z(φi ). As a specific case, if it is assumed that Z(φi ) = − sin(φi ), the dynamics of the pulse-coupled oscillators are schematically illustrated in Fig. 1. In order to simplify the function shape of Z(φi ) for hardware implementation, we have used three-value functions as Z(φi ); a typical function shape is shown in Fig. 2, which is expanded in the time domain and has values: −1, 0, 1 [7].

These characteristics are also known as laws or principles of Gestalt. Several brain studies have confirmed the application of Gestalt principles. For instance, Wannig et al. (2011), recorded neuronal activity in area V1 of macaque monkeys and observed an automatic spread of attention to image elements outside of the attentional focus when they were bound to an attended stimulus by Gestalt criteria [9]. This result shows neurological correlates of operating Gestalt principles. Fig. 2 shows some images that are grouped differently using various principles of Gestalt.

2. Examples of Gestalt principles: (a) Similarity, (b) Proximity, (c) Common fate (d) Connectedness, (e) Good continuation, (f) Common region 3 Proposed Model Roelfsema et al. [8] conjectured that Gestalt grouping is implemented by connecting neurons tuned to image features that are likely to belong to the same perceptual object. Koostra and Kragic used Markov random field and graph-cut techniques for 44 H. Jo, A. Ojha, and M. Lee segmenting the object from the background. They tried to quantify seven Gestalt principles to improve the accuracy of segmentation model.

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Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part III by Toshikazu Samura, Yutaka Sakai, Hatsuo Hayashi (auth.), Minho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil (eds.)


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