By David Van Hamme, Peter Veelaert, Wilfried Philips (auth.), Jacques Blanc-Talon, Richard Kleihorst, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This ebook constitutes the refereed complaints of the thirteenth overseas convention on complex suggestions for clever imaginative and prescient structures, ACIVS 2011, held in Ghent, Belgium, in August 2011.
The sixty six revised complete papers provided have been conscientiously reviewed and chosen from 124 submissions. The papers are geared up in topical sections on category acceptance, and monitoring, segmentation, photos research, photograph processing, video surveillance and biometrics, algorithms and optimization; and 3D, intensity and scene understanding.
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Extra resources for Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings
Hence a complete system has been discussed that is able to learn 3D objects and scenes and, at a later visit to the learned scenes, detect salient locations based upon the changes occurred after the previous visit. J. Blanc-Talon et al. ): ACIVS 2011, LNCS 6915, pp. 34–45, 2011. c Springer-Verlag Berlin Heidelberg 2011 Knowledge-Driven Saliency: Attention to the Unseen 2 35 Related Literature The system under discussion has roots in various disciplines. In order to be concise, selected literature is reviewed here that has conceptually close relation to the major contributions of the presented work.
In: CVPR, Los Alamitos, CA, USA, pp. 1889–1895 (2009) 19. : Scene classification with low-dimensional semantic spaces and weak supervision. In: CVPR, pp. 1–6 (2008) 20. : Video google: A text retrieval approach to object matching in videos. In: ICCV, vol. 2, pp. 1470–1477 (2003) 21. : Linear spatial pyramid matching using sparse coding for image classification. In: CVPR (2009) Knowledge-Driven Saliency: Attention to the Unseen M. de Abstract. This paper deals with attention in 3D environments based upon knowledge-driven cues.
Liu and V. Caselles Fig. 4. Confusion matrix for supervised vocabulary Table 2. 4 in order to give more details on the categorization of each category. The ﬁrst column contains the true labels and the last row lists the referred labels. 4 Conclusions This paper proposed supervised visual vocabulary construction approach which jointly considers image features and their class labels. A novel cost function is adopted to measure the dissimilarity of category information. The proposed method runs like the k-means algorithm to minimize the clustering cost function.
Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings by David Van Hamme, Peter Veelaert, Wilfried Philips (auth.), Jacques Blanc-Talon, Richard Kleihorst, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)