Computer Vision Systems: 6th International Conference, ICVS by Muhammad Zaheer Aziz, Bärbel Mertsching (auth.), Antonios

By Muhammad Zaheer Aziz, Bärbel Mertsching (auth.), Antonios Gasteratos, Markus Vincze, John K. Tsotsos (eds.)

This booklet constitutes the refereed court cases of the sixth overseas convention on computing device imaginative and prescient platforms, ICVS 2008, held in Santorini, Greece, could 12-15, 2008.

The 23 revised papers awarded including 30 poster shows and a pair of invited papers have been rigorously reviewed and chosen from 128 submissions. The papers are equipped in topical sections on cognitive imaginative and prescient, computer screen and surveillance, machine imaginative and prescient architectures, calibration and registration item popularity and monitoring, studying, human computer interplay in addition to go modal systems.

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Extra info for Computer Vision Systems: 6th International Conference, ICVS 2008 Santorini, Greece, May 12-15, 2008 Proceedings

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2 Related Work Previous work in the field of human motion capture and recognition is extensive, Moeslund et al. [11] give a detailed introduction and overview. Bobick and Davis [3] provide another good introduction. They classify human motion using a temporal template representation from a set of consecutive background subtracted images. A drawback of this approach is the dependency on the viewpoint. Li et al. [10] use Hidden Markov Models (HMMs) to classify hand trajectories of manipulative actions and take the object context into account.

On the other hand, we can show in our practical experiment how an extended set of attributes improves the results of object interpretation. Three examples of such validations are shown in Fig. 4. In Fig. e. on hypotheses that the attentional system rates similar to the car model in terms of color and gradients. As it can be seen, the top-ranked hypotheses are on the car in the image (left image). By combining this result with the segmentation, hypotheses can be grouped and also neglected (right image).

The resolution of the sequence is 320 × 240 and it was obtained with a standard, low-end web camera at 30 frames per second. Figure 4 depicts various intermediate results obtained at different stages of the proposed approach. A frame of the test sequence is shown in Fig. 4(a). e P (M ). In order to achieve real-time performance, the background subtraction algorithm operates at down-sampled images of dimensions 160 × 120. e. the results of the first processing layer of the proposed approach. The contours of the blobs that correspond to hand and face hypotheses are shown in Figures 4(e) and 4(f), respectively.

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