By C.V. Jawahar, Shiguang Shan
The three-volume set, along with LNCS 9008, 9009, and 9010, comprises conscientiously reviewed and chosen papers awarded at 15 workshops held together with the twelfth Asian convention on desktop imaginative and prescient, ACCV 2014, in Singapore, in November 2014. The 153 complete papers awarded have been chosen from various submissions. LNCS 9008 comprises the papers chosen for the Workshop on Human Gait and motion research within the Wild, the second one overseas Workshop on great information in 3D machine imaginative and prescient, the Workshop on Deep studying on visible information, the Workshop on Scene realizing for independent platforms and the Workshop on powerful neighborhood Descriptors for laptop imaginative and prescient. LNCS 9009 includes the papers chosen for the Workshop on rising themes on photograph recovery and Enhancement, the 1st overseas Workshop on powerful interpreting, the second one Workshop on User-Centred machine imaginative and prescient, the foreign Workshop on Video Segmentation in computing device imaginative and prescient, the Workshop: My automobile Has Eyes: clever motor vehicle with imaginative and prescient know-how, the 3rd Workshop on E-Heritage and the Workshop on desktop imaginative and prescient for Affective Computing. LNCS 9010 comprises the papers chosen for the Workshop on characteristic and Similarity for computing device imaginative and prescient, the 3rd overseas Workshop on clever cellular and selfish imaginative and prescient and the Workshop on Human identity for Surveillance.
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Extra resources for Computer Vision - ACCV 2014 Workshops: Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part I
We developed the method, explained the theory of Gauss maps on which it relies and demonstrated how it can exploit boundary curvature information. We veriﬁed that the new feature encodes enhanced shape information when compared with state-of-the-art approaches. Our superior results are validated through rigorous experiments using the world’s largest gait database. In addition, our method does not require any training which makes it also valid when a small number of sequences per subject is available.
28]’s method to extract dense trajectory aligned motion features and our hand displacement features from the detecting and tracking results. We conduct a Fisher vector for each type of extracted features. To combine diﬀerent features, we concatenate their Fisher vectors. We train a multiclass linear SVM to classify the videos. By focusing only on regions which are expected to be the most related to the actions in stead of considering the whole frame, we can improve action recognition precision.
0 %, rank-5 id. 7 %) is obtained using 32-bin histograms and a grid of 169 (13 × 13) boxes. 2 H. El-Alfy et al. Evaluation of Descriptor Variants The results shown earlier are for the period-based approach, as pointed out. We now evaluate the performance of the frame-based approach. Matching key frames, as required with this approach, necessitates the alignment of the phases of the gallery and probe sequences as much as possible. We ﬁrst used the cycle boundaries provided with the dataset in order to align the viewpoint.