Titlebook: Computer Vision -- ACCV 2012; 11th Asian Conferenc Kyoung Mu Lee,Yasuyuki Matsushita,Zhanyi Hu Conference proceedings 2013 Springer-Verlag

N斯巴達(dá)人

Arbitrary-Shape Object Localization Using Adaptive Image Gridspartition method which takes image content into account and can be efficiently implemented by dynamic programming. The use of adaptive partition further improves the localization accuracy of our approach. Experiments on PASCAL VOC 2007 and VOC 2008 datasets demonstrate the effectiveness of our appro

罵人有污點(diǎn)

Salient Object Detection via Color Contrast and Color Distributionn visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutters. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improve

冰雹

Data Decomposition and Spatial Mixture Modeling for Part Based Modelproposed data decomposition framework. We evaluate our system on the challenging PASCAL VOC2007 and PASCAL VOC2010 datasets, demonstrating the state-of-the-art performance compared with other related methods in terms of accuracy and efficiency.

objection

Max-Margin Regularization for Reducing Accidentalness in Chamfer Matchinges the advantages of accurately detecting objects or parts via chamfer matching and the robustness of a max-margin learning. Our results on standard benchmark datasets show that our method significantly outperforms current directional chamfer matching, thus redefining the state-of-the-art in this fi

惡臭

Coupling-and-Decoupling: A Hierarchical Model for Occlusion-Free Car Detectionearance templates for the X pairs, single X’s and latent parts of the single X’s, respectively. The part appearance templates can also be shared among different single X’s. In detection, a dynamic programming (DP) algorithm is used and as a natural consequence we decouple the two single X’s from the

指令

Conference proceedings 2013CCV 2012, held in Daejeon, Korea, in November 2012. The total of 226 contributions presented in these volumes was carefully reviewed and selected from 869submissions. The papers are organized in topical sections on object detection, learning and matching; object recognition; feature, representation,

褲子

儀式

Takashi Inoguchi,Lien Thi Quynh Le our method on common object discovery and model learning, which needs no fine/coarse alignment in the input data; in addition, it achieves comparable results with standard two-class MIL learning algorithms but our method is learning from one-class data only.

initiate

Local Context Priors for Object Proposal Generationg the Caltech pedestrian and PASCAL VOC dataset show that our method achieves the detection performance of an exhaustive search approach with much less computational load. Since we model the prior distribution over the proposals locally, it generalizes well and can be successfully applied across datasets.

Insubordinate

One-Class Multiple Instance Learning via Robust PCA for Common Object Discovery our method on common object discovery and model learning, which needs no fine/coarse alignment in the input data; in addition, it achieves comparable results with standard two-class MIL learning algorithms but our method is learning from one-class data only.