Titlebook: Computer Vision -- ACCV 2014; 12th Asian Conferenc Daniel Cremers,Ian Reid,Ming-Hsuan Yang Conference proceedings 2015 Springer Internation

巧思

相互影響

泰然自若

Image Restoration via Multi-prior Collaboration,lar prior methods are applied to evaluate the effectiveness of the proposed multi-prior collaboration framework. Compared with the state-of-the-art image restoration approaches, the proposed framework improves the restoration performance significantly.

勤勉

Conference proceedings 2015ACCV 2014, held in Singapore, Singapore, in November 2014..The total of 227 contributions presented in these volumes was carefully reviewed and selected from 814 submissions. The papers are organized in topical sections on recognition; 3D vision; low-level vision and features; segmentation; face and

Abjure

Accurate Vessel Segmentation with Progressive Contrast Enhancement and Canny Refinement,ntal results on a public retinal dataset and our clinical cerebral data demonstrate that our approach outperforms state-of-the-art methods including the vesselness based method [.] and the optimally oriented flux (OOF) based method [.].

有發(fā)明天才

長矛

Local Generic Representation for Face Recognition with Single Sample per Person,ation dictionary, and it uses correntropy to measure the representation residual of each patch. Half-quadratic analysis is adopted to solve the optimization problem. LGR takes the advantages of patch based local representation and generic variation representation, showing leading performance in face

ENACT

Real-Time Head Orientation from a Monocular Camera Using Deep Neural Network,based post-processing to enhance stability of the estimation further in video sequences. We compare the performance with the state-of-the-art algorithm which utilizes depth sensor and we validate our head orientation estimator on Internet photos and video.

Cerumen

瘋狂

Visual Salience Learning via Low Rank Matrix Recovery,rix plus a sparse?matrix. We aim at learning a unified sparse matrix that represents the salient regions using these obtained individual saliency maps. The sparse matrix can be inferred by conducting low rank matrix recovery using the robust principal component analysis technique. Experiments show t