Titlebook: Computer Vision - ACCV 2014 Workshops; Singapore, Singapore C.V. Jawahar,Shiguang Shan Conference proceedings 2015 Springer International P

浸軟

https://doi.org/10.1007/978-3-319-47482-3s. The effectiveness of the proposed learning technique is empirically evaluated with a dataset which contains 38 classes (2030 character samples) captured from actual products by standard digital cameras. The recognition accuracy has been improved from 78.37?% to 98.52?% by introducing the variatio

Archipelago

“Circular Talk”: S.I. Martin’s , each moment, the pre-recorded 3D motion parameters can instantly be used for natural interaction. The proposed bare-hand interaction technology performs in real-time with high accuracy using an ordinary camera.

collateral

清楚

0302-9743 h Vision Technology, the Third Workshop on E-Heritage, and the Workshop on Computer Vision for Affective Computing. LNCS 9010 contains the papers selected for the Workshop on Feature and Simila978-3-319-16630-8978-3-319-16631-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

環(huán)形

Feature-Preserving Image Restoration from Adaptive Triangular Meshesnear image edges (or feature boundaries). In the current paper, a new method of restoring an image from its triangulation representation is proposed, by utilizing anisotropic radial basis functions (ARBFs). This method considers not only the geometrical (Euclidean) distances but also the local featu

Affluence

小卷發(fā)

nonplus

Performance Improvement of Dot-Matrix Character Recognition by Variation Model Based Learnings. The effectiveness of the proposed learning technique is empirically evaluated with a dataset which contains 38 classes (2030 character samples) captured from actual products by standard digital cameras. The recognition accuracy has been improved from 78.37?% to 98.52?% by introducing the variatio

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猜忌

Debugging Object Tracking Results by a Recommender System with Correction Propagation tracking accuracy. Our proposed approach is evaluated on three challenging datasets. The quantitative evaluation and comparison validate that the recommender system with correction propagation is effective and efficient to help humans debug tracking results.