30 research outputs found

    Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

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    In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and KK-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

    A cooperative algorithm for stereo matching and occlusion detection

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    Determining patch saliency using low-level context

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    10.1007/978-3-540-88688-4-33Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5303 LNCSPART 2446-45

    Unsupervised learning of hierarchical spatial structures in images

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    10.1109/CVPRW.2009.52065492009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 20092009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition2743-275

    Exploring tiny images: The roles of appearance and contextual information for machine and human object recognition

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    10.1109/TPAMI.2011.276IEEE Transactions on Pattern Analysis and Machine Intelligence34101978-1991ITPI

    From appearance to context-based recognition: Dense labeling in small images

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    10.1109/CVPR.2008.458759526th IEEE Conference on Computer Vision and Pattern Recognition, CVPR458759

    Unsupervised learning of hierarchical spatial structures in images

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    Image deblurring and denoising using color priors

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    Wavelet-Based Correlation for Stereopsis

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    Multiview Depth-Image Compression Using an Extended H.264 Encoder

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    Abstract. This paper presents a predictive-coding algorithm for the compression of multiple depth-sequences obtained from a multi-camera acquisition setup. The proposed depth-prediction algorithm works by synthesizing a virtual depth-image that matches the depth-image (of the predicted camera). To generate this virtual depth-image, we use an image-rendering algorithm known as 3D image-warping. This newly proposed prediction technique is employed in a 3D coding system in order to compress multiview depth-sequences. For this purpose, we introduce an extended H.264 encoder that employs two prediction techniques: a blockbased motion prediction and the previously mentioned 3D image-warping prediction. This extended H.264 encoder adaptively selects the most efficient prediction scheme for each image-block using a rate-distortion criterion. We present experimental results for several multiview depthsequences, which show a quality improvement of about 2.5 dB as compared to H.264 inter-coded depth-images.
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