30 research outputs found
Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database
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 -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
Determining patch saliency using low-level context
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
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
10.1109/TPAMI.2011.276IEEE Transactions on Pattern Analysis and Machine Intelligence34101978-1991ITPI
From appearance to context-based recognition: Dense labeling in small images
10.1109/CVPR.2008.458759526th IEEE Conference on Computer Vision and Pattern Recognition, CVPR458759
Multiview Depth-Image Compression Using an Extended H.264 Encoder
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.