PHR: A parallel hierarchical radiosity system with dynamic load balancing

In this paper, we present a parallel system called PHR for computing hierarchical radiosity solutions of complex scenes. The system is targeted for multi-processor architectures with distributed memory. The system evaluates and subdivides the interactions level by level in a breadth first fashion, and the interactions are redistributed at the end of each level to keep load balanced. In order to allow interactions freely travel across processors, all the patch data is replicated on all the processors. Hence, the system favors load balancing at the expense of increased communication volume. However, the results show that the overhead of communication is negligible compared with total execution time. We obtained a speed-up of 25 for 32 processors in our test scenes. © 2005 Springer Science + Business Media, Inc

E.: Content-based retrieval of historical Ottoman documents stored as textual images

Abstract—There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. In this paper, a framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images, which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domain based on angular and distance span of shapes are used to extract the symbols. In order to make content-based retrieval in historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in textual images. The querying process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts. Index Terms—Angular and distance span, binary wavelet decomposition, content-based retrieval, historical document compression, partial symbol-wise matching. I

Content-Based Retrieval of Historical Ottoman Documents Stored As Textual Images

There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. In this paper, a framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images,which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domain based on angular and distance span of shapes are used to extract the symbols. In order to make content-based retrieval in historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in textual images. The querying process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts

Cervid Distribution Browse and Snow Cover in Alberta

Abstract. In this paper we propose an algorithm for image segmentation using graph cuts which can be used to efficiently solve labeling problems on high resolution images or image sequences. The basic idea of our method is to group large homogeneous regions to one single variable. Therefore we combine the appearance and the task specific similarity with Dempster’s theory of evidence to compute the basic belief that two pixels/groups will have the same label in the minimum energy state. Experiments on image and video segmentation show that our grouping leads to a significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner with a low approximation loss.

A Reduction Method For Graph Cut Optimization

International audienceIn a couple of years, graph cuts methods appeared as a leading method in computer vision and graphics due to their efficiency in computing globally optimal solutions. Such an approach remains however impractical for large-scale problems due to the memory requirements for storing the graphs. Among strategies to overcome this situation, an existing strategy consists in reducing the size of these graphs by only adding the nodes which satisfy a local condition. In the image segmentation context, this means for instance that when unary terms are locally strong, the remaining nodes are typically located in a thin band around the object of interest to segment. In this paper, we empirically prove on a large number of experiments that the distance between the global minimizer and the minimizer obtained with an heuristic test, remains very low. In addition to this preliminary work, we detail existing strategies to reduce the memory footprint of graph cuts and provide extra parameters for further reducing the graphs and removing isolated speckles and islands due to noise in the segmentation

Reformulating and optimizing the Mumford–Shah functional on a graph—A faster, lower energy solution

Abstract. Active contour formulations predominate current minimization of the Mumford-Shah functional (MSF) for image segmentation and filtering. Unfortunately, these formulations necessitate optimization of the contour by evolving via gradient descent, which is known for its sensitivity to initialization and the tendency to produce undesirable local minima. In order to reduce these problems, we reformulate the corresponding MSF on an arbitrary graph and apply combinatorial optimization to produce a fast, low-energy solution. The solution provided by this graph formulation is compared with the solution computed via traditional narrow-band level set methods. This comparison demonstrates that our graph formulation and optimization produces lower energy solutions than gradient descent based contour evolution methods in significantly less time. Finally, by avoiding evolution of the contour via gradient descent, we demonstrate that our optimization of the MSF is capable of evolving the contour with non-local movement.