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LEDA: A Platform for Combinatorial and Geometric Computing
, 1999
"... We give an overview of the LEDA platform for combinatorial and geometric computing and an account of its development. We discuss our motivation for building LEDA and to what extent we have reached our goals. We also discuss some recent theoretical developments. This paper contains no new technical ..."
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Cited by 643 (46 self)
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We give an overview of the LEDA platform for combinatorial and geometric computing and an account of its development. We discuss our motivation for building LEDA and to what extent we have reached our goals. We also discuss some recent theoretical developments. This paper contains no new technical material. It is intended as a guide to existing publications about the system. We refer the reader also to our webpages for more information.
Hybrid Image Segmentation Using Watersheds and Fast Region Merging
 IEEE transactions on Image Processing
, 1998
"... Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate est ..."
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Cited by 88 (1 self)
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Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude. This initial segmentation is the input to a computationally efficient hierarchical (bottomup) region merging process that produces the final segmentation. The latter process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all RAG edges in a priority queue. We propose a significantly faster algorithm, which additionally maintains the socalled nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, onepixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with twodimensional/threedimensional (2D/3D) magnetic resonance images are presented. Index Terms — Image segmentation, nearest neighbor region merging, noise reduction, watershed transform. I.
Transforming Set Data Types to Power Optimal Data Structures
 IEEE Transactions on Computeraided Design
, 1996
"... In this paper we present a novel approach to model the search space for the custom implementation of set data types, a data type that is commonly found in important application domains such as network component realisations and database applications. The main objective is to arrive at power efficien ..."
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Cited by 27 (7 self)
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In this paper we present a novel approach to model the search space for the custom implementation of set data types, a data type that is commonly found in important application domains such as network component realisations and database applications. The main objective is to arrive at power efficient realisations of these data types in custom data structures, but the model can also be used with nonpower cost functions. Based on the model, we propose an efficient optimisation method for finding the implementation with minimum power consumption without performing an exhaustive scan of the search space. The range of power costs for different solutions can easily span 4 orders of magnitude, so a near optimal solution is crucial. This work also strongly contributes to our overall goal of a higher level of specification and shorter design cycles for tablebased memory organisations for applications where these data types are frequently used. The proposed model and methodology are suited for...
Practical LengthLimited Coding for Large Alphabets
 The Computer Journal
, 1995
"... The use of Huffman coding for economical representation of a stream of symbols drawn from a defined source alphabet is widely known. In this paper we consider the problems encountered when Huffman coding is applied to an alphabet containing millions of symbols. Conventional treebased methods for ge ..."
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Cited by 18 (0 self)
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The use of Huffman coding for economical representation of a stream of symbols drawn from a defined source alphabet is widely known. In this paper we consider the problems encountered when Huffman coding is applied to an alphabet containing millions of symbols. Conventional treebased methods for generating the set of codewords require large amounts of main memory; and worse, the codewords generated may be longer than 32 bits, which can severely limit the usefulness of both software and hardware implementations. The solution to the second problem is to generate "lengthlimited" codes, but previous algorithms for this restricted problem have required even more memory space than Huffman's unrestricted method. Here we reexamine the "packagemerge" algorithm for generating optimal lengthlimited prefixfree codes and show that with a considered reorganisation of the key steps and careful attention to detail it is possible to implement it to run quickly in modest amounts of memory. As evid...
Hybrid Image Segmentation Using Watersheds
, 1996
"... A hybrid image segmentation algorithm is proposed which combines edge and regionbased techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: a) Edgepreserving statistical noise reduction, b) Gradient Approximation, c) Detection of watersheds o ..."
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Cited by 2 (1 self)
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A hybrid image segmentation algorithm is proposed which combines edge and regionbased techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: a) Edgepreserving statistical noise reduction, b) Gradient Approximation, c) Detection of watersheds on gradient magnitude image, and d) Hierarchical Region Merging (HRM) in order to get semantically meaningful segmentations. The HRM process uses the Region Adjacency Graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all the RAG edges in a priority queue (heap). We propose a signi cantly faster algorithm which maintains an additional graph, the Most Similar Neighbor Graph, through which the priority queue size and processing time are drastically reduced. The nal segmentation is an image partition which, through the RAG, provides information that can be used by knowledgebased high level processes, i.e. recognition. In addition, this region based representation provides onepixel wide, closed, and accurately localized contours/surfaces. Due to the small number of free parameters, the algorithm can be quite eectively used in interactive image processing. Experimental results obtained with 2D MR images are presented.
Analysis of an Efficient Algorithm for the HardShere Problem
, 1996
"... ing with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works, requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept, ACM Inc., 1515 Broadway, New York, N ..."
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ing with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works, requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept, ACM Inc., 1515 Broadway, New York, NY 10036 USA, fax +1 (212) 8690481, or permissions@acm.org Analysis of an Efficient Algorithm for the HardSphere Problem Alan T. Krantz Department of Computer Science, University of Colorado, Boulder, Colorado Many similar algorithms for performing simulations of hardsphere systems have been presented. Among these algorithms are the algorithms designed by Rapaport (RAP), Lubachevsky (LUB), Krantz (HAD), and Marin (HYBRID). These algorithms exhibit a similar design in that they each use an O(logn) event queue which becomes the overwhelming bottleneck when simulating large systems. In this paper the design of HAD is presented and contrasted to RAP, LUB and HYBRID. Next, both an empirical...