Van Wyk. Details if other :. Thanks for telling us about the problem. Return to Book Page. Van Wyk ,. Christopher J. Get A Copy. Hardcover , pages. More Details Original Title. Friend Reviews. To see what your friends thought of this book, please sign up. To ask other readers questions about Data Structures and C Programs , please sign up.
Be the first to ask a question about Data Structures and C Programs. Lists with This Book. This book is not yet featured on Listopia. Add this book to your favorite list ». Community Reviews. Showing Rating details. All Languages. More filters. 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 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 web-pages for more information.
Efstratiadis, Nicos Maglaveras, Aggelos K. Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds. An edge-preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate est Abstract - Cited by 1 self - Add to MetaCart Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds.
An edge-preserving 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 so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. Index Terms — Image segmentation, nearest neighbor region merging, noise reduction, watershed transform.
Citation Context 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 Abstract - Cited by 30 7 self - Add to MetaCart 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 non-power 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 table-based memory organisations for applications where these data types are frequently used. The proposed model and methodology are suited for 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 tree-based methods for ge Abstract - Cited by 21 0 self - Add to MetaCart The use of Huffman coding for economical representation of a stream of symbols drawn from a defined source alphabet is widely known.
Conventional tree-based 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. As evid Haris, S. Efstratiadis, N. Maglaveras, C.
Pappas , A hybrid image segmentation algorithm is proposed which combines edge- and region-based techniques through the morphological algorithm of watersheds.
The algorithm consists of the following steps: a Edge-preserving statistical noise reduction, b Gradient Approximation, c Detection of watersheds o Abstract - Cited by 2 1 self - Add to MetaCart A hybrid image segmentation algorithm is proposed which combines edge- and region-based techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: a Edge-preserving 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.
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