The Experts below are selected from a list of 74406 Experts worldwide ranked by ideXlab platform
Fabio Somenzi - One of the best experts on this subject based on the ideXlab platform.
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An Algorithm for Strongly Connected Component Analysis in n log n Symbolic Steps
Formal Methods in System Design, 2006Co-Authors: Roderick Bloem, Harold N. Gabow, Fabio SomenziAbstract:We present a symbolic algorithm for strongly Connected Component decomposition. The algorithm performs Θ( n log n ) image and preimage computations in the worst case, where n is the number of nodes in the graph. This is an improvement over the previously known quadratic bound. The algorithm can be used to decide emptiness of Büchi automata with the same complexity bound, improving Emerson and Lei's quadratic bound, and emptiness of Streett automata, with a similar bound in terms of nodes. It also leads to an improved procedure for the generation of nonemptiness witnesses.
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FMCAD - An Algorithm for Strongly Connected Component Analysis in n log n Symbolic Steps
2000Co-Authors: Roderick Bloem, Harold N. Gabow, Fabio SomenziAbstract:We present a symbolic algorithm for strongly Connected Component decomposition. The algorithm performs ?(n log n) image and preimage computations in the worst case, where n is the number of nodes in the graph. This is an improvement over the previously known quadratic bound. The algorithm can be used to decide emptiness of B?chi automata with the same complexity bound, improving Emerson and Lei's quadratic bound, and emptiness of Streett automata, with a similar bound in terms of nodes. It also leads to an improved procedure for the generation of nonemptiness witnesses.
Lionel Lacassagne - One of the best experts on this subject based on the ideXlab platform.
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A new run-based Connected Component Labeling for efficiently analyzing and processing holes
2021Co-Authors: Florian Lemaitre, Lionel LacassagneAbstract:This article introduces a new Connected Component labeling and analysis algorithm for foreground and background labeling that computes the adjacency tree. The computation of features (bounding boxes, first statistical moments, Euler number) is done on-the-fly. The transitive closure enables an efficient hole processing that can be filled while their features are merged with the surrounding Connected Component without the need to rescan the image. A comparison with existing algorithms shows that this new algorithm can do all these computations faster than algorithms processing black and white Components.
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How to speed Connected Component Labeling up with SIMD RLE algorithms
2020Co-Authors: Florian Lemaitre, Arthur Hennequin, Lionel LacassagneAbstract:The research in Connected Component Labeling, although old, is still very active and several efficient algorithms for CPUs and GPUs have emerged during the last years and are always improving the performance. This article introduces a new SIMD run-based algorithm for CCL. We show how RLE compression can be SIMDized and used to accelerate scalar run-based CCL algorithms. A benchmark done on Intel, AMD and ARM processors shows that this new algorithm outperforms the State-of-the-Art by an average factor of ×1.7 on AVX2 machines and ×1.9 on Intel Xeon Skylake with AVX512.
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Designing efficient SIMD algorithms for direct Connected Component Labeling
2019Co-Authors: Arthur Hennequin, Ian Masliah, Lionel LacassagneAbstract:Connected Component Labeling (CCL) is a fundamental algorithm in computer vision, and is often required for real-time applications. It consists in assigning a unique number to each Connected Component of a binary image. In recent years, we have seen the emergence of direct parallel algorithms on multicore CPUs, GPUs and FPGAs whereas, there are only iterative algorithms for SIMD implementation. In this article, we introduce new direct SIMD algorithm for Connected Component Labeling. They are based on the new Scatter-Gather, Collision Detection (CD) and Vector Length (VL) instructions available in the recent Intel AVX512 instruction set. These algorithms have also been adapted for multicore CPU architectures and tested for each available SIMD vector length. These new algorithms based on SIMD Union-Find algorithms can be applied to other domains such as graphs algorithms manipulating Union-Find structures.
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Parallel Light Speed Labeling: an efficient Connected Component algorithm for labeling and analysis on multi-core processors
Journal of Real-Time Image Processing, 2018Co-Authors: Laurent Cabaret, Lionel Lacassagne, Daniel EtiembleAbstract:In the last decade, many papers have been published to present sequential Connected Component labeling (CCL) algorithms. As modern processors are multi-core and tend to many cores, designing a CCL algorithm should address parallelism and multithreading. After a review of sequential CCL algorithms and a study of their variations, this paper presents the parallel version of the Light Speed Labeling for Connected Component Analysis (CCA) and compares it to our parallelized implementations of State-of-the-Art sequential algorithms. We provide some benchmarks that help to figure out the intrinsic differences between these parallel algorithms. We show that thanks to its run-based processing, the LSL is intrinsically more efficient and faster than all pixel-based algorithms. We show also, that all the pixel-based are memory-bound on multi-socket machines and so are inefficient and do not scale, whereas LSL, thanks to its RLE compression can scale on such high-end machines. On a 4×15-core machine, and for 8192×8192 images, LSL outperforms its best competitor by a factor ×10.8 and achieves a throughput of 42.4 gigapixel labeled per second.
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distanceless label propagation an efficient direct Connected Component labeling algorithm for gpus
International Conference on Image Processing, 2017Co-Authors: Laurent Cabaret, Lionel Lacassagne, Daniel EtiembleAbstract:Modern computer architectures are mainly composed of multi-core processors and GPUs. Consequently, solely providing a sequential implementation of algorithms or comparing algorithm performance without regard to architecture is no longer pertinent. Today, algorithms have to address parallelism, multithreading and memory topology (private/shared memory, cache or scratchpad, …). Most Connected Component Labeling (CCL) algorithms are sequential, direct and optimized for processors. Few were designed specifically for GPU architectures and none were designed to be adapted to different architectures. The most efficient GPU implementations are iterative; in order to manage synchronizations between processing units, but the number of iterations depends on the image shape and density. This paper describes the DLP (Distanceless Label Propagation) algorithms, an adaptable set of algorithms usable both on GPU and multi-core architectures, and DLP-GPU, an efficient direct CCL algorithm for GPU based on DLP mechanisms.
Eric Schost - One of the best experts on this subject based on the ideXlab platform.
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properness defects of projections and computation of at least one point in each Connected Component of a real algebraic set
Discrete and Computational Geometry, 2004Co-Authors: Eric SchostAbstract:Computing at least one point in each Connected Component of a real algebraic set is a basic subroutine to decide emptiness of semi-algebraic sets, which is a fundamental algorithmic problem in effective real algebraic geometry. In this article we propose a new algorithm for the former task, which avoids a hypothesis of properness required in many of the previous methods. We show how studying the set of non-properness of a linear projection ź enables us to detect the Connected Components of a real algebraic set without critical points for ź. Our algorithm is based on this observation and its practical counterpoint, using the triangular representation of algebraic varieties. Our experiments show its efficiency on a family of examples.
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Properness defects of projections and computation of at least one point in each Connected Component of a real algebraic set
Discrete and Computational Geometry, 2004Co-Authors: Mohab Safey El Din, Eric SchostAbstract:Computing at least one point in each Connected Component of a real algebraic set is a basic subroutine to decide emptiness of semi-algbraic sets, which is a fundamental algorithmic problem in effective real algebraic geometry. In this article, we propose a new algorithm for this task, which avoids a hypothesis of properness required in many of the previous methods. We show how studying the set of non-properness of a linear projection enables to detect Connected Components of a real algebraic set without critical points. Our algorithm is based on this result and its practical counterpoint, using the triangular representation of algebraic varieties. Our experiments show its efficiency on a family of examples.
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polar varieties and computation of one point in each Connected Component of a smooth real algebraic set
International Symposium on Symbolic and Algebraic Computation, 2003Co-Authors: Eric SchostAbstract:Let f 1 , ldots, f s be polynomials in Q[X 1 , ..., X n ] that generate a radical ideal and let V be their complex zero-set. Suppose that V is smooth and equidimensional; then we show that computing suitable sections of the polar varieties associated to generic projections of V gives at least one point in each Connected Component of V ∩ Rn. We deduce an algorithm that extends that of Bank, Giusti, Heintz and Mbakop to non-compact situations. Its arithmetic complexity is polynomial in the complexity of evaluation of the input system, an intrinsic algebraic quantity and a combinatorial quantity.
T. Srihari - One of the best experts on this subject based on the ideXlab platform.
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real time speed bump detection using gaussian filtering and Connected Component approach
Circuits and Systems, 2016Co-Authors: W. Devapriya, Nelson Kennedy C Babu, T. SrihariAbstract:An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notification systems, Automatic road enforcement, Collision avoidance systems, Automatic parking, Map database management, etc. Advance Driver Assists System (ADAS) belongs to ITS which provides alert or warning or information to the user during driving. The proposed method uses Gaussian filtering and Median filtering to remove noise in the image. Subsequently image subtraction is achieved by subtracting Median filtered image from Gaussian filtered image. The resultant image is converted to binary image and the regions are analyzed using Connected Component approach. The prior work on speed bump detection is achieved using sensors which are failed to detect speed bumps that are constructed with small height and the detection rate is affected due to erroneous identification. And the smartphone and accelerometer methodologies are not perfectly suitable for real time scenario due to GPS error, network overload, real-time delay, accuracy and battery running out. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.
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Real time speed bump detection using Gaussian filtering and Connected Component approach
2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), 2016Co-Authors: W. Devapriya, C. Nelson Kennedy Babu, T. SrihariAbstract:Nowadays the number of vehicle users increasing day by day, so the vehicle manufacture trying to develop higher end vehicle that reduce the complexity during driving. Advance Driver Assists Sytsem is one of such type that provide alert, warning and information during driving. In our proposed method Gaussian filtering, median filtering and Connected Component analysis are used to detect speed bump. This system go well with the roads that are constructed with proper painting. Several existing method need special hardware, sensors, accelerometer and GPS for detecting speed bump.
Xinhua Zhuang - One of the best experts on this subject based on the ideXlab platform.
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significance linked Connected Component analysis for wavelet image coding
IEEE Transactions on Image Processing, 1999Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:The success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's (1993) embedded zerotree wavelets (EZW), Servetto et al.'s (1995) morphological representation of wavelet data (MRWD), and Said and Pearlman's (see IEEE Trans. Circuits Syst. Video Technol., vol.6, p.245-50, 1996) set partitioning in hierarchical trees (SPIHT). We develop a novel wavelet image coder called significance-linked Connected Component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, MRWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256/spl times/256 grayscale texture images compressed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.
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significance linked Connected Component analysis for very low bit rate wavelet video coding
IEEE Transactions on Circuits and Systems for Video Technology, 1999Co-Authors: J Vass, Bingbing Chai, Kannappan Palaniappan, Xinhua ZhuangAbstract:A novel hybrid wavelet video coding algorithm termed video significance-linked Connected Component analysis (VSLCCA) is developed for very low bit-rate applications. In the proposed VSLCCA codec, first, fine-tuned motion estimation based on the H.263 Recommendation is developed to reduce temporal redundancy, and exhaustive overlapped block motion compensation is utilized to ensure coherency in motion compensated error frames. Second, the wavelet transform is applied to each coherent motion compensated error frame to attain global energy compaction. Third, significant fields of wavelet-transformed error frames are organized and represented as significance-linked Connected Components so that both the within-subband clustering and the cross-scale dependency are exploited. Last, the horizontal and vertical Components of motion vectors are encoded separately using adaptive arithmetic coding while significant wavelet coefficients are encoded in bit-plane order by using high order Markov source modeling and adaptive arithmetic coding. Experimental results on eight standard MPEG-4 test sequences show that for intraframe coding, on average the proposed codec exceeds H.263 and ZTE (zero-tree entropy) in peak signal-to-noise ratio by as much as 2.07 and 1.38 dB at 28 kbit/s, respectively. For entire sequence coding, VSLCCA is superior to H.263 and ZTE by 0.35 and 0.71 dB on average, respectively.
