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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems"

共 12724 条记录，以下是4811-4820 订阅

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Step shift: A fast image segmentation algorithm and its hardware implementation for next-generation sequencing fluorescence data

Step shift: A fast image segmentation algorithm and its hard...

引用

IEEE Asia-Pacific conference on Circuits and systems

作者： Xiao-Xuan Huang Chun-Hsien Ho Yu-Cheng Li Nae-Chyun Chen Yi-Chang Lu Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Institute of Statistical Science Academia Sinica Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9781509015719

In this study, an image segmentation algorithm for processing Next-Generation Sequencing (NGS) fluorescence data is proposed. In NGS applications, fluorescent spots are randomly distributed, the number of spots is large, and the sizes of these spots are small. All the characteristics make the conventional segmentation algorithm not as efficient. To speed up the segmentation process, we propose a hardware-compatible algorithm, Step Shift, and implement an ASIC design for hardware acceleration. When compared to the software version of Step Shift method, our hardware accelerator can reach 9.2 times speed-up in the experiments.

关键词： Hardware image segmentation Clustering algorithms Algorithm design and analysis Software algorithms Switches Registers

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Evaluative study of PSO/snake hybrid algorithm and gradient path labeling for calculating solar differential rotation 7

Evaluative study of PSO/snake hybrid algorithm and gradient ...

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7th International Joint conference on Computational Intelligence, IJCCI 2015

作者： Shahamatnia, Ehsan Mora, André Dorotovič, Ivan Ribeiro, Rita A. Fonseca, José M. Computational Intelligence Group of CTS/UNINOVA Caparica Portugal FCT/NOVA University of Lisbon Monte de Caparica2829-516 Portugal Slovak Central Observatory Hurbanovo Slovakia

ISBN: (纸本)9783662535240

PSO/Snake hybrid algorithm is a merge of particle swarm optimization (PSO), a successful population based optimization technique, and the Snake model, a specialized image processing algorithm. In the PSO/Snake hybrid algorithm each particle in the population represents only a portion of the solution and the population, as a whole, will converge to the final complete solution. In this model there is a one-to-one relation between Snake model snaxels and PSO particles with the PSO’s kinematics being modified accordingly to the snake model dynamics. This paper provides an evaluative study on the performance of the customized PSO/Snake algorithm in solving a real-world problem from astrophysics domain and comparing the results with Gradient Path Labeling (GPL) image segmentation algorithm. The GPL algorithm segments the image into regions according to its intensity from where the relevant ones can be selected based on their features. A specific type of solar features called coronal bright points have been tracked in a series of solar images using both algorithms and the solar differential rotation is calculated accordingly. The final results are compared with those already reported in the literature. © Springer-Verlag Berlin Heidelberg 2016.

关键词： image segmentation

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Brain MRI image Segmentation Based on Improved Fuzzy C-Means Algorithm

Brain MRI Image Segmentation Based on Improved Fuzzy C-Means...

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International conference on Smart City and systems Engineering (ICSCSE)

作者： Shiling Sun Shuxun Yan Ying Wang Yun Li Henan College of Traditional Chinese Medicine Zhengzhou Henan China The First Affiliated Hospital Zhengzhou University Zhengzhou Henan China

ISBN: (纸本)9781509055319

image segmentation is a challenge and difficult task in image processing, and the foundation of image analysis and identifying. This paper mainly studies the means clustering image segmentation. In view of the traditional clustering image segmentation algorithm for image segmentation accuracy is low problem, put forward a kind of fuzzy control based on C-means clustering image segmentation method. Methods firstly in clustering image segmentation algorithm based on fast, using fuzzy C-means clustering algorithm for image segmentation. The experimental results show that the algorithm in clustering, to optimize the performance of the same premise, image segmentation edge clear, segmentation better than traditional clustering algorithm for image segmentation.

关键词： image segmentation Clustering algorithms Algorithm design and analysis Magnetic resonance imaging image edge detection Brain modeling

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Road sign recognition system on Raspberry Pi

Road sign recognition system on Raspberry Pi

引用

IEEE Long Island systems, Applications and Technology conference (LISAT)

作者： Enis Bilgin Stefan Robila Department of Computer Science Montclair State University Montclair NJ USA

ISBN: (纸本)9781467384919

Digital image processing, i.e. the use of computer systems to process pictures, has applications in many fields, including of medicine, space exploration, geology and oceanography and continues to increase in its applicability. The main objective of this paper is to demonstrate the ability of image processing algorithms on a small computing platform. Specifically we created a road sign recognition system based on an embedded system that reads and recognizes speed signs. The paper describes the characteristics of speed signs, requirements and difficulties behind implementing a real-time base system with embedded system, and how to deal with numbers using image processing techniques based on shape and dimension analysis. The paper also shows the techniques used for classification and recognition. Color analysis also plays a specifically important role in many other different applications for road sign detection, this paper points to many problems regarding stability of color detection due to daylight conditions, so absence of color model can led a better solution. In this project lightweight techniques were mainly used due to limitation of real- time based application and Raspberry Pi capabilities. Raspberry Pi is the main target for the implementation, as it provides an interface between sensors, database, and image processing results, while also performing functions to manipulate peripheral units (usb dongle, keyboard etc.).

关键词： Optical character recognition software Shape Character recognition Engines image recognition Cameras

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Colour constancy using sub-blocks of the image

Colour constancy using sub-blocks of the image

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International conference on Signals and Electronic systems, ICSES

作者： Md. Akmol Hussain Akbar Sheikh Akbari Bruhanth Mallik School of Computing Leeds Beckett University Leeds UK

Colour constancy is the ability to measure the colour of objects independent of the light source, while colour casting is the presence of unwanted colour in digital images. Colour casting significantly affects the performance of image processing algorithms such as image segmentation and object recognition. The presence of large uniform background within the image considerably deteriorates the performance of many state of the art colour constancy algorithms. This paper presents a colour constancy method using the sub-blocks of the image to alleviate the effect of large uniform colour area of the scene. The proposed method divides the input image into a number of non-overlapping blocks, and Average Absolute Difference (AAD) value of each block colour component is calculated. The blocks with AAD greater than threshold values, which are empirically determined for each colour component, are considered to have sufficient colour information. The selected blocks are then used to determine the scaling factors to achieve achromatic values for the input image colour components. Comparing the performance of the proposed technique with the state of the art methods using images from three datasets shows that the proposed method outperforms the state of the art techniques in the presence of large uniform colour patches.

关键词： image color analysis image edge detection Cameras Speech recognition Light sources Algorithm design and analysis

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GPU implementation of multi-scale Retinex image enhancement algorithm

GPU implementation of multi-scale Retinex image enhancement ...

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ACS/IEEE International conference on Computer systems and Applications

作者： Hui Li Weihao Xie Xingang Wang Shousheng Liu Yingying Gai Lei Yang Institute of Oceanographic Instrumentation Shandong Academy of Science Qingdao China

Multi-scale Retinex algorithm is an image enhancement algorithm that aims at image reconstruction. The algorithm maintains the high fidelity and the dynamic range compression of the image, so the enhancement effect is obvious. The algorithm exploits a large number of convolution operations to achieve dynamic range compression and color/brightness rendition, and the calculation time increased significantly with the increase of the image resolution. In order to improve the real-time performance of the algorithm, a multi-scale Retinex image enhancement algorithm based on GPU CUDA is proposed in this paper. Through the data mining and parallel analysis of the algorithm, time-consuming modules of the calculation, such as Gauss filter, convolution, logarithm difference, are implemented in GPU by exploiting the massively parallel threading and heterogeneous memory hierarchy of GPGPU to improve efficiency. The experimental results show that the algorithm can improve the computing speed significantly in NVIDIA Tesla K20 and CUDA7.5, and with the increase of image resolution, the maximum speedup can reach 202 times.

关键词： Graphics processing units Instruction sets Convolution Algorithm design and analysis Parallel processing Heuristic algorithms Kernel

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QUANTITATIVE STUDY OF image FEATURES OF CLUSTERED MICROCALCIFICATIONS IN FOR-PRESENTATION MAMMOGRAMS

QUANTITATIVE STUDY OF IMAGE FEATURES OF CLUSTERED MICROCALCI...

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IEEE International conference on image processing

作者： Juan Wang Yongyi Yang Robert M. Nishikawa Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL 60616 USA Department of Radiology University of Pittsburgh Pittsburgh PA 15213 USA

ISBN: (纸本)9781467399623

Mammogram images are now increasingly acquired with full-field digital mammography (FFDM) systems in the clinics. Traditionally, the "for-processing" format of FFDM images is used in computer-aided diagnosis (CAD) of breast cancer. In this study, we investigate the feasibility of using "for-presentation" format of FFDM (which are more readily available) in development of CAD algorithms for microcalcification (MC) lesions. We conduct a quantitative evaluation of both the image features and the detectability of individual MCs on a set of 188 mammograms acquired in both formats. The results demonstrate that there is a high degree of agreement in the image features between the two image formats, and that a slight increase in false-positives in MC detection is observed in for-presentation images.

关键词： Lesions Feature extraction Mammography Design automation Detectors Standards Dogs

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Multi-level parallelism analysis and system-level simulation for many-core Vision processor design

Multi-level parallelism analysis and system-level simulation...

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Mediterranean conference on Embedded Computing (MECO)

作者： Jones Y. Mori Michael Hübner Department of Mechanical Engineering University of Brasilia Brazil Chair of Embedded Systems for Information Technology (ESIT) Ruhr-University Bochum Germany

The technology convergence and the evolution of embedded systems to multi/many-core architectures allow envisioning future cameras as many-core systems able to process complex image processing and Computer Vision (IP/CV) applications. IP/CV algorithms have natural parallelism which must be efficiently explored to meet embedded application's constraints (real-time, power consumption, silicon area, temperature management, fault tolerance, and so on). In the case of many-core architectures, the efficiency comes not only from the number and type of processing cores but how they communicate and how the memory is organized. In this work, we show a multi-level parallelism study of IP/CV algorithms/applications, analyzing how to explore the different features available in many-core architecture's design space. The analysis is performed using a high-level SystemC/TLM2.0 platform specially developed for this task. As results, we propose a hierarchical parallelism extraction, a transparent programming model and a many-core architecture template for the next generation of vision processors.

关键词： Parallel processing image edge detection Program processors Programming Computer architecture Algorithm design and analysis Space exploration

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A novel image detail preserving impulse noise removal algorithm

A novel image detail preserving impulse noise removal algori...

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IEEE International conference on Multimedia Computing and systems (ICMCS)

作者： Abhinash Kumar Jha Punjal Agarwal Gerald Schaefer Youssef Zaz Hindustan Times HT Media Limited New Delhi India The LNM Institute of Information Technology Jaipur India Department of Computer Science Loughborough University Loughborough U.K. Faculty of Sciences Abdelmalek Essaadi University Tetouan Morocco

In this paper, we introduce a novel edge directed image detail preserving impulse noise removal algorithm. Our approach is based on local image directionality features. Local edge features are analysed on both a downsampled version of the image and the noisy image, and both soft an sharp edges are considered for selective noise removal. Experimental results on a set of standard images show our technique to be effective in removing salt-and-pepper noise even at high noise levels, to yield good image quality and to outperform a number of other noise removal techniques.

关键词： image edge detection Noise measurement image restoration Noise level Signal processing algorithms Switches image denoising

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Combinatorial Energy Learning for image Segmentation 16

Combinatorial Energy Learning for Image Segmentation

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Annual conference on Neural Information processing systems

作者： Jeremy Maitin-Shepard Viren Jain Michal Januszewski Peter Li Pieter Abbeel UC Berkeley Google Google UC Berkeley

ISBN: (纸本)9781510838819

We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image. Our approach, combinatorial energy learning for image segmentation (CELIS) places a particular emphasis on modeling the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the energy function, and for local optimization of this energy in the space of supervoxel agglomerations. We extensively evaluate our method on a publicly available 3-D microscopy dataset with 25 billion voxels of ground truth data. On an 11 billion voxel test set, we find that our method improves volumetric reconstruction accuracy by more than 20% as compared to two state-of-the-art baseline methods: graph-based segmentation of the output of a 3-D convolutional neural network trained to predict boundaries, as well as a random forest classifier trained to agglomerate supervoxels that were generated by a 3-D convolutional neural network.

关键词： image segmentation Neural network Energy sources Neural Networks (Computer) local optimization Learning ground truth

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