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

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Implementation of image processing algorithms Based on GPU

Implementation of Image Processing Algorithms Based on GPU

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International Scientific and Technical conference on Computer Science and Information Technologies

作者： Ivan Tsmots Oleh Berezkyi Ihor Ihnatiev Iryna Gumovska Automated control systems Department Lviv Polytechnic National University Computer Engineering Department Ternopil National Economic University Department of Foreign Languages and Professional Communication Ternopil National Economic University

ISBN: (纸本)9781509027415

The most perspective direction in the image processing became the use of graphics processing units (GPU). The main advantage of it is the exceptional dedication to work with 2d and 3d graphics, respectively processing large volumes of similar data. The consequence of this was the emergence of a single graphics card on the board of several stream processors responsible for performing the same type of command. However, a major breakthrough in the use of the GPU is the emergence of the possibility of programming the stream processors (vertex and pixel) image processing. The special feature of the group of processors are parallel processing, i.e., for a pixel processor unit is one piece or texel. For example, modern GPU carry on board more than 1,000 stream processors, each type of which can significantly improve performance by parallel use of all processors. Work related to the parallelization takes on the GPU, the engineer must identify only the basic algorithm for a single processor.

关键词： Neural oriented computer system An integrated approach Principles Structure Specialized module algorithm CUDA

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Domain Separation Networks 16

Domain Separation Networks

引用

Annual conference on Neural Information processing systems

作者： Konstantinos Bousmalis George Trigeorgis Nathan Silberman Dilip Krishnan Dumitru Erhan Google Brain Mountain View CA Imperial College London London UK Google Research New York NY Google Research Cambridge MA

ISBN: (纸本)9781510838819

The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach circumventing this cost is training models on synthetic data where annotations are provided automatically. Despite their appeal, such models often fail to generalize from synthetic to real images, necessitating domain adaptation algorithms to manipulate these models before they can be successfully applied. Existing approaches focus either on mapping representations from one domain to the other, or on learning to extract features that are invariant to the domain from which they were extracted. However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain. We hypothesize that explicitly modeling what is unique to each domain can improve a model's ability to extract domain-invariant features. Inspired by work on private-shared component analysis, we explicitly learn to extract image representations that are partitioned into two subspaces: one component which is private to each domain and one which is shared across domains. Our model is trained to not only perform the task we care about in the source domain, but also to use the partitioned representation to reconstruct the images from both domains. Our novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process.

关键词： Domain ANNOTATIONS domains image representation Machine learning algorithms Visualization Data Collection Methods Individual Characteristics synthetic data appeal FOCUSING Phyla

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Architecture for Parallelizing Decoding of Marker-Free Variable Length Code Streams

Architecture for Parallelizing Decoding of Marker-Free Varia...

引用

IEEE Signal processing algorithms, Architectures, Arrangements, and Applications conference

作者： Yousef Baroud Jose Manuel Marinos Velarde Sven Simon Institut fur Parallele und Verteilte Systeme Universitat Stuttgart

ISBN: (纸本)9781509026616

Parallel decoding of encoded data streams is an important scheme that is widely applied in image and video applications. Especially with the increasing demand on higher resolutions and frame rates, parallel decoding becomes very useful to meet the throughput requirements. Parallel decoding is conventionally enabled by inserting markers into the variable length code (VLC) stream. The markers allow for an easy separation of the sub-streams in order to be processed in parallel. The use of markers, however, adversely affects the compression making it unfavorable especially when the bandwidth of the channel is almost fully utilized. In a previous work, we proposed a proof of concept marker-free architecture that enables parallel decoding exhibiting the feasibility of the solution. In this paper, an efficient marker-free architecture is proposed. The memory requirements and the performance of the architecture are thoroughly analyzed. The architecture preserves the original compression factor of the system in contrary to the marker-based approaches. In the experimental results, it is shown that the architecture can be readily configured to drive up to 20 parallel decoders on a medium range FPGA at high clock rates. In addition, the hardware resources in terms of LUTs, registers and block RAMs are reported from which it can be seen that the resources tend to scale linearly with the number of decoders.

关键词： Decoding Streaming media Registers image coding Transform coding Pipelines Control systems DECODING Building construction ARCHITECTURE transform coding Registers Streaming Video image coding pipeline control systems variable length codes memory requirements decoders Compressibility factors Parallel Lines clock frequency

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Zero resource anti-spoofing detection for unit selection based synthetic speech using image spectrogram artifacts

Zero resource anti-spoofing detection for unit selection bas...

引用

Asia-Pacific Signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Su Jun Leow Eng Siong Chng Chin-hui Lee Nanyang Technological University Singapore Georgia Institute of Technology USA

ISBN: (纸本)9781509024018

Synthesized speech poses a serious threat to speaker verification systems, which is aggravated by speech synthesis systems becoming more freely available and easily adaptable to a target speaker. This motivated research into synthetic speech detection to circumvent the threat. Although current algorithms are effective in the detection of HMM-based speech synthesizers, unit selection based speech synthesizers remain a serious threat due to its ability to generate spoofing speech which easily overcame existing detectors. Current error rates for their detection is a lot higher than that obtained for other spoofing methods. This paper proposes a detection algorithm to counter unit selection based synthesis speech. It is free of training and exploits presence of artifacts in image spectrogram to perform detection. To the best of our knowledge, this is the first attempt targeted for unit selection based synthesis speech. Experimental results show the effectiveness of the proposed approach.

关键词： Speech Spectrogram Synthesizers Speech synthesis Hidden Markov models Vocoders Feature extraction

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FPGA based Hardware Implementation of Automatic Vehicle License Plate Detection System

FPGA based Hardware Implementation of Automatic Vehicle Lice...

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International conference on Advances in Computing, Communications and Informatics

作者： Surbhi Chhabra Himanshu Jain Sandeep Saini Department of Electronics and Communication Engineering The LNM Institute of Information Technology Jaipur (Raj.) India 302031

ISBN: (纸本)9781509020300

Automatic Vehicle License Plate Detection System (AVLPDS) is the extraction of vehicle license plate information from an image. Besides the safety aspects this system is used in many applications, viz. electronic payment systems, freeway, arterial monitoring systems for traffic surveillance etc. The purpose of this paper is to present the FPGA algorithmic model of most efficient algorithm among three algorithms: Edge-based, Connected-Component based and Histogram based. Each approach is analyzed on the basis of precision and recall rates to determine the success of each approach. After comparison, we can say Histogram based approach has an advantage of being simple and thus faster. Therefore, in this paper, we have used Histogram based Edge processing approach to detect the license plate and presented the FPGA implementation of AVLPDS for the same. The whole system is implemented using MATLAB Simulink and Xilinx System Generator (XSG). Use of XSG for image processing effectively reduces the complexity in structural design and contributes for hardware co-simulation. The accuracy of the algorithm is checked for different sets of input images and significant performance improvement has been found, thereby performing an optimal FPGA-based hardware implementation of AVLPDS.

关键词： Automatic Vehicle License Plate Detection System MATLAB Simulink Xilinx System generator VIVADO HLS FPGA ZedBoard image processing

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Fundamental Matrix Estimation Based on Improved Genetic Algorithm

Fundamental Matrix Estimation Based on Improved Genetic Algo...

引用

International conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Ying Zhang Lan Zhang Changyin Sun Guifeng Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China China Southern Power Grid Electric Power Research Institute Guangzhou China

ISBN: (纸本)9781509007691

Fundamental matrix estimation is the key technology in 3D reconstruction and widely used in many aspects in computer vision. In this paper, a global search genetic algorithm combining with a local search hill climbing algorithm is proposed to optimize MAPSAC algorithm for estimating fundamental matrix. The average distances between points and epipolar lines under noise and outliers are investigated with synthetic and real image feature point data respectively. The simulation results show proposed method is more robust to noise and outliers and can estimate fundamental matrix more precisely.

关键词： Estimation Genetic algorithms Robustness Geometry Signal processing algorithms Noise level Three-dimensional displays

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Parallel Implementation of FCM-Based Volume Segmentation of 3D images

Parallel Implementation of FCM-Based Volume Segmentation of ...

引用

IEEE/ACS International conference of Computer systems and Applications

作者： Shadi AlZu'bi Moahmmed A. Shehab Mahmoud Al-Ayyoub Elhadj Benkhelifa Yaser Jararweh Zaytoonah University of Jordan Jordan University of Science and Technology Staffordshire University

ISBN: (纸本)9781509043217

Parallel programming has many benefits that can help developers and researchers to improve the performance of some algorithms to become more efficient in real life. This is especially true for systems involving medical images. image segmentation for volume extraction is a famous segmentation process that takes long time to finish execution. In this paper, we consider a new version of the Fuzzy C-Means (FCM) segmentation algorithm (known as IT2FPCM) and provide a parallel implementation of it that is 12X time faster than the sequential implementation. The considered algorithm is based on Interval Type-2 FCM and combines fuzzy and possibilistic ideas in order to obtain higher accuracy. We conduct our experiments using two different machines and the results show that the improvement gains for both machines 11X and 12X, respectively.

关键词： Clustering algorithms Mathematical model image segmentation Three-dimensional displays Graphics processing units Phase change materials Linear programming

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A novel shadow removal algorithm using Niblack segmentation in satellite images

A novel shadow removal algorithm using Niblack segmentation ...

引用

International conference on Communication systems and Networks (ComNet)

作者： Geethu Vijayan S. R. Reshma F. E. Dhanya S. Anju Gayathri R. Nair R. P. Aneesh Mohandas College of Engineering And Technology Kerala University Anad Kerala India Mohandas College of Engineering and Tecnnotoqy Kerala University Anad Kerata India IHRd Regional Centre Trivandrum Kerala India

ISBN: (纸本)9781509033508

Shadow is formed by the interaction of light with object. Effect of shadow is very crucial in the case of satellite image processing. Roads, buildings, trees etc are detected for various applications. But the interference of shadow makes mismatching of these objects. Several algorithms are being developed to detect and reconstruct the shadow region. This paper presents a Shadow detection technique based on Niblack segmentation. Niblack segmentation gives better shadow regions compared to Otsu's thresholding method and Sauvola based thresholding. Reconstruction of the shadow region is done by the Bayesian classifier. This classifier generate a training vector and reconstruct non shadow region from shadow region. Posterior probability is determined to reconstruct the non shadow image intensity level. This algorithm is successfully tested with VHSR images.

关键词： image reconstruction image segmentation Satellites Filtering image edge detection Bayes methods Histograms

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Text recognition in bilingual machine printed image documents — Challenges and survey: A review on principal and crucial concerns of text extraction in bilingual printed images 10th

Text recognition in bilingual machine printed image document...

引用

International conference on (ISCO) Intelligent systems and Control

作者： Shalini Puri Satya Prakash Singh Department of Computer Science Birla Institute of Technology Mesra Ranchi India

ISBN: (纸本)9781467378086

In this digital world, accurate text identification and recognition has become an important key area of image document analysis and processing. Textual data, ranging from simple to complex images along with language variations - mono, bi, tri or multilingual scripts, is identified and extracted. This paper is designed to focus the challenges and complex issues of text recognition in bilingual machine printed imaged documents. Major crucial factors are discovered and mentioned which become the bottlenecks in correct and accurate recognition. With this, a hierarchical structure depicting three Classification Schemes (CS) A, B and C of bilingual printed imaged document is shown, where A, B and C are related to the content form, image mining and language or script determination. Some loopholes of OCR working are also discussed. To analyze the existing algorithms and methods, a survey is presented to focus on their critical issues, proposed solutions along with constraints and errors found during text processing. It leads to find out the shortcomings and limitations of different methods. Various specifications and factors found from the techniques are also shown as their characteristics and are compared relatively to distinguish them. It is observed that most of the existing methods are based on the classification schemes CS A-A1 and C-C1 and C2 and are designed for the script identification with 300 dpi gray scale image using SVM classifier.

关键词： Two dimensional displays Manganese

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LEARNING A LOW-RANK SHARED DICTIONARY FOR OBJECT CLASSIFICATION

LEARNING A LOW-RANK SHARED DICTIONARY FOR OBJECT CLASSIFICAT...

引用

IEEE International conference on image processing

作者： Tiep H. Vu Vishal Monga Pennsylvania State University

ISBN: (纸本)9781467399623

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. Inspired by this observation, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e. claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Further, we propose a new fast and accurate algorithm to solve the sparse coding problems in the learning step, accelerating its convergence. The said algorithm could also be applied to FDDL and its extensions. Experimental results on widely used image databases establish the advantages of our method over state-of-the-art dictionary learning methods.

关键词： Dictionaries Training Sparse matrices Encoding Learning systems Optimization Signal processing algorithms

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