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检索条件"任意字段=12th International Conference on Computing and Pattern Recognition, ICCPR 2023"

共 284 条记录，以下是221-230 订阅

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Segmentation of diverse image types using opening and closing

Segmentation of diverse image types using opening and closin...

引用

international conference on pattern recognition

作者： M. Lybanon S.M. Lea S.M. Himes Naval Res. Lab. Washington DC USA University of North Carolina Greensboro USA Forsyth Memorial Hospital USA

A flexible approach to image segmentation, originally developed for optical astronomical images, is useful for analyzing infrared oceanographic images and solar magnetograms; it should be applicable in other problem domains. the technique is based on the morphological operations of opening and closing. the original computer implementation was designed to find star boundaries in optical astronomical images, where the background is dark and stars, the brightest objects, are uniform or increase in intensity toward their centers. For infrared ocean images, the aim is to find fronts and associated rings; for magnetograms, the aim is to find sunspots, magnetically active regions of the solar surface. Rings, fronts, and sunspots are not very star-like. Hence these images must be pre-processed before applying the method. the edges found from images processed with the algorithm are useful as inputs to other processing, e.g. in automated tracking of ocean features.

关键词： Image segmentation Infrared imaging Oceans Image analysis Magnetic analysis Magnetic domains Morphological operations Optical computing Optical design Sea surface

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VLSI arrays for speech processing with linear predictive coding

VLSI arrays for speech processing with linear predictive cod...

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international conference on pattern recognition

作者： Y.Y. Tang Tao Li C.Y. Suen Institute of Electronic Information Engineering Chongqing University Chongqing China Department of Computer Science Concordia University Montreal West QUE Canada Centre for Pattern Recognition and Machine Intelligence

the covariance analysis of linear predictive coding has wide applications, especially in speech recognition and speech signal processing. Real-time applications demand very high processing speed for linear predictive coding analysis. VLSI technology which possesses properties of low-cost, high-speed and massive computing capabilities is a suitable candidate. In this paper, systolic array processors for the covariance analysis of linear predictive coding are developed. the covariance analysis of linear predictive coding contains a large set of irregular and nested recurrence equations. Systolizing the algorithm is a difficult task for such a complex problem, Existing methods of systematic design for systolic arrays are not much helpful to this problem. To overcome it, a break-combination method is presented in this paper. In this manner, the task is first decomposed and then mapped onto several interconnected systolic arrays. the resulting systolic arrays of the sub problems are then combined to form a complete solution.

关键词： Very large scale integration Speech processing Linear predictive coding Systolic arrays Speech analysis Signal analysis Speech recognition Speech coding Array signal processing Difference equations

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Page segmentation using decision integration and wavelet packets

Page segmentation using decision integration and wavelet pac...

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international conference on pattern recognition

作者： K. Etemad D. Doermann R. Chellappa Department of Electrical Engineering University of Maryland College Park MD USA University System of Maryland Adelphi MD US

A new algorithm for layout-independent document page segmentation is suggested. Text, image and graphics regions in a document image are treated as three different "texture" classes. Soft local decisions on small blocks are made using wavelet packet based feature vectors. Segmentation is performed by propagating and integrating soft local decisions over neighboring blocks, within and across scales. the "uncertainties" associated with local decisions are reduced as more contextual evidence is incorporated in the process of decision integration. the majority, taken over weighted combined votes, determines the final decision. the suggested algorithm is based on parallel independent computations which have low complexity. It can also be applied to other signal and image segmentation tasks.

关键词： Wavelet packets Image segmentation Layout Graphics Automation Educational institutions Voting Concurrent computing Information retrieval Image databases

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Use of the surface-attribute probe for 3-D object characterization

Use of the surface-attribute probe for 3-D object characteri...

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international conference on pattern recognition

作者： Y. Tan H. Freeman Department of Electrical and Computer Engineering Rutgers State University of New Jersey Piscataway NJ USA

this paper deals with the classical problem of 3-D object recognition, in which we are asked to establish unique correspondence between an arbitrarily oriented object apparent in an image and one of a set of known model objects, or to demonstrate that no such correspondence exists. We have developed a "smart" approach to this problem, which permits us to extract only as much feature data as is needed to perform the recognition task rapidly and efficiently. the object is assumed illuminated with patterned light, and both edge and surface-curvature features are extracted in a mutually reinforcing manner from selected regions of interest in the image. An "active model" is hypothesized and progressively refined by extracting feature data from an increasing number of judiciously chosen image regions by means of a so-called surface-attribute probe (SAP). the novel features of the method lie in the use of selectively placed, variable-size windows to extract image features in a progressive refinement manner. the method alternates between using surface-discontinuity features (edges) to guide the extraction of surface-curvature attributes, and using surface-curvature attributes to refine the extraction of surface-discontinuity features. the scheme handles planar as well as curved-surface objects, minimizes unproductive activity, and lends itself to parallel-processor implementation.

关键词： Probes Data mining Layout Feature extraction Object recognition Surface texture Shape Image segmentation Image recognition Grid computing

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Research on Defect Segmentation Algorithm of Industrial Products Based on Transformer

Research on Defect Segmentation Algorithm of Industrial Prod...

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Information and Communication Technology (ICTech), international conference of

作者： Bo Yu Zijian Yan Wei Wang Shenyang Institute of computing technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Computer Sciences and Engineering Northeastern University Shenyang China

For detecting surface defects of industrial products, especially on recognition accuracy and more detailed boundary recognition, an improved semantic segmentation model based on SegFormer network is put forward in this paper. In the decoder part of the model, the bi-directional feature pyramid network (BiFPN) structure is used to receive the multi-scale feature maps output from encoder, and semantic information between feature maps of different sizes are integrated. then, the integrated semantic features are input to Boundary Refinement (BR) module to enhance Boundary information and enhance the final segmentation effect, so that the improved Segformer structure is better applicable to surface defect detection of industrial products.

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Photonic reservoir computing: A new approach to optical information processing

Photonic reservoir computing: A new approach to optical info...

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international conference on Transparent Optical Networks

作者： Kristof Vandoorne Martin Fiers David Verstraeten Benjamin Schrauwen Joni Dambre Peter Bienstman Photonics Research Group Department of Information Technology Ghent University IMEC Ghent Belgium PARIS Department of Electronics and Information Systems Ghent University Ghent Belgium

ISBN: (纸本)9781424477999;9781424477975

Despite ever increasing computational power, recognition and classification problems remain challenging to solve. Recently advances have been made by the introduction of the new concept of reservoir computing. this is a methodology coming from the field of machine learning and neural networks and has been successfully used in several pattern classification problems, like speech and image recognition. the implementations have so far been in software, limiting their speed and power efficiency. Photonics could be an excellent platform for a hardware implementation of this concept because of its inherent parallelism and unique nonlinear behaviour. We propose using a network of coupled Semiconductor Optical Amplifiers (SOA) and show in simulation that it could be used as a reservoir by comparing it on a benchmark speech recognition task to conventional software implementations. In spite of several differences, they perform as good as or better than conventional implementations. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed. We will also address the role phase plays on the reservoir performance.

关键词： Reservoirs Optical computing Information processing Semiconductor optical amplifiers High speed optical techniques Nonlinear optics Machine learning Neural networks pattern classification Speech

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Knowledge-Based Intelligent Information and Engineering Systems 2008

引用

丛书名： Lecture Notes in Computer Science

2008年

作者： Ignac Lovrek

ISBN: (数字)9783540855651

ISBN: (纸本)9783540855644

the three volume set LNAI 5177, LNAI 5178, and LNAI 5179, constitutes the refereed proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008, held in Zagreb, Croatia, in September 2008. the 316 revised papers presented were carefully reviewed and selected. the papers present a wealth of original research results from the field of intelligent information processing in the broadest sense; topics covered in the second volume are artificial intelligence driven engineering design optimization; biomedical informatics: intelligent information management from nanomedicine to public health; communicative intelligence; computational intelligence for image processing and pattern recognition; computational intelligence in human cancer research; computational intelligence techniques for Web personalization; computational intelligent techniques for bioprocess modelling, monitoring and control; intelligent computing for Grid; intelligent security techniques; intelligent utilization of soft computing techniques; reasoning-based intelligent systems: relevant reasoning for discovery and prediction; spatio-temporal database concept support for organizing virtual earth; advanced knowledge-based systems; chance discovery; innovation-oriented knowledge management platform; knowledge-based creativity support systems; knowledge-based interface systems; knowledge-based multi-criteria decision support; and knowledge-based systems for e-business.

关键词： Electronic apparatus and appliances Automatic control Congresses. Artificial intelligence Congresses. Expert systems (Computer science) Congresses.

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ICDAR 2013 Competition on Historical Book recognition (HBR 2013)

ICDAR 2013 Competition on Historical Book Recognition (HBR 2...

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international conference on Document Analysis and recognition

作者： A. Antonacopoulos C. Clausner C. Papadopoulos S. Pletschacher Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Salford Manchester UK

this paper presents an objective comparative evaluation of layout analysis and recognition methods for scanned historical books. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2013 and the 2nd international Workshop on Historical Document Imaging and Processing (HIP2013), presenting the results of the evaluation of five methods - three submitted and two state-of-the-art systems (one commercial and one open-source). three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions, one evaluating segmentation and region classification (with a text extraction goal) and the other the whole pipeline including recognition. the results indicate that there is a convergence to a certain methodology, in terms of layout analysis, with some variations in the approach. However, there is still a considerable need to develop robust methods that deal with the idiosyncrasies of historical books, especially for OCR.

关键词： Layout Optical character recognition software Particle separators Performance evaluation Video recording Text analysis Image segmentation

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Deep Feature Learning for Image-Based Kinship Verification 40th

Deep Feature Learning for Image-Based Kinship Verification

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40th Computer Graphics international conference (CGI)

作者： Zhao, Shuhuan Wang, Chunrong Liu, Shuaiqi Cheng, Hongfang Hebei Univ Coll Elect & Informat Engn Baoding 071002 Peoples R China Machine Vis Technol Innovat Ctr Hebei Baoding 071002 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit NLPR Beijing 100190 Peoples R China

ISBN: (纸本)9783031500688;9783031500695

Facial image-based kinship verification is one of the challenging tasks in computer vision. It has many potential applications, such as human trafficking, studying human genetics, generating family maps, family photo albums, etc. therefore, we propose a deep feature learning method (DFLKV) which can extract more discriminative features for kinship verification. For a pair of facial images, we firstly design a network with multi-scale channel attention for the features extraction;then, select four methods for feature fusion;finally, infer kinship based on the fused features. We jointly adopt the contrastive loss and the binary cross-entropy loss to compute matching degree for paired samples. the experimental results on four widely used datasets KinFaceW-I, KinFaceW-II, Cornell KinFace and TS KinFace to validate the effectiveness of our approach.

关键词： Kinship verification Deep feature learning Channel attention

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Deep Reinforcement Learning Based Intelligent Resource Allocation Techniques with Applications to Cloud computing 6th

Deep Reinforcement Learning Based Intelligent Resource Alloc...

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Kaur, Ramanpreet Anand, Divya Kaur, Upinder Kaur, Jaskiran Verma, Sahil Kavita Department of Computer Application Lovely Professional University Punjab Phagwara India Baba Farid College Punjab Bathinda India Lovely Professional University Punjab Phagwara India Akal University Punjab Talwandi Sabo India NMIMS Punjab Chandigarh India CGC Jhanjeri Punjab Mohali India

ISBN: (纸本)9783031530814

Resources allocation in cloud computing needs an efficient and accurate prediction of network workload. To predict the high dimensional and high variable data is difficult to predict. It leads to failing services level agreement and quality of services. To address these challenges deep reinforcement learning techniques are used for predicting the heterogeneous workload on the network. this paper presents a comprehensive review of deep reinforcement learning techniques. It includes the use of deep reinforcement learning in the field of resource allocation, task scheduling, traffic identification, future prediction and to fulfil the QoS and SLA. Some parameters such as SLA violation, Resource Allocation (RA), cost, the response time (RT), and resource prediction (RP) are also discussed. A case study related to deep reinforcement learning for automated task scheduling in cloud computing is also included which encourages us to use these techniques for task scheduling without any response delay and proper resource utilization. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Resource allocation

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