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检索条件"任意字段=12th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2023"

共 89 条记录，以下是61-70 订阅

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Remote Sensing Image Cloud Detection Algorithm Based on Residual Network Module and Pyramid Structure of Dilated Convolution 12

Remote Sensing Image Cloud Detection Algorithm Based on Resi...

引用

SPIE 12th international Symposium on Multispectral Image Processing and pattern recognition, MIPPR 2023

作者： Chen, Yi Zhu, Ying School of Electrical and Information Engineering Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Approximately 50% of the Earth's surface is covered by clouds. Optical remote sensing satellites encounter challenges in capturing clear ground images due to the difficulty of visible photoelectric magnetic waves penetrating clouds. therefore, cloud detection is an essential and basic step in the processing and application of optical satellite remote sensing images. the traditional threshold-based cloud detection methods utilize spectral information of images to set thresholds, which is sensitive and less adaptable to varying imaging conditions. To enhance cloud detection performance, this paper proposes a deep learning method that combines residual network module and pyramid structure of dilated convolution. the method employs an encoder-decoder structure where the residual convolution module replaces conventional convolution kernels, reducing parameter quantity while enhancing feature expression capabilities. Furthermore, a pyramid structure of dilated convolution is introduced between the encoder and decoder to improve the acquisition of global information and reduce misjudgment of cloud pixels. In this work, ablation experiments are conducted to validate the reliability of the proposed network model. To evaluate the effectiveness of the proposed method, GaoFen-1 remote sensing image data was used for experimental verification. the results indicated that compared to traditional methods, the proposed approach achieves satisfactory cloud detection results in various surface types, including barren land, forests, grasslands/crops, and wetlands, while having a smaller model size and parameter quantity. © 2024 SPIE.

关键词： Convolution

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Nonlinear relaxation labeling as growth transformation

Nonlinear relaxation labeling as growth transformation

引用

international conference on pattern recognition

作者： M. Pelillo Dipartimento di Informatica Università di Bari Bari Italy

Presents some new results which demonstrate that, despite its heuristic and simple-minded derivation, the familiar nonlinear relaxation labeling algorithm of Rosenfeld et al. (1976) is in fact intimately related with a well-established theory of constraint satisfaction developed by Hummel and Zucker (1983). In particular, it is shown that, when a certain symmetry condition is met, the algorithm possesses a Liapunov function which turns out to be (the negative of) a well-known consistency measure. this follows almost immediately from a powerful result of Baum and Eagon (1967) developed in the context of Markov chain theory. these properties are also shown to naturally generalize to higher-order relaxation schemes. Some applications of the results presented here are finally outlined.

关键词： Labeling pattern recognition Constraint theory Approximation algorithms Information resources Particle measurements Machine vision Uncertainty Heuristic algorithms Bayesian methods

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Shape Google: a computer vision approach to isometry invariant shape retrieval

Shape Google: a computer vision approach to isometry invaria...

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international conference on Computer Vision Workshops (ICCV Workshops)

作者： Maks Ovsjanikov Alexander M. Bronstein Michael M. Bronstein Leonidas J. Guibas ICME University of Stanford USA Department of Computer Science Technion-Israel Institute of Technology Israel Department of Computer Science University of Stanford USA

Feature-based methods have recently gained popularity in computer vision and pattern recognition communities, in applications such as object recognition and image retrieval. In this paper, we explore analogous approac... 详细信息

关键词： Shape Computer vision Image retrieval Robustness Computer science Information retrieval Image databases Spatial databases pattern recognition Object recognition

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Recognising groups of curves based on new affine mutual geometric invariants, with applications to recognizing intersecting roads in aerial images

Recognising groups of curves based on new affine mutual geom...

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international conference on pattern recognition

作者： M. Barzohar D. Keren D.B. Cooper RAFAEL Department Computer Vision Group Haifa Israel Department of Mathematics Haifa University Haifa Israel Laboratory for Engineering Brown University of Providence Providence RI USA

this paper treats some of the problem in recognizing the geometry of objects composed of groups of curves that undergo arbitrary affine transformations, providing the objects can be invariantly segmented into groups of curves representable by third degree implicit polynomials. As an illustrative example, the authors treat the problem of representing and recognizing portions of roads in aerial images. the approach the authors develop is to represent each road segment in a disc by a third degree implicit-polynomial, and recognition of road geometry is then Bayesian recognition of a vector of mutual algebraic invariants for the polynomials within such a disc. the mutual affine invariants developed and used are for pairs of polynomials. this is a new powerful approach to dealing with the recognition of complex curves.

关键词： Roads Polynomials Geometry Image recognition Bayesian methods Application software Rivers Computer vision Laboratories Image segmentation

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the Statistical Identification of Motion Anomalies in Ground Moving Target Radar Imagery

The Statistical Identification of Motion Anomalies in Ground...

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12th IEEE international conference on Technologies for Homeland Security (HST)

作者： Deaett, Michael A. White, Zachary T. Appl Radar Inc North Kingstown RI 02852 USA

ISBN: (纸本)9781467327091

the manual analysis of moving target indicator (MTI) imagery is a complex and time consuming process. therefore, automated methods of statistically identifying anomalous patterns within data in real time are important. applications include the evaluation of ground surveillance scenery to locate lost individuals in the presence of wind-driven debris, the identification of traffic patterns and foliage locations. Additional use can be made of automated techniques on board space probes that are evaluating atmospheric and terrain disturbances on distant planets. As the volume of data grows and as the time criticality of the data products becomes more urgent, the importance of this automated analysis grows. In our paper we apply multiscale vector quantization to ground-based range-Doppler radar imagery. the vector quantization codebook that results is used to segment the background into regions of statistically similar backscatter range and Doppler variation. Within these regions, smaller scale patterns are used to identify anomalies associated with non-naturally occurring objects. the time progression of the anomaly location is further used to enhance the feature set that is used to ascribe a class to each of the anomalies. We introduce the viewer to the Applied Radar radar testbed data collection system that produced the data used in this study using block diagram and processing descriptions. We then present the GMTI imagery that was used for our study. Within that data we highlight the identified anomalies and give the feature set associated with each. these anomalies are produced by individuals, vehicles and naturally present foliage. Preliminary classifier results are then presented including spectrogram and color mapped images of the data, region feature tables, process flow charts and image examples of the vector codebook members that result. this paper is of interest to researchers involved in analyzing any kind of multidimensional time-varying scenes.

关键词： vector quantization ground surveillance radar anomaly statistics pattern recognition

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Periodicity, directionality, and randomness: Wold features for perceptual pattern recognition

Periodicity, directionality, and randomness: Wold features f...

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international conference on pattern recognition

作者： Fang Liu R.W. Picard Media Laboratory Massachusetts Institute of Technology Cambridge MA USA

One of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems. In applications such as image retrieval, if is important that features used by the system in pattern comparison provide good measures of "perceptual similarity". the authors present a new set of features and an image model based on the three mutually orthogonal components produced by the 2-D Wold decomposition of random fields. these components have visual properties which approximate the three most important perceptual dimensions of human texture perception. the method presented here is different from the existing Wold-based models in that it tolerates certain local inhomogeneities which arise in natural textures and reduces computation for comparison of patterns subjected to transformations such as rotation. An image retrieval algorithm based on the new texture model is presented. the effectiveness of the new Wold features for retrieving perceptually similar natural textures is demonstrated by comparing it to that of other well-known pattern recognition methods. the Wold model appears to offer a perceptually more satisfying measure of pattern similarity.

关键词： pattern recognition Image retrieval Humans Information retrieval Image databases Principal component analysis Laboratories Appropriate technology Prototypes Two dimensional displays

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Real-time facial-feature tracking based on matching techniques and its applications

Real-time facial-feature tracking based on matching techniqu...

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international conference on pattern recognition

作者： H. Sako M. Whitehouse A. Smith A. Sutherland Research & Development Centre Dublin Laboratory (HDL) OE28099Reilly Institute Trinity College Hitachi Europe Limited Dublin Ireland

this paper describes a method of real-time facial-feature extraction which is based on matching techniques. the method is composed of facial-area extraction and mouth-area extraction using colour histogram matching, and eye-area extraction using template matching. By the combination of these methods, we can realize real-time processing, user-independent recognition and tolerance to changes of the environment. Also, this paper touches on neural networks which can extract characteristics for recognizing the shape of facial parts. the methods were implemented in an experimental image processing system, and we discuss the cases that the system is applied to man-machine interface using facial gesture and to sign language translation.

关键词： Shape Feature extraction Image recognition Histograms Neural networks Face recognition Data mining User interfaces Samarium Statistics

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A New Genetic-Based Hyper-Heuristic Algorithm for Clustering Problem 1

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12th international conference on Soft Computing and pattern recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Bonab, Mohammad Babrdel Bok-Min, Goi Nair, Madhavan a/l Balan Huat, Chua Kein Chwee, Wong Chim Center for Artificial Intelligence and Computing Applications Universiti Tunku Abdul Rahman Petaling Jaya Selangor Malaysia Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Petaling Jaya Malaysia

ISBN: (数字)9783030736897

ISBN: (纸本)9783030736880

Observations over recent studies indicate that most of the methods and algorithms used to deal with clustering problems are based on hybrid metaheuristic and metaheuristic algorithms to rectify the solutions. However, these approaches are restricted by the number of heuristics. Hyperheuristic algorithms are new generation of metaheuristic algorithm that use a collection of low-level search strategies and high-level heuristics which works in heuristic space and solution spaces, while metaheuristic algorithms just work in solution space to find better solutions. the main goal of this research is to propose a hyperheuristic framework for clustering problem which is able to optimize the difference of all data objects of one cluster from their respective cluster centres. the proposed hyperheuristic clustering algorithm has used from pool of meta-heuristic and heuristic approaches and mapping between solution space and heuristic spaces is one of the prevalent and powerful methods in the optimization domains. By mapping the solution spaces into heuristic space, it would be possible to make easy decision to settle data clustering problems. Our suggested hyperheuristic clustering works into three major spaces including high-level space, low-level space and problem space. the experiments of this study have proven that the suggested method has successfully generated efficient and robust clustering results. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clustering algorithms

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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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DistilDoc: Knowledge Distillation for Visually-Rich Document applications 18th

DistilDoc: Knowledge Distillation for Visually-Rich Document...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Van Landeghem, Jordy Maity, Subhajit Banerjee, Ayan Blaschko, Matthew Moens, Marie-Francine Llados, Josep Biswas, Sanket Katholieke Univ Leuven Leuven Belgium Contractfit Brussels Belgium Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9783031705458;9783031705465

this work explores knowledge distillation (KD) for visually-rich document (VRD) applications such as document layout analysis (DLA) and document image classification (DIC). While VRD research is dependent on increasingly sophisticated and cumbersome models, the field has neglected to study efficiency via model compression. Here, we design a KD experimentation methodology(dagger) for more lean, performant models on document understanding (DU) tasks that are integral within larger task pipelines. We carefully selected KD strategies (response-based, feature-based) for distilling knowledge to and from backbones with different architectures (ResNet, ViT, DiT) and capacities (base-small-tiny). We study what affects the teacher-student knowledge gap and find that some methods (tuned vanilla KD, MSE, SimKD with an apt projector) can consistently outperform supervised student training. Furthermore, we design downstream task setups to evaluate covariate shift and the robustness of distilled DLA models on zero-shot layout-aware document visual question answering (DocVQA). DLA-KD experiments result in a large mAP knowledge gap, which unpredictably translates to downstream robustness, accentuating the need to further explore how to efficiently obtain more semantic document layout awareness.(dagger Code available at: https://***/Jordy-VL/DistilDoc_ICDAR24)

关键词： Semantics

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