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Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization

Reducing the feature divergence of RGB and near-infrared ima...

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Siwei Yang Shaozuo Yu Bingchen Zhao Yin Wang Tongji University Shanghai China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Visual pattern recognition over agricultural areas is an important application of aerial image processing. In this paper, we consider the multi-modality nature of agricultural aerial images and show that naively combining different modalities together without taking the feature divergence into account can lead to sub-optimal results. Thus, we apply a Switchable Normalization block to our DeepLabV3+ segmentation model to alleviate the feature divergence. Using the popular symmetric Kullback-Leibler divergence measure, we show that our model can greatly reduce the divergence between RGB and near-infrared channels. Together with a hybrid loss function, our model achieves nearly 10% improvements in mean IoU over previously published baseline.

关键词： Task analysis Computational modeling pattern recognition Switches computer vision Agriculture Semantics

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A survey of plant recognition method based on image processing

A survey of plant recognition method based on image processi...

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International conference on Electrical and Control Engineering, ICECE 2010

作者： Zhang, Juan Huang, Xin-Yuan School of Information Science and Technology Beijing Forestry University Beijing China Department of Information and Engineering Jiangsu Maritime Institute Nanjing China

ISBN: (纸本)9780769540313

With the development of digital forestry, image processing.and pattern recognition technology have been extensively used in the study of forestry research. It is currently a hot research that automatic plant recognition by use of computer. In some developed countries, the technology has been used in the agricultural production. This paper overviews the study of automatic plant recognition by use of computer from plant leaves and flowers, and analyses the limitations exist in the present study. In the end, this paper looking forward to the technology of automatic plant recognition. © 2010 ieee.

关键词： image processing

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Rotated wedge averaging method for junction characterization

Rotated wedge averaging method for junction characterization

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conference on computer Vision and pattern recognition (CVPR)

作者： Weichuan Yu K. Daniilidia G. Sommer Institute of Computer Science Christian Albrecht University Kiel Germany

The computational cost of conventional filter methods for junction characterization is very high. This burden can be attenuated by using steerable filters. However, in order to achieve a high orientational selectivity to characterize complex junctions a large number of basis filters is necessary. From this results a yet too high computational effort for steerable filters. In this paper we present a new method for characterizing junctions which keeps the high orientational resolution and is computationally efficient. It is based on applying rotated copies of a wedge averaging filter and estimating the derivative with respect to the polar angle. The new method is compared with the steerable wedge filter method in experiments with real images. We show the superiority of our method as well as its adaptability to scale changes and robustness against noise.

关键词： Kernel Computational efficiency Gaussian processes computer science Noise robustness image processing Object recognition Tracking Motion analysis image coding

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Document image enhancement using directional wavelet

Document image enhancement using directional wavelet

引用

conference on computer Vision and pattern recognition (CVPR)

作者： Qian Wang Tao Xia Lida Li Chew Lim Tan Dept. of Comput. Sci. Nat. Univ. of Singapore Singapore Centre for Wavelets Approximation and Information Processing Department of Mathematics National University of Singapore Singapore

ISBN: (纸本)0769519008

This paper proposes an algorithm to clean up a large collection of historical handwritten documents kept up in the National Archives of Singapore. Due to the seepage of ink over long period of storage, the front page of each document has been severely marred by the reverse side writing. Earlier attempts have been made to match both sides of the page to identify the offending strokes originating from the back so as to eliminate them with the aid of a wavelet transform. Perfect matching, however, is difficult due to document skews, differing resolutions, inadvertently missing out reverse side and warped pages during image capture. An approach is now proposed to do away with double side mapping by using a directional wavelet transform that is able to distinguish the foreground and reverse side strokes much better than the conventional wavelet transform. Experiments have shown that the method indeed enhances the readability of each document significantly over after the directional wavelet operation without the need for mapping with its reverse side.

关键词： image enhancement Wavelet transforms Ink Writing image resolution computer science Information processing Mathematics Approximation algorithms Background noise

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Iterative data adaptive anisotropic image filtering

Iterative data adaptive anisotropic image filtering

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2012 International conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Yang, Chang-Cai Zheng, Xin-Yi Tian, Jin-Wen Shang, Ke Tian, Xin Hao, Wei Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-Spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan 430074 China Electrical Engineering and Renewable Energy School China Three Gorges University Yichang 443002 China Institute of Intelligent Vision and Image Information College of Computer and Information Technology China Three Gorges University Yichang 443002 China

ISBN: (纸本)9781467314855

This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency. © 2012 ieee.

关键词： Iterative methods

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Mri Segmentation Using Supervised And Unsupervised Methods

Mri Segmentation Using Supervised And Unsupervised Methods

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Annual International conference of the ieee Engineering in Medicine and Biology society (EMBC)

作者： A.M. Bensaid L.O. Hall L.P. Clarke R.P. Velthuizen Department of Computer Science & Engineering University of South Florida Tampa FL USA

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3D deformable image matching using multiscale minimization of global energy functions

3D deformable image matching using multiscale minimization o...

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conference on computer Vision and pattern recognition (CVPR)

作者： O. Musse F. Heitz J.-P. Armspach Institut de Physique Biologique CNRS UPRES-A 7004 Strasbourg I University Strasbourg France

This paper presents a hierarchical framework to perform deformable matching of three dimensional (3D) images. 3D shape deformations are parameterized at different scales, using a decomposition of the continuous deformation vector field over a sequence of nested subspaces, generated from a single scaling function. The parameterization of the field enables to enforce smoothness and differentiability constraints without performing explicit regularization. A global energy function, depending on the reference image and the transformed one, is minimized via a coarse-to-fine algorithm over this multiscale decomposition. Contrary to standard multigrid approaches, no reduction of image data is applied. The continuous field of deformation is always sampled at the same resolution, ensuring that the same energy function is handled at each scale and that the energy decreases at each step of the minimization.

关键词： image matching image registration Biomedical imaging Shape Biology Energy resolution Yield estimation image processing Motion estimation Layout

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Automated design of Bayesian perceptual inference networks

Automated design of Bayesian perceptual inference networks

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Sarkar Boyer Department of Computer Science and Engg. University of South Florida Tampa FL USA SAMP-Laboratory Department of Electrical Engineering Ohio State Uinversity Columbus OH USA

We previously presented (Sarkar and Boyer, 1993) the Perceptual Inference Network (PIN), a formalism based on Bayesian Networks, to reason among a set of object or feature hypotheses and to integrate multiple sources of information in the context of perceptual organization. The design of a PIN requires knowledge of the dependency structure among the organizations of interest and the specification of the conditional probabilities. This design was done manually with large doses of tedium and guesswork. In this paper we present an algorithm based on structural entropic measures and random parametric structural descriptions (RPSDs) to design a PIN automatically and in a (more) theoretically sound fashion. Experimental results present evidence of the robustness of the algorithm and make performance comparisons on real image data with a manually structured PIN. Since PINs are a form of Bayesian Network, we hope that this work will also prove useful towards structuring Bayesian Networks in other computer vision contexts.< >

关键词： Bayes procedures Inference mechanisms Probability image processing

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Inferring segmented surface description from stereo data

Inferring segmented surface description from stereo data

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conference on computer Vision and pattern recognition (CVPR)

作者： Mi-Suen Lee G. Medioni University of Southern California Los Angeles CA USA

We present an integrated approach to the derivation of scene description from binocular stereo images. By inferring the scene description directly from local measurements of both point and line correspondences, we address both the stereo correspondence problem and the surface reconstruction problem simultaneously. We introduce a robust computational technique called tensor voting for the inference of scene description in terms of surfaces, junctions, and region boundaries. The methodology is grounded in two elements: tensor calculus for representation, and non-linear voting for data communication. By efficiently and effectively collecting and analyzing neighborhood information, we are able to handle the tasks of interpolation, discontinuity detection, and outlier identification simultaneously. The proposed method is non-iterative, robust to initialization and thresholding in the preprocessing.stage, and the only critical free parameter is the size of the neighborhood. We illustrate the approach with results on a variety of images.

关键词： Surface reconstruction Layout Robustness Tensile stress Voting image segmentation Stereo image processing image reconstruction Calculus Data communication

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Counting Crowded Moving Objects

Counting Crowded Moving Objects

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conference on computer Vision and pattern recognition (CVPR)

作者： V. Rabaud S. Belongie Department of Computer Science and Engineering University of California San Diego USA

In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.

关键词： computer vision Motion segmentation Motion analysis Karhunen-Loeve transforms Trajectory image motion analysis Optical computing computer science Humans Animals

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