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International Conference on pattern recognition

作者： Keuper, Margret Schmidt, Thorsten Padeken, Jan Heun, Patrick Palme, Klaus Burkhardt, Hans Ronneberger, Olaf Pattern Recognition and Image Processing Computer Science Department Freiburg University Germany Max-Planck Institute of Immunobiology Freiburg Germany University of Freiburg Germany Inst. of Biology II Freiburg Inst. for Advanced Studies - FRIAS University of Freiburg Germany

ISBN: (纸本)9780769541099

When using deformable models for the segmentation of biological data, the choice of the best weighting parameters for the internal and external forces is crucial. Especially when dealing with 3D fluorescence microscopic data and cells within dense tissue, object boundaries are sometimes not visible. In these cases, one weighting parameter set for the whole contour is not desirable. We are presenting a method for the dynamic adjustment of the weighting parameters, that is only depending on the underlying data and does not need any prior information. The method is especially apt to handle blurred, noisy, and deficient data, as it is often the case in biological microscopy. © 2010 IEEE.

关键词： Deformation

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A Modified Numerical Integration Method for Deformable Object Animation

A Modified Numerical Integration Method for Deformable Objec...

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Asia Simulation Conference 2007, AsiaSim 2007

作者： Hu, Xinrong Wei, Lan Li, Dehua Dept. of Computer Science and Technology WuHan University of Science and Engineering WuHan 430074 China Institute for Pattern Recognition and Artificial Intelligence State Education Commission Laboratory for Image Processing and Intelligence Control HuaZhong Univ. of Science and Technology WuHan 430074 China

ISBN: (纸本)9783540775997

A satisfied deformable object simulation should be general, accurate, efficient and stable. Explicit, implicit and semi-implicit numerical integration methods have contributed to large performance enhancements in the field of deformable simulation. Cloth is the most representative deformable object. In this paper, we propose an improved embedded Runge-Kutta integration method to solve the deformable simulation that based on classical spring-mass model. Traditional embedded Runge-Kutta methods generally solve ordinary differential equations of deformable object simulation. Most of them tend to concentrate on the efficiency of the simulation process, and not the fidelity of the simulation result. We investigate and determine the extent to which the overall quality must be compromised in order for the stable conditions to be satisfied. The improved Runge-Kutta method proposed in our paper incorporates the fixed time step and adaptive time step in solving cloth motion equations to achieve a controllable error evaluation. Compared with the other Runge-Kutta methods, the proposed method has some advantages for cloth simulation: controllable error evaluation without extra computations, excellent efficiency, good stability and satisfied precision. Experiment demonstrates that this method results in simulation efficiency improvements and is considerable practicable. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Animation

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Exponential image Enhancement in Daytime Fog Conditions

Exponential Image Enhancement in Daytime Fog Conditions

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17th International IEEE Conference on Intelligent Transportation Systems & The Asia-Pacific Council on Systems Engineering Conference 2014

作者： Mihai Negru Sergiu Nedevschi Radu Ioan Peter Image Processing and Pattern Recognition Group Computer Science DepartmentTechnical University of Cluj-Napoca head of the Image Processing and Pattern Recognition Group Computer Science DepartmentTechnical University of Cluj-Napoca Mathematics Department Technical University of Cluj-Napoca

The images captured in fog conditions have degraded contrast,that makes current image processing applications sensitive and error *** propose in this paper an efficient single image enhancement algorithm suitable for daytime fog conditions and based on an original mathematical model,for computing the atmospheric veil,that takes into account the variation in fog density to the *** model is inspired by the functions that appear in partition of unity in the differential geometry *** observing images captured in fog conditions,usually the fog has a very low density in front of the camera and this density has a non-linear increase with the distance,such that objects are no longer visible at greater *** using our mathematical model we are able to obtain superior reconstructions of the original fog-free image,when comparing to traditional *** advantage of our method is the ability to adapt the model in accordance to the density of the fog.A quantitative and qualitative evaluation is performed on both synthetic and real camera *** evaluation proves that our mathematical model is more suitable for image enhancement in both homogeneous and heterogeneous fog *** algorithm is able to perform image enhancement in real time for both color and gray scale images.

关键词： image Exponential image Enhancement in Daytime Fog Conditions

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Learning from partially labeled data 28

Learning from partially labeled data

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28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020

作者： Mehrkanoon, Siamak Huang, Xiaolin Suykens, Johan A.K. Department of Data Science and Knowledge Engineering Maastricht University Netherlands Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Department of Electrical Engineering ESAT-STADIUS KU Leuven LeuvenB-3001 Belgium

ISBN: (纸本)9782875870742

Providing sufficient labeled training data in many application domains is a laborious and costly task. Designing models that can learn from partially labeled data, or leveraging labeled data in one domain and unlabeled data in a different but related domain is of great interest in many applications. In particular, in this context one can refer to semi-supervised modelling, transfer learning, domain adaptation and multi-view learning among oth- ers. There are several possibilities for designing such models ranging from shallow to deep models. These type of models have received increasing in- terest due to their successful applications in real-life problems. This paper provides a brief overview of recent techniques in learning from partially labeled data. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Neural networks

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Multi-Class Segmentation for Traffic Scenarios at Over 50 FPS

Multi-Class Segmentation for Traffic Scenarios at Over 50 FP...

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IEEE Intelligent Vehicles Symposium

作者： Arthur D. Costea Sergiu Nedevschi Image Processing and Pattern Recognition Group Computer Science Department Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781479936397

Multi-class segmentation assigns a class label to each pixel in an image. It represents a significant task for the semantic understanding of images and has received plentiful attention over the last years. The current state of art is dominated by conditional random field based approaches, defined over pixels or image segments. However, high accuracy segmentation comes at a high computational cost. The best performing methods can barely run at few frames per second and are far from real-time applications. Our goal is to bridge the gap between current state of the art segmentation approaches and real-time applications. In this paper we propose an efficient approach for individual pixel classification. Multiple local descriptors are computed densely and then quantized using visual codebooks. Joint boosting is used to classify each pixel based on the quantized local descriptors. We show that using careful design choices and GPU optimization we can achieve sate of the art segmentation results at over 50 FPS. We also propose a Conditional Random Field (CRF) model defined over superpixels that uses the proposed pixel classifier for the estimation of unary potentials. The CRF based multi-class segmentation can run at over 30 FPS. The proposed approach is validated on the MSRC21 and CamVid multi-class segmentation benchmarks, the former one consisting of urban traffic sequences.

关键词： choices GPU CRF

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Moving rigid objects segmentation in 3D dynamic traffic scenes using a stereovision system

Moving rigid objects segmentation in 3D dynamic traffic scen...

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IEEE International Conference on Intelligent computer Communication and processing (ICCP)

作者： Catalin Golban Sergiu Nedevschi Computer Science Department Technical University Image Processing and Pattern Recognition Group Cluj-Napoca Romania

This paper proposes a novel method for detecting the moving vehicles in dynamic urban traffic scenes using a stereo camera. Relying on the fact that a set of feature points on a rigid 3D scene object are staying in a rigid 3D configuration, we propose to compute the relative motion between the camera and a moving object with an algorithm that follows from the visual odometry based motion estimation methods. Subtracting the camera motion we obtain the absolute object motion. Additionally we create a compact representation of the scene using superpixels computed from intensity and depth information. A graph-like structure is built, having superpixels as nodes and indicating neighboring relationships between adjacent superpixels. Objects are segmented using a fast region growing algorithm that considers as seeds the features used to compute the object motion.

关键词： Three-dimensional displays Visualization computer vision Cameras Feature extraction Stereo vision image motion analysis

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Line-based cascade labeling algorithm for hyper-scale issue

Line-based cascade labeling algorithm for hyper-scale issue

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2nd International Conference on Multimedia Technology, ICMT 2011

作者： Zhao, Hualong Sang, Hongshi Zhang, Tianxu Fan, Yebin National Key Laboratory of Science and Technology on Multi-spectral Information Processing Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Department of Computer Science Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781612847740

A fast and efficient algorithm is presented to label the connected components for binary image, especially for very huge images or any image larger than the available memory. The cascading style scheme compresses the algorithm's workspace to only 2 rows of image, which makes it possible to process any scale image with very low memory requirement. Two techniques are also applied for improving the performance further: the search path compression;the image data prefetching. An extensive comparison with the state-of-art algorithms is proposed, both on random and real datasets. Our algorithm shows an impressive speedup as much as 10X, while the total memory cost is significantly reduced. © 2011 IEEE.

关键词： Binary images

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GEMI: A high performance and high flexibility memory interface architecture for complex embedded SOC

GEMI: A high performance and high flexibility memory interfa...

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International Conference on computer science and Software Engineering, CSSE 2008

作者： Hualong, Zhao Hongshi, Sang Tianxu, Zhang Yebin, Fan State Key Lab. for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Department of Computer Science Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769533360

Two of the most significant factors in the success of today's system-on-chip (SoC) designs are the ability to deliver efficient access to off-chip high speed memory and the ability to be compatible with several different interface-timings of several different sorts of memories. And the scheduling scheme of data streams for multi-master and multi-outstanding requests situation in complex SoC should be considered carefully, too. This paper introduces a novel general external memory interface (GEMI) architecture for the high performance and high flexibility memory interface. Several techniques and methods, i.e. data and command buffers based on asynchronous FIFO, optimized bank management, multi-port access scheduler, request reorder unit, are proposed in this paper for fulfilling the requirements mentioned above. Some techniques are specially proposed for data streams scheduling in complex SoC. At the end of the paper, the simulation and tape-out results are provided. The whole implementation of architecture is proven to be not only functional and efficient, but also flexible and reusable by simulation and silicon verification. © 2008 IEEE.

关键词： Scheduling

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MLPP: A modified version of locality preserving projection

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Journal of Computational Information Systems 2008年第5期4卷 1891-1896页

作者： Zheng, Zhonglong Zhang, Haoran Yang, Fa Yang, Jie Department of Computer Science Zhejiang Normal University Jinhua 321004 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China

Locality Preserving Projection (LPP), as a linear manifold learning algorithm, has attracted much interests in recent years. LPP considers an n1× n2image as a vector in €n1×n2space, and thus is limited by the curse of dimensionality. To deal with such problem, Principal Component Analysis (PCA) is traditionally used for dimensionality reduction first. However, some discriminant information would be discarded by PCA. In this paper, a modified version of LPP (MLPP) is proposed which can be applied directly in the original face space without the aid of PCA. Moreover, the mapping matrix derived from MLPP is orthogonal in contrast to that of LPP. Experiments show competitive performance of our method. © 2008 Binary Information Press.

关键词： Principal component analysis

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Grouping edge points into line segments by sequential Hough transformation

Grouping edge points into line segments by sequential Hough ...

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International Conference on pattern recognition

作者： Xiaofeng Zhang H. Burkhardt Chair of Pattern Recognition and Image Processing Department of Computer Science University of Freiburg Freiburg im Breisgau Germany

ISBN: (纸本)0769507506

An algorithm to group edge points into digital line segments with Hough transformation is described. The edge points are mapped onto the parameter domain discretized at specific intervals, on which peaks appear to represent different line segments. By modeling each peak as a Gaussian function in the parameter domain, a region to which the edge points are supposed to be mapped is determined. Then the edge points are grouped and the parameters for a line segment are computed. For the edges including multiple line segments, a sequential Hough transformation for detecting peaks one by one in the parameter domain is implemented, and the points from the region around each peak are grouped, thus the line segments are described. Experiments show the robustness of the algorithm implemented on both the generated edges disturbed by different noise levels and real images taken from an indoor environment.

关键词： image segmentation image edge detection Indoor environments Face detection pattern recognition image processing computer science Noise robustness Noise level Noise measurement

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