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第35届中国控制会议

作者： Kunqi Gu Mingna Liu Tao Zhou Fanghui Liu Xiangjian He Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai Institute of Spaceflight Control Technology Infrared Detection Technology Research and Development Center China Aerospace Science and Technology Corporation School of Computing and Communications University of Technology The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

In this paper,the Locality-constrained Linear Coding(LLC) algorithm is incorporated into the object tracking ***,we extract local patches within a candidate and then utilize the LLC algorithm to encode these *** on these codes,we exploit pyramid max pooling strategy to generate a richer feature *** feature histogram which integrates holistic and part-based features can be more discriminative and ***,an occlusion handling strategy is utilized to make our tracker more ***,an efficient graph-based manifold ranking algorithm is exploited to capture the relevance between target templates and *** tracking,target templates are taken as labeled nodes while target candidates are taken as unlabeled nodes,and the goal of tracking is to search for the candidate that is the most relevant to existing labeled nodes by manifold ranking *** on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to other state-of-the-art baselines.

关键词： object tracking LLC pyramid max-pooling local appearance model manifold ranking

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Modeling and tracking of dynamic obstacles for logistic plants using omnidirectional stereo vision

Modeling and tracking of dynamic obstacles for logistic plan...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： A. Vatavu A. D. Costea S. Nedevschi Computer Science Department Technical University of Cluj-Napoca Image Processing and Pattern Recognition Research Center Romania

In this work we present an obstacle detection and tracking solution applied to Automated Guided Vehicles (AGVs) in industrial environments. The proposed method relies on information provided by an omnidirectional stereo vision system enabling 360 degree perception around the AGV. The stereo data is transformed into a classified digital elevation map (DEM). Based on this intermediate representation we are able to generate a set of obstacle hypotheses, each represented by a 3D cuboid and a free-form polygonal model. The cuboidal model is used for the classification of each hypothesis as "Pedestrian", "AGV", "Large Obstacle" or "Small Obstacle", while the free-form polylines are used for object motion estimation relying on an Iterative Closest Point (ICP) method. The obtained measurements are subjected to a Kalman filter based tracking approach, in which the data association takes into account also the classification results.

关键词： Three-dimensional displays Logistics Stereo vision Cameras Tracking Computational modeling Visualization

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Fast Pedestrian Detection for Mobile Devices

Fast Pedestrian Detection for Mobile Devices

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International IEEE Conference on Intelligent Transportation Systems

作者： Arthur Daniel Costea Andreea Valeria Vesa Sergiu Nedevschi Image Processing and Pattern Recognition Research Center Computer Science Department Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781467365970

In this paper we present a fast and robust solution for pedestrian detection that can run in real time conditions even on mobile devices with limited computational power. An optimization of the channel features based multiscale detection schemes is proposed by using 8 detection models for each half octave scales. The image features have to be computed only once each half octave and there is no need for feature approximation. We use multiscale square features for training the multiresolution pedestrian classifiers. The proposed solution achieves state of art detection results on Caltech pedestrian benchmark at over 100 FPS using a CPU implementation, being the fastest detection approach on the benchmark. The solution is fast enough to perform under real time conditions on mobile platforms, yet preserving its robustness. The full detection process can run at over 20 FPS on a quad-core ARM CPU based smartphone or tablet, being a suitable solution for limited computational power mobile devices or embedded platforms.

关键词： pedestrian detection image features solution image feature Mobile devices Pedestrians Solutions Fasting Robust solution FES gene Smartphone Solution computational power

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Automated multi-contrast brain pathological area extraction from 2D MR images

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Journal of Applied research and Technology 2015年第1期13卷 58-69页

作者： Dvořák, P. Bartušek, K. Kropatsch, W.G. Smékal, Z. Institute of Scientific Instruments of the ASCR V.v.i Academy if Sciences of the Czech Republic Brno Czech Republic Dept. of Telecommunications Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Faculty of Informatics Vienna University of Technology Vienna Austria

The aim of this work is to propose the fully automated pathological area extraction from multi-parametric 2D MR images of brain. The proposed method is based on multi-resolution symmetry analysis and automatic thresholding. The proposed algorithm first detects the presence of pathology and then starts its extraction. T2 images are used for the presence detection and the multi-contrast MRI is used for the extraction, concretely T2 and FLAIR images. The extraction is based on thresholding, where Otsu's algorithm is used for the automatic determination of the threshold. Since the method is based on symmetry, it works for both axial and coronal planes. In both these planes of healthy brain, the approximate left-right symmetry exists and it is used as the prior knowledge for searching the approximate pathology location. It is assumed that this area is not located symmetrically in both hemispheres, which is met in most cases. The detection algorithm was tested on 203 T2-weighted images and reached the true positive rate of 87.52% and true negative rate of 93.14%. The extraction algorithm was tested on 357 axial and 443 coronal real images from publicly available BRATS databases containing 3D volumes brain tumor patients. The results were evaluated by Dice Coefficient (axial: 0.85 ± 0.11, coronal 0.82 ± 0.18) and by Accuracy (axial: 0.96 ± 0.05, coronal 0.94 ± 0.09). © 2015 Universidad Nacional Autónoma de México.

关键词： Brain Pathology Brain Tumor MRI Multi-contrast MRI Symmetry Analysis

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Online learning of multi-feature weights for robust object tracking

Online learning of multi-feature weights for robust object t...

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IEEE International Conference on image processing

作者： Tao Zhou Harish Bhaskar Kai Xie Jie Yang Xiangjian He Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Dept. of Elec. & Comp. Engg. Khalifa Univ. of Science Technology and Research Abu Dhabi U.A.E Faculty of Engineering and Information Technology University of Technology Sydney Australia

Sparse Representation based Classification (SRC) and its potential in object tracking have been explored in recent years. However, the trade-off between the discriminative ability of the overly emphasized sparse representation and the lack of insight on correlation of visual information has raised questions over the general applicability of such methods in object tracking. In addition, the need for the optimization of a series of l 1 -regularized least square norm, increases the computational complexity thereby limiting their usage in real-time applications. In this paper, a novel approach to robust object tracking is proposed. First, the variations in the appearance of the tracked target is modelled using PCA basis vectors, and further, a l 2 -regularized least square method is used to solve the proposed representation model. In order to improve the robustness of feature representation in object tracking applications, weights are associated with multiple trackers; each formulated using a different feature, and adapted via an online learning scheme. Finally, a decision fusion criterion is imposed to generate an optimized output through the weighted combination of different tracking results. Experiments on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to thirteen other state-of-the-art baselines.

关键词： Target tracking Robustness Object tracking Lighting Clutter Computed tomography Visualization

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Posterior distribution learning (PDL): A novel supervised learning framework

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8834卷 86-94页

作者： Tu, Enmei Yang, Jie Jia, Zhenghong Kasabov, Nicola Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China School of Information Science and Engineering Xinjiang University Urumqi830046 China The Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand

In order to obtain a robust supervised model with good generalization ability, traditional supervised learning method has to be trained with sufficient well labeled and uniformly distributed samples. However, in many real applications, the cost of labeled samples is generally very expensive. How to make use of ample easily available unlabeled samples to remedy the insufficiency of labeled samples to train a supervised model is of great interest and practical significance. In this paper we propose a new supervised learning framework, Posterior Distribution Learning (PDL), which could train a robust supervised model with very a few labeled samples by including those unlabeled samples into training stage. Experimental results on both synthetic and real world data sets are presented to demonstrate the effectiveness of the proposed framework. © Springer International Publishing Switzerland 2014.

关键词： Supervised learning

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Pedestrian Detection in Infrared images Using HOG,LBP,Gradient Magnitude and Intensity Feature Channels

Pedestrian Detection in Infrared Images Using HOG,LBP,Gradie...

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

作者： Raluca Brehar Sergiu Nedevschi the Image Processing and Pattern Recognition Group Computer Science DepartmentTechnical University of Cluj-Napoca

Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible.A popular and successful approach for monocular intensity pedestrian detection is based on the approximation（instead of computation） of image features for multiple scales based on the features computed on set of predefined *** port this idea to the infrared *** contributions reside in the combination of four channel features,namely infrared,histogram of gradient orientations,normalized gradient magnitude and local binary patterns with the objective of detecting pedestrians for night vision applications dealing with far infrared *** scale feature computation is done by feature *** contribution is the study of different formulations for Local Binary patterns like uniform patterns and rotation invariant patterns and their effect on detection *** detection speed is also boosted by the aid of a fast morphological based region of interest *** vary the number of approximated scales per octave and study the impact on execution time and accuracy.A reasonable result hits a speed of 18 fps with a log average miss rate of 39%.

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Word Channel Based Multiscale Pedestrian Detection Without image Resizing and Using Only One Classifier

Word Channel Based Multiscale Pedestrian Detection Without I...

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IEEE Conference on Computer Vision and pattern recognition

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

ISBN: (纸本)9781479951192

Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for real-time applications are based on histograms of gradient orientations. Usually multiscale detection is attained by resizing the image several times and by recomputing the image features or using multiple classifiers for different scales. In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale. We go beyond the low level pixel-wise gradient orientation bins and use higher level visual words organized into Word Channels. Boosting is used to learn classification features from the integral Word Channels. The proposed approach is evaluated on multiple datasets and achieves outstanding results on the INRIA and Caltech-USA benchmarks. By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.

关键词： Visualization Boosting Training Feature extraction Graphics processing units Semantics image color analysis

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Unsupervised detection of different SAR images based on improved NSCT domain image fusion algorithm

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Guangdianzi Jiguang/Journal of Optoelectronics Laser 2015年第10期26卷 2023-2030页

作者： Zhang, Yi-Chen Jia, Zhen-Hong Qin, Xi-Zhong Yang, Jie Kasabov, Nikola College of Information Science and Engineering Xinjiang University Urumqi China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Aukland New Zealand

In order to reduce the total errors and obtain a better result in change detection with different SAR images, an unsupervised change detection algorithm is proposed in this paper. The technique is based on nonsubsampled contourlet transform (NSCT) domain with an improved fusion algorithm and fuzzy local information C-means clustering (FLICMC). First of all, mean-ratio operation and log-ratio operation are adopted to obtain the difference image, by using two images acquired in the same geographical area at different moments. Secondly, the two difference images are transformed into NSCT domain. The low frequency coefficients of two images of difference images respectively are fused by the rule of the maximum gradient quadratic sum;the high frequency coefficients of two images of difference images respectively are fused by the rule of the weighted fusion. And the final difference image can be got by using NSCT inverse transform. Finally, on the basis of FLICMC algorithm, the final difference image is divided into change area and unchanged area. According to the research between the real remote sensing data set and the simulated remote sensing data set, the experimental results show that the proposed algorithm can effectively suppress speckle noise impact on the detection results, and is not limited by the statistical distribution of change class and unchanged class. It has great applicability and doesn't require prior knowledge. So the proposed algorithm can get better detection effect. Compared with DWT2-FLICMC and MRF-FCMC, the proposed algorithm can obtain higher detection accuracy and shorter running time. ©, 2015, Board of Optronics Lasers. All right reserved.

关键词： Synthetic aperture radar

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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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