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检索条件"机构=Vienna University of Technology Pattern Recognition and Image Processing"

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Repetitive shapes detection using affine invariant constraints

Repetitive shapes detection using affine invariant constrain...

引用

2014 International Conference on Communication technology and Application, CTA 2014

作者： Yang, Z.H. Dong, P. Institute of Image Processing and Pattern Recognition North China University of Technology Beijing China

ISBN: (纸本)9781845649302

This paper presents a novel approach for finding more accurate feature pairs which is not only invariant to affine transformation, but also deals with images with repetitive shapes. First, the more accurate and robust homographic transformation between different views could be calculated through bi-directional matching and appropriate selection of threshold. Second, an affine geometric property, ratios of areas of corresponding triangles are affine invariant, is used to obtain more feature pairs based on the first step. Experiments demonstrate that the proposed method outperforms the state-of-art ASIFT in the number of correct feature pairs and matching accuracy among images with repetitive patterns. © 2014 WIT Press.

关键词： Affine invariance ASIFT Feature points image matching Repetitive shapes

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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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Visual tracking based on weighted subspace reconstruction error

Visual tracking based on weighted subspace reconstruction er...

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作者： Zhou, Tao Zhang, Junhao Xie, Kai Yang, Jie He, Xiangjian Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9781479957514

It is a challenging task to develop an effective and robust visual tracking method due to factors such as pose variation, illumination change, occlusion, and motion blur. In this paper, a novel tracking algorithm based on weighted subspace reconstruction error is proposed. We first compute the discriminative weights by sparse construction error with template dictionary consisted of positive and negative samples, and then confidence map for candidates is computed through subspace reconstruction error. Finally, the location of the target object is estimated by maximizing the decision map which is combined discriminative weights and subspace reconstruction error. Furthermore, we use the new evaluation criterion to verify the robustness of the current tracking result, which can reduce the accumulated error effectively. Experimental results on some challenging video sequences show that the proposed algorithm performs favorably against seven state-of-the-art methods in terms of accuracy and robustness. © 2014 IEEE.

关键词： Errors

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Video-based tracking and quantified assessment of spontaneous limb movements in neonates

Video-based tracking and quantified assessment of spontaneou...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Long Xu Irene Yu-Hua Gu Anders Flisberg Magnus Thordstein Inst. of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Dept. of Signals and Systems Chalmers University of Technology Gotheburg Sweden Dept. of Pediatrics Sahlgrenska University Hospital Gothenburg Sweden Dept. of Clinical Neurophysiology Sahlgrenska University Hospital Gothenburg Sweden

ISBN: (纸本)9781467383264

Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limb movements. Detection and quantification of these movements in infants from videos are hence desirable for providing useful information to clinicians. This could lead to computer-aided diagnosis of dysfunctions where early treatment may improve infant development. In this paper, we propose a scheme for detecting and quantifying qualitative aspects of limb movement through multiple tracking and state space motion modeling on videos. The main novelties of the paper include: (a) An enhanced detection method for effectively detection small weak marker points from video; (b) Bayesian estimation and nearest neighbor searching for selecting new observation in individual tracker and for tracking marker trajectories on limbs; (c) A criterion for anomaly detection based on the frequency and duration of abrupt changes in limb movement, using window averaged prominent residual powers. The proposed method has been tested on videos of neonates, results show that the proposed method is promising for tracking and quantifying the movement of neonate limbs for helping medical diagnostics.

关键词： Pediatrics Videos Trajectory Tracking Estimation Feature extraction Clutter

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Accurate Computation of Fingerprint Intrinsic images with PDE-Based Regularization

Accurate Computation of Fingerprint Intrinsic Images with PD...

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7th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2012

作者： Li, Mingyan Chen, Xiaoguang College of Sciences Beijing Forestry University 100083 Beijing China Institute of Image Processing and Pattern Recognition College of Sciences North China University of Technology 100083 Beijing China

The intrinsic images of fingerprint, such as orientation field and frequency map, represent the particular and basic characteristics of fingerprint ridge/valley patterns, and play a key role in feature extraction and matching of the fingerprint recognition system. In this paper, a novel algorithm is presented for accurate computation of fingerprint intrinsic images with PDE-based regularization technique. First, the coarse orientation field is estimated using general gradient-based method, and the frequency map is estimated by Fourier spectrum analysis of the projected curve of local window. Then, to measure the reliability of the intrinsic images, the quality map is computed based on the gray-scale intensity and local structural information. Finally, accurate orientation field and frequency map are reconstructed with PDE-based regularization of nonlinear diffusion filtering which is controlled by the quality-based diffusivity. Experimental results illustrated the efficiency of the proposed approach. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Spectrum analysis

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Adaptive multi-level region merging for salient object detection 25

Adaptive multi-level region merging for salient object detec...

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25th British Machine Vision Conference, BMVC 2014

作者： Fu, Keren Gong, Chen Yun, Yixiao Li, Yijun Gu, Irene Yu-Hua Yang, Jie Yu, Jingyi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden University of Delaware Newark United States

Most existing salient object detection algorithms face the problem of either under-or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph-based merging scheme is developed to reassemble regions based on their shared contour strength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance. © 2014. The copyright of this document resides with its authors.

关键词： Merging

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Repetitive shapes detection using affine invariant constraints

Repetitive shapes detection using affine invariant constrain...

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2014 International Conference on Communication technology and Application(CTA2014)

作者： Z.H.Yang P.Dong Institute of Image Processing & Pattern Recognition North China University of Technology

ISBN: (纸本)9781845649302

关键词： image matching repetitive shapes feature points affine invariance ASIFT

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Human activity recognition in images using SVMs and geodesics on smooth manifolds 14

Human activity recognition in images using SVMs and geodesic...

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8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014

作者： Yun, Yixiao Fu, Keren Gu, Irene Yu-Hua Aghajan, Hamid Yang, Jie Dept. of Signals and Systems Chalmers University of Technology Sweden Inst. of Image Processing and Pattern Recognition Shanghai Jiao Tong University China IMinds Ghent University Belgium AIR Lab. Stanford University United States

ISBN: (纸本)9781450329255

This paper addresses the problem of human activity recognition in still images. We propose a novel method that focuses on human-object interaction for feature representation of activities on Riemannian manifolds, and exploits underlying Riemannian geometry for classification. The main contributions of the paper include: (a) represent human activity by appearance features from local patches centered at hands containing interacting objects, and by structural features formed from the detected human skeleton containing the head, torso axis and hands;(b) formulate SVM kernel function based on geodesics on Riemannian manifolds under the log-Euclidean metric;(c) apply multi-class SVM classifier on the manifold under the one-against-all strategy. Experiments were conducted on a dataset containing 17196 images in 12 classes of activities from 4 subjects. Test results, evaluations, and comparisons with state-of-the-art methods provide support to the effectiveness of the proposed scheme. Copyright 2014 ACM.

关键词： Support vector machines

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Topological image Analysis and (Normalised) Representations for Plant Phenotyping

Topological Image Analysis and (Normalised) Representations ...

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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Ines Janusch Walter G. Kropatsch Wolfgang Busch Pattern Recognition and Image Processing Group Vienna University of Technology Vienna Austria Gregor Mendel Institute of Molecular Plant Biology Austrian Academy of Sciences Vienna Austria

This paper discusses the use of topological image analysis to derive characteristics needed in plant phenotyping. Due to certain features of root systems (deformation over time, overlaps of branches in a 2D image of the root system) a topological analysis is needed to correctly derive these characteristics. The advantages of such a topological analysis are highlighted in this paper and root phenotyping is presented as a new application for computational topology. Characteristics used in plant phenotyping that can be derived from root images using methods of topological image analysis are further presented. A Reeb graph based representation of root images is shown as an example for such a topological analysis. Based on a graph representation a new, normalised representation of root images is introduced.

关键词： image segmentation Level measurement Topology Length measurement Skeleton image analysis Soil

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A novel level set method for remote sensing image based on K-means

A novel level set method for remote sensing image based on K...

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2014 International Conference on Applied Mechanics, Mechatronics and Intelligent System, AMMIS 2014

作者： Xu, Er Jing Jia, Zhen Hong Wang, Lie Jun Hu, Ying Jie Yang, Jie College of Information science and engineering Xinjiang University Urumchi 830046 China Knowledge Engineering and Research Discovery Institute Auckland University of Technology Auckland New Zealand The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9783038351757

Due to the characteristic of remote sensing image, we propose a novel method based on K-means algorithm also with the improved multi-phrase level set model. Comparing with the classical multi-phase C-V model, the improved model considers the region area information, gradient information and edge detection. Proper use of gradient information can overcome the inaccurate edge localization defects in image segmentation. The edge detection is used for keeping the boundary information better in the evolution process. For the reason of picking up the contour's convergent speed and enable the avoidance of trap. Four stages are constructed. Firstly, a median filtering is applied to smooth the original image and reduce parts of noise. Secondly, the usage of K-means algorithm gains more obvious differences of characteristics. Next, the reconstruction of gradient is obtained by using Sobel operator. Finally, segmentation result is achieved by using an improved method of multi-phase level set image segmentation. Experimental results show that the proposed approach has advantages in rapid and efficient application of remote sensing image segmentation. © (2014) Trans Tech Publications, Switzerland.

关键词： image segmentation

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