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2013 IEEE 9th International Conference on Intelligent Computer Communication and processing, ICCP 2013

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

ISBN: (纸本)9781479914937

This paper proposes a novel method to determine the speed of the surrounding vehicles in traffic scenarios. Relying on the video information obtained from a stereo camera mounted on a moving vehicle, we first determine the vehicle ego motion based on static scene features then we determine the relative motion between objects based on features situated on the moving objects. For robustness to false feature matches everything is plugged into a multi-RANSAC framework. The novelty of the method consist in the fact that the relative motion between the objects can be determined with the same algorithm that was previously used for ego motion estimation, the only difference consisting in the geometric constraints that are imposed to the subset of point features considered for inliers set detection and evaluation. Also, the proposed method does not rely on the fact that objects are detected previously and it does not detect the objects. © 2013 IEEE.

关键词： Vehicles

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Improved Predictive Personalized Modelling with the use of Spiking Neural Network System and a Case Study on Stroke Occurrences Data

Improved Predictive Personalized Modelling with the use of S...

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International Joint Conference on Neural Networks

作者： Muhaini Othman Nikola Kasabov Enmei Tu Valery Feigin Rita Krishnamurthi Zhengguang Hou Yixiong Chen Jin Hu Knowledge Engineering and Discovery Research Institute (KEDRI) Auckland University of Technology Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The National Institute for Stroke and Applied Neurosciences (NISAN) AUT University Institute of Automation Chinese Academy of Sciences (CASIA)

ISBN: (纸本)9781479914821

This paper is a continuation of previous published work by the same authors on Personalized Modelling and Evolving Spiking Neural Network Reservoir architecture (PMeSNNr). The focus is on improvement of predictive modeling methods for the stroke occurrences case study utilizing an enhanced NeuCube architecture. The adaptability of the new architecture leads towards understanding feature correlations that affect the outcome of the study and extracts new knowledge from hidden patterns that reside within the associations. Through this new method, estimation of the earliest time point for stroke prediction is possible. This study also highlighted the improvement from designing a new experimental dataset compared to previous experiments. Comparative experiments were also carried out using conventional machine learning algorithms such as kNN, wkNN, SVM and MLP to prove that our approach can result in much better accuracy level.

关键词： Neurons Data models Reservoirs Accuracy Brain modeling Computer architecture Predictive models

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Unsupervised segmentation using cluster ensembles

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 8836卷 76-84页

作者： Zhang, Wei Yang, Jie Jia, Wenjing Kasabov, Nikola Jia, Zhenhong Zhou, Lei Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China School of Computing and Communications University of Technology Sydney United States Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Information Science and Engineering Xinjiang University Urumqi China

We propose a novel framework for automatic image segmentation. In this approach, a mixture of several over-segmentation methods are used to produce superpixels and then aggregation is achieved using a cluster ensemble method. Generated by different existing segmentation algorithms, superpixels can describe the manifold patterns of a natural image such as color space, smoothness and texture. We use them as the initial superpixels. grouping cues which affect the performance of segmentation can also be captured. After the oversegmentation, the simultaneous collection of superpixels is expected to achieve synergistic effects and ensure the accuracy of the segmentation. For this purpose, cluster ensemble methods are used to process the initial segmentation results and produce the final result. Our method achieves significantly better performance on the Berkeley Segmentation Database compared to state-of-theart techniques. © Springer International Publishing Switzerland.

关键词： Superpixels

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NeuCube(ST) for spatio-temporal data predictive modelling with a case study on ecological data

NeuCube(ST) for spatio-temporal data predictive modelling wi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Enmei Tu Nikola Kasabov Muhaini Othman Yuxiao Li Susan Worner Jie Yang Zhenghong Jia The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China The Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand Universiti Tun Hussein Onn Malaysia Lincoln University Lincoln NZ School of Information Science and Engineering Xinjiang University Urumqi China

Early event prediction challenges most of existing modeling methods especially when dealing with complex spatio-temporal data. In this paper we propose a new method for predictive data modelling based on a new development of the recently proposed NeuCube spiking neural network architecture, called here NeuCube (ST) . The NeuCube uses a Spiking Neural Network reservoir (SNNr) and dynamic evolving Spiking Neuron Network (deSNN) classifier. NeuCube (ST) is an integrated environment including data conversion into spike trains, input variable mapping, unsupervised learning in the SNNr, supervised classification learning, activity visualization and network structure analysis. A case study on a real world ecological data set is presented to demonstrate the validity of the proposed method.

关键词： Neurons Training Reservoirs Data models Predictive models Biological neural networks Biological system modeling

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Remote sensing image enhancement based on relative entropy and fuzzy algorithm

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Journal of Applied Sciences 2013年第13期13卷 2394-2398页

作者： Yang, Bo Jia, Zhen-Hong Qin, Xi-Zhong Yang, Jie Hu, Ying-Jie College of Information Science and Engineering Xinjiang University Urumqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

An improved maximum between-cluster variance (OTSU) algorithm was proposed to obtain the threshold adaptively in order to overcome the disadvantages of traditional Pal-King algorithm. The algorithm is able to globally enhance the remote sensing images but, for the relevance of neighboring pixel information is not taken into account in the improved algorithm, the visual effect of low contrast images is also not so good. The algorithm in this study can be used to realize the self-adaptive contrast enhancement when the relative entropy is regarded as a criterion. In order to test the enhancement effect of the proposed algorithm for remote sensing image, we choose 50 remote sensing images for test. Experimental results show that a good visual effect is obtained and the clarity, standard deviation has been improved greatly. Therefore, the algorithm is thought to be an effective ways to get better visual effects and more obvious detailed information. © 2013 Asian Network for Scientific Information.

关键词： image enhancement

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Fuzzy headlight intensity controller using wireless sensor network

Fuzzy headlight intensity controller using wireless sensor n...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Victor Nutt Shubhalaxmi Kher Mehul Raval Electrical Engineering Arkansas State University Jonesboro USA Pattern Recognition and Image Processing Group DAIICT Gandhinagar India

ISBN: (纸本)9781479900213

This paper addresses the issue of headlight intensity to alleviate glare and blinding during night for drivers. Many factors are considered when analyzing automobile transportation in order to increase safety. One of the most prominent factors for night-time travel is temporary blindness due to elevated headlight intensity. This is particularly prominent on single lane roads. While headlight intensity provides better visual acuity, it inversely affects oncoming traffic. This problem is compounded when both drivers are using a higher headlight intensity setting. Also, higher speed due to decreased traffic levels at night increases the severity of accidents. In order to eliminate accidents due to temporary driver blindness, a fuzzy controller is designed based on the data captured using a wireless sensor network (WSN). Low latency allows quicker headlight intensity adjustment to minimize temporary blindness. Multiple attributes are taken into consideration for controller design. The results show that controller output is nearly instantaneous and generates control signal continuously.

关键词： Vehicles Roads Meteorology Wireless sensor networks Fuzzy logic Sensors

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Dynamic monitoring technique for remote-sensing image of invasive alien plant species

Dynamic monitoring technique for remote-sensing image of inv...

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2013 International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2013

作者： Hesen, Rabigul Jia, Zhenhong Yang, Jie Hu, Raphael College of Information Science and Engineering Xinjiang University Urumqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

ISBN: (纸本)9783642419072

Invasive Alien Plant Species (IAPS) could seriously affect the local ecosystem balance, and pose a threat to the ecological security. In order to effectively monitor and control invasive alien plants, it needs to monitor the spatial distribution and dynamic changes of IAPS. It will cite the method of nonsubsampled contourlet transform (NSCT) combination with fuzzy C-means clustering algorithm in this paper, which is used in remote sensing image change detection, for monitoring the dynamic changes of biological invasive species in this paper, to get a better detection result. © Springer-Verlag Berlin Heidelberg 2013.

关键词： image enhancement

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Novel change detection method based on direction feature and fuzzy clustering for remote sensing images and its application in biological invasions

Novel change detection method based on direction feature and...

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2013 International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2013

作者： Li, Qingsong Qin, Xizhong Jia, Zhenhong Yang, Jie Hu, Raphael College of Information Science and Engineering Xinjiang University 830046 Urumqi China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 200240 Shanghai China Knowledge Engineering and Discovery Research Institute Auckland University of Technology 1020 Auckland New Zealand

ISBN: (纸本)9783642419072

Change detection techniques attempt to be used for remote sensing monitoring of invasive plants. A novel change detection method based on direction feature and RFLICM (an improved fuzzy C-means clustering) is proposed. Firstly, the difference image is acquired from multitemporal images. The pixel values of the difference image are updated using the template of directional neighborhood based on direction feature. The final change detection map is achieved by clustering the pixel values of the difference image using RFLICM algorithm into two disjoint classes: changed and unchanged. The results obtained by experiment are compared with some other existing state of the art methods. It is observed that the proposed method outperforms the other methods. Finally, the proposed change detection technique is applied to remote sensing monitoring of invasive plants. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Change detection

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Automatic detection of slab surface defects base on the machine vision

Automatic detection of slab surface defects base on the mach...

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第五届宝钢学术年会

作者： CHEN Ziyi LAN Shouren FANG Zhihong XIA Yong WANG Lisheng YAN Jingqi Institute of Image Processing & Pattern Recognition Department of AutomationShanghai Jiaotong Unive Research Institute Baoshan Iron & Steel Co.Ltd.Shanghai 201900China

The surface quality of the steel slab directly affects the performance and quality of the final steel *** to the raw material,rolling equipment and processing technology,however,different types of surface defects might exist on the steel slab surface,such as scarring,crack,roll marks,scratches,pinholes,phosphorus leather,holes,pitting,*** defects not only affect the appearance of the steel products,but also reduce the performances of steel products,such as the corrosion resistance,wear resistance and fatigue strength of the ***,it is very important to detect and identify automatically surface defects of steel slab products on-line and to control and improve the surface quality of the steel *** is a very complex *** this paper,we introduce some machine vision techniques to detect and identify defects from the steel slab *** adaboost based machine leaming method is proposed to detect defects from steel slab *** is usually able to remove pseudo-defects.A directional detector based on wavelet transformation is used to detect directional structural crack feature from the complex steel slab surface.A saliency based method combining elastic threshold method and Gabor wavelet filter is used to detect crack from the steel slab *** results show the effectiveness of these methods.

关键词： the steel slab surface defect surface detection machine vision

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Remote sensing image enhancement based on Shearlet transform

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Guangdianzi Jiguang/Journal of Optoelectronics Laser 2013年第11期24卷 2249-2253页

作者： Yang, Bo Jia, Zhen-Hong Qin, Xi-Zhong Yang, Jie Hu, Raphael College of Information Science and Engineering Xinjiang University Urumqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

Remote sensing image with the characteristics of overall dim brightness, low-contrast and no obvious distinction between the target and background, makes the remote sensing image enhancement technology play an active role for improving the contrast of the image and highlighting some local details. At present, the image multi-scale system has gained successful applications in image processing, wavelet transform, Curverlet transform, Contourlet transform and some improved algorithms based on them. One of the most common shortcomings of the frameworks of the system Curverlet and Contourlet is lacking of providing a unified treatment between the continuity and the digital world. Shearlet system is the only one which satisfies this property, yet still optimally delivers sparse approximations of images. Based on Shearlet transform, we propose a new algorithm for remote sensing image enhancement. First, the remote sensing images are transformed into the low frequency coefficients and high frequent coefficients by Shearlet transform;second, we use fuzzy contrast enhancement on the low frequency coefficients of Shearlet transform;third, we use fuzzy enhancement on the high frequency coefficients of each scale and direction. Experimental results show that in the subjective side, we obtain a good visual effect, and objectively, the image entropy and mean have a significant promotion.

关键词： image enhancement

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