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An interactive method for computing tooth-root orientation from 3D CT image

An interactive method for computing tooth-root orientation f...

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2010 international conference on Medical Image Analysis and Clinical Application, MIACA 2010

作者： Xie, Jie Liu, Yang Yin, Jilin Qiu, Tiantian Huang, Yuanliang Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiaotong University Shanghai 200240 China Department of Dentistry Shanghai East Hospital Affiliated to Tongji University Shanghai 200120 China

ISBN: (纸本)9781424480128

In tooth implantation surgery, dentist or surgeon needs to derive quantitative information about the place each implant should be go, the length and width of each implant should be, as well as the angle each implant should be inserted, based on the 3D CT image of jaw of a patient. In existing techniques, the information is measured from such slices that are generated from virtually cutting teeth-ridge along the direction perpendicular to teeth-ridge. However, according to the clinical experience and anatomical knowledge, usually the largest length of each implant should be measured on such slice that is generated by cutting teethridge along the tooth root direction. Besides, for each defect tooth, the most suitable angle or orientation to insert an implant is the tooth root orientation. Thus, in surgical planning of dental implant, it is much important to compute or estimate tooth root orientation from 3D CT image of jaw. To our knowledge, this problem is seldom studied in existing literature. In this paper, we try to discuss this problem. © 2010 IEEE.

关键词： Dental prostheses

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Compressive Sensing of Time Series for Human Action recognition

Compressive Sensing of Time Series for Human Action Recognit...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Oscar Perez Concha Richard Yi Da Xu Massimo Piccardi School of Computing and Communications Sydney University of Technology Sydney NSW Australia

Compressive Sensing (CS) is an emerging signal processing technique where a sparse signal is reconstructed from a small set of random projections. In the recent literature, CS techniques have demonstrated promising results for signal compression and reconstruction. However, their potential as dimensionality reduction techniques for time series has not been significantly explored to date. To this aim, this work investigates the suitability of compressive-sensed time series in an application of human action recognition. In the paper, results from several experiments are presented: (1) in a first set of experiments, the time series are transformed into the CS domain and fed into a hidden Markov model (HMM) for action recognition, (2) in a second set of experiments, the time series are explicitly reconstructed after CS compression and then used for recognition, (3) in the third set of experiments, the time series are compressed by a hybrid CS-Haar basis prior to input into HMM, (4) in the fourth set, the time series are reconstructed from the hybrid CS-Haar basis and used for recognition. We further compare these approaches with alternative techniques such as sub-sampling and filtering. Results from our experiments show unequivocally that the application of CS does not degrade the recognition accuracy, rather, it often increases it. This proves that CS can provide a desirable form of dimensionality reduction in pattern recognition over time series.

关键词： Time series analysis Hidden Markov models Accuracy Feature extraction Compressed sensing Image reconstruction pattern recognition

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Outdoor camera calibration method for a GPS & camera based surveillance system

Outdoor camera calibration method for a GPS & camera based s...

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IEEE-ICIT 2010 international conference on Industrial Technology, ICIT 2010

作者： Liu, Haoting Wang, Chunhui Lu, Hanqing Yang, Weihao National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China Laboratory of System Usability Evaluation Astronaut Research and Training Center of China Beijing 100193 China

ISBN: (纸本)9781424456970

A reliable outdoor binocular camera calibration method for a multi-GPS (Global Positioning System) apparatuses and multi-cameras based surveillance system is introduced in this paper. Different to other system, the user needs our system to track the motion and estimate the shape of the selected vehicle in real time by our binocular cameras. So we implement our system by these steps below: 1) fixing a GPS apparatus in each vehicle to collect the location information. 2) Calibrating the interior and exterior parameters of the binocular cameras. 3) computing the coordinate transform matrix between the GPS coordinate system and the camera coordinate system. 4) Using the GPS information to guide the binocular cameras to track the moving vehicle. To calibrate the binocular camera in an outdoor environment, we use the Scale Invariant Feature Transform (SIFT) descriptor to compute the features. The RANSAC matching technique is used to eliminate the mismatch point pairs. After that, we use an essential matrix based self-calibration technique to compute the camera parameter. Finally, we analyze the relationship between the blur affection level of the calibration image and our proposed method. Many experiment results have shown the robustness of our method. ©2010 IEEE.

关键词： Global positioning system

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Facial expression recognition using Local Directional pattern (LDP)

Facial expression recognition using Local Directional Patter...

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IEEE international conference on Image Processing

作者： Taskeed Jabid Md. Hasanul Kabir Oksam Chae Department of Computer Engineering Kyung Hee University South Korea

A robust face descriptor is an essential component for a good facial expression recognition system. In this paper, we analyze the performance of a new feature descriptor, Local Directional pattern (LDP), for the representation of facial expressions. LDP features are obtained by computing the edge response values in all eight directions at each pixel position and then a code is generated according to the relative magnitude's strength. Thus each expression is represented as a distribution of LDP codes. Different machine learning techniques are compared using Cohn-Kanade facial expression database for classification. Extensive experiments explicate the superiority of the proposed LDP based descriptor over other existing well known descriptors.

关键词： Face recognition Support vector machines Face Feature extraction Histograms Image recognition Facial features

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Novel Edge Features for Text Frame Classification in Video

Novel Edge Features for Text Frame Classification in Video

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international conference on pattern recognition

作者： Palaiahnakote Shivakumara Chew Lim Tan School of Computing National University of Singapore Singapore

ISBN: (纸本)9781424475421

Text frame classification is needed in many applications such as event identification, exact event boundary identification, navigation, video surveillance in multimedia etc. To the best of our knowledge, there are no methods reported solely dedicated to text frame classifications so far. Hence this paper presents a new approach to text frame classification in video based on capturing local observable edge properties of text frames, by virtue of the strong presence of sharp edges, straight appearances of edges and consistent proximity between edges. The approach initially classifies the blocks of the frame into text blocks and non-text blocks. The true text block is then identified among classified text blocks to detect text frames by the proposed features. If the text frame produces one true text block then it is considered as a text frame otherwise a non-text frame. We evaluate the proposed approach on a large database containing both text and nontext frames and publicly available data at two levels, i.e., estimating recall and precision at the block level and the frame level.

关键词： Image edge detection Feature extraction Classification algorithms pattern recognition Databases Robustness Pixel

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Real-Time Robotic Hand Control Using Hand Gestures

Real-Time Robotic Hand Control Using Hand Gestures

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international conference on Machine Learning and computing

作者： Jagdish Lal Raheja Radhey Shyam Umesh Kumar P. Bhanu Prasad Digital Systems Group Central Electronics Engineering Research Institute Council of Scientific and Industrial Research Pilani Rajasthan India

This paper presents a new approach for controlling robotic hand or an individual robot by merely showing hand gestures in front of a camera. With the help of this technique one can pose a hand gesture in the vision range of a robot and corresponding to this notation, desired action is performed by the robotic system. Simple video camera is used for computer vision, which helps in monitoring gesture presentation. This approach consists of four modules: (a) A real time hand gesture formation monitor and gesture capture, (b) feature extraction, (c) pattern matching for gesture recognition, (d) Command determination corresponding to shown gesture and performing action by robotic system. Real-time hand tracking technique is used for object detection in the range of vision. If a hand gesture is shown for one second, the camera captures the gesture. Object of interest is extracted from the background and the portion of hand, representing the gesture, is cropped out using the statistical property of hand. Extracted hand gesture is matched with the stored database of hand gestures using pattern matching. Corresponding to the matched gesture, action is performed by the robot.

关键词： Robot control Robot vision systems Cameras pattern matching Computerized monitoring Computer vision Real time systems Feature extraction pattern recognition Object detection

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Personal Authentication Based on Keystroke Dynamics Using soft computing Techniques

Personal Authentication Based on Keystroke Dynamics Using So...

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international conference on Communication software and Networks, ICCSN

作者： Marcus Karnan M. Akila Department of Computer Science and Engineering Tamilnadu College of Engineering Coimbatore India Department of Information Technology Vivekanandha College of Engineering of Women Tiruchengode India

ISBN: (纸本)9781424457267;9780769539614

The need to secure sensitive data and computer systems from intruders, while allowing ease of access for authenticate user is one of the main problems in computer security. Traditionally, passwords have been the usual method for controlling access to computer systems but this approach has many inherent flaws. Keystroke dynamics is a promising biometric technique to recognize an individual based on an analysis of his/her typing patterns. In the experiment, we measure mean, standard deviation and median values of keystroke features such as latency, duration, digraph and their combinations and compare their performance. Particle swarm optimization (PSO), genetic algorithm (GA) and the proposed ant colony optimization (ACO) are used for feature subset selection. Back propagation neural network (BPNN) is used for classification. ACO gives better performance than PSO and GA with regard to feature reduction rate and classification accuracy. Using digraph as the feature for feature subset selection is novel and show good classification performance.

关键词： Authentication Computer security Control systems Biometrics pattern recognition pattern analysis Measurement standards Delay Particle swarm optimization Genetic algorithms

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A Wheel Graph Structured Associative Memory for Single-Cycle pattern recognition within P2P Networks

A Wheel Graph Structured Associative Memory for Single-Cycle...

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international conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Amiza Amir Raja Azlina Raja Mahmood Asad I. Khan Clayton School of IT Monash University Melbourne Australia

A novel and efficient associative-memory-based pattern recognition scheme within P2P networks is proposed and implemented. The proposed scheme, known as the multi-wheel Graph Neuron, is adapted from Graph Neuron-based algorithms which are single-cycle, light-weight, and scalable associative-memory-based pattern recognition algorithms for wireless sensor networks, and has been implemented over a structured P2P Chord overlay network. The proposed approach promotes collaboration among peers during the detection process within the P2P networks. Since the scheme only required single cycle learning, the communication cost amongst peers is minimized. The preliminary results show that the proposed single-cycle recognition scheme guarantees high detection accuracy.

关键词： Wheels Associative memory pattern recognition Neurons Peer to peer computing Wireless sensor networks Crosstalk Costs Magnesium compounds Neural networks

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Fusion of Manifold Learning and Spectral Clustering Algorithmwith Applications to Fault Diagnosis

Fusion of Manifold Learning and Spectral Clustering Algorith...

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international conference on Machine Learning and computing

作者： Yulin Zhang Jian Zhuang Sun'an Wang Mechanical Engineering Xi'an Jiaotong University Xi'an Shaanxi China

Large amount of multivariate data in many areas of science raises the problem of data analysis and visualization. Focusing on high dimensional and nonlinear data analysis, an improved manifold learning algorithm is introduced, then a new approach is proposed by combining adaptive local linear embedding (ALLE) and recursively applying normalized cut algorithm (RANCA). A novel adaptive local linear embedding algorithm is employed for nonlinear dimension reduction of original dataset. The recursively applying normalized cut algorithm is used for clustering of low dimensional data. The simulation results on three UCI standard datasets show that the new algorithm maps high-dimensional data into low-dimensional intrinsic space, and perfectly solves the problem of higher dependence on the structure of datasets in the traditional methods. Thus classification accuracy and robustness of spectral clustering algorithm are remarkably improved. The experiment results on Tennessee-Eastman process (TEP) also demonstrate the feasibility and effectiveness in fault pattern recognition.

关键词： Clustering algorithms Fault diagnosis Signal processing algorithms Data analysis pattern recognition Machine learning Manifolds Machine learning algorithms Mechanical engineering Robustness

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A hybrid image representation for indoor scene classification

A hybrid image representation for indoor scene classificatio...

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international conference on Image and Vision computing New Zealand, IVCNZ

作者： Zhibin Niu Yue Zhou Kun Shi Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424496297

Although scene classification has been studied for decades, indoor scene recognition remains challenging due to its large view point variance and massive irregular artefacts. In fact, most existing methods for outdoor scene classification perform poorly in the indoor situation. To address the problem, we propose a hybrid image representation by combining the global information with the local structure of the scene. First, the global discriminative information is captured by pyramid GIST feature. Second, the local structure is encoded by the bag of features method with Histogram Intersection Kernel (HIK). Finally, HIK based SVM is employed for learning and classification. Experiments on the MIT indoor scene database show that our approach could significantly improve the recognition accuracy of the state-of-art methods by about 14%.

关键词： Support vector machines

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