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Throat microphone signal for speaker recognition 8

Throat microphone signal for speaker recognition

引用

8th International Conference on Spoken Language Processing, ICSLP 2004

作者： Shahina, A. Yegnanarayana, B. Kesheorey, M.R. Speech and Vision Laboratory Department of Computer Science and Engg. Indian Institute of Technology Madras Chennai600 036 India Center for Artificial Intelligence and Robotics Bangalore560 001 India

Speaker recognition systems perform better when clean speech signals are used for the task. In the presence of high levels of background noise, speech recorded from a close speaking microphone will be degraded and hence the performance of the speaker recognition system. Use of a transducer held at the throat results in a signal that is clean even in a noisy environment. This paper discusses the prospect of using such signals for speaker recognition. A study of a text-independent speaker recognition system based on features extracted from speech simultaneously recorded using a throat microphone and a close-speaking microphone in clean and simulated noisy conditions is conducted. Autoassociative neural networks are used to model the speaker characteristics based on the vocal tract system and excitation source features represented by weighted linear prediction cepstral coefficients and linear prediction residual, respectively. The results of experimental studies show that the speech collected from the throat microphone can be used for tasks like speaker recognition, especially in noisy conditions.

关键词： Microphones

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Kernel snakes: non-parametric active contour models

Kernel snakes: non-parametric active contour models

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： W. Abd-Almageed C.E. Smith S. Ramadan Robotics Artificial Intelligence and Vision Laboratory Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.

关键词： Kernel Active contours Deformable models Image edge detection Intelligent robots Robot vision systems Artificial intelligence Laboratories Bayesian methods Decision theory

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Hidden Markov models for silhouette classification

Hidden Markov models for silhouette classification

引用

作者： Abd-Almageed, Wael Smith, Christopher Robotics Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico United States

ISBN: (纸本)1889335185

In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov Models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach and show its invariance under severe rotation conditions. Also, a comparison with other techniques that use Hidden Markov Models for object classification from silhouettes is presented.

关键词： Hidden Markov models

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Mixture models for dynamic statistical pressure snakes

Mixture models for dynamic statistical pressure snakes

引用

作者： Abd-Almageed, Wael Smith, Christopher E. Robotics Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico Albuquerque NM 87131

This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is used to model the data into a Mixture of Gaussions (MoG). Bayesian theory is then employed as a decision making mechanism. Experimental results using the traditional pressure model and the new mixture pressure model demonstrate the effectiveness of the new models. © 2002 IEEE.

关键词： Object recognition

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Mixture models for dynamic statistical pressure snakes

Mixture models for dynamic statistical pressure snakes

引用

16th International Conference on Pattern Recognition, ICPR 2002

作者： Abd-Almageed, Wael Smith, Christopher E. Electrical and Computer Engineering Department Robotics Artificial Intelligence and Vision Laboratory University of New Mexico AlbuquerqueNM87131 United States

ISBN: (纸本)0769516963

This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is used to model the data into a Mixture of Gaussians (MoG). Bayesian theory is then employed as a decision making mechanism. Experimental results using the traditional pressure model and the new mixture pressure model demonstrate the effectiveness of the new models. © 2002 IEEE.

关键词： Mixtures

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Using intrinsic images for shadow handling

Using intrinsic images for shadow handling

引用

International Conference on Intelligent Transportation

作者： R.F.K. Martin O. Masoud N. Papanikolopoulos Artificial Intelligence Vision and Robotics Laboratory Department of Computer Science and Engineering University of Minnesota USA

The ability to detect shadows is a critical feature of any intelligent transportation system (ITS). The improper handling of shadows can be the cause of erroneous conclusions in traffic analysis. As vision-based ITS applications are becoming more popular, it is important to minimize the effects of shadows. Here, we present a novel method for shadow handling in a sequence of images. Following recent work motivated by studies of the statistics of natural images, we show that this new method is a reliable detector of shadows and can be easily implemented in real-time.

关键词： Intelligent transportation systems Vehicle detection Artificial intelligence Intelligent robots Vehicles Layout Magnetic analysis computerized monitoring computer vision Image edge detection

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vision-based monitoring of intersections

Vision-based monitoring of intersections

引用

International Conference on Intelligent Transportation

作者： H. Vceraraghavan O. Masoud N. Papanikolopoulos Artificial Intelligence University of Minnesota Vision and Robotics Laboratory Department of Computer Science and Engineering Artificial Intelligence University of Minnesota USA

The goal of tills project Is to develop a passive vision-based sensing system. The system will be capable of monitoring an intersection by observing the vehicle and pedestrian flow, and predicting situations that might give rise to accidents. A single camera looking at all intersection from an arbitrary position is used. However, for extended applications, multiple cameras will be needed. Some of the key elements are camera calibration, motion tracking, vehicle classification, and situations giving rise to collisions. In this paper, we focus on motion tracking. Motion segmentation is performed using an adaptive background model that models each pixel as a mixture of Gaussians. The method used is similar to the method of Stauffer et at. for motion segmentation. Tracking of objects is performed by computing the overlap between oriented bounding boxes. The oriented boxes are computed by vector quantization of the blobs in the scene. The principal angles computed during vector quantization along with other cues of the object are used for classification of detected entities into vehicles and pedestrians.

关键词： Monitoring Cameras Tracking Motion segmentation computer vision Vector quantization Vehicle detection Accidents Calibration Gaussian processes

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Contour migration: solving object ambiguity with shape-space visual guidance

Contour migration: solving object ambiguity with shape-space...

引用

IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： W. Abd-Almageed C.E. Smith Department of Electrical and Computer Engineering Robotics Artificial Intelligence and Vision Laboratory University of New Mexico Albuquerque NM USA

A fundamental problem in computer vision is the issue of shape ambiguity. Simply stated, a silhouette cannot uniquely identify an object or an object's classification since many unique objects can present identical occluding contours. This problem has no solution in the general case for a monocular vision system. This paper presents a method for disambiguating objects during silhouette matching using a visual servoing system. This method identifies the camera motion(s) that gives disambiguating views of the objects. These motions are identified through a new technique called contour migration. The occluding contour's shape is used to identify objects or object classes that are potential matches for that shape. A contour migration is then determined that disambiguates the possible matches by purposive viewpoint adjustment. The technique is demonstrated using an example set of objects.

关键词： Shape Cameras computer vision Object recognition Machine vision Databases Intelligent robots Robot vision systems Artificial intelligence Laboratories

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Mixture models for dynamic statistical pressure snakes

Mixture models for dynamic statistical pressure snakes

引用

International Conference on Pattern Recognition

作者： W. Abd-Almageed C.E. Smith Robotics Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico Albuquerque NM USA

This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is used to model the data into a Mixture of Gaussians (MoG). Bayesian theory is then employed as a decision making mechanism. Experimental results using the traditional pressure model and the new mixture pressure model demonstrate the effectiveness of the new models.

关键词： Image edge detection computer vision Equations Robot vision systems Robustness Bayesian methods Active contours Gaussian distribution Intelligent robots Artificial intelligence

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Complementary linear biases in spatial derivative estimation for improving geometry-driven diffusion processes

Complementary linear biases in spatial derivative estimation...

引用

15th International Conference on Pattern Recognition, ICPR 2000

作者： Toczyski, William D. Papanikolopoulos, Nikolaos P. Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota 4-192 EE/CS Bldg. 200 Union Street SE MinneapolisMN55455 United States

ISBN: (纸本)0769507506

This paper introduces a broadly applicable technique for visibly improving the digitized, grey-level outputs produced by a host of iterative geometric diffusion methods. By replacing standard, central-difference estimates of discrete spatial gradients with alternating image derivative estimates that are offset by known, complementary biases, errors accumulated during iteration are reduced and the quality of geometric diffusions is improved. This unexpected synergy occurs at no added computational cost over central difference methods. Very simple to implement, the innovation is introduced at the level of spatial derivatives;hence, for a given process, any derived higher level mathematical properties - for example, group invariance or scale-space properties - can be preserved. © 2000 IEEE.

关键词： Iterative methods

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