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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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Preface

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2002年 2359卷 v页

作者： Tistarelli, Massimo Bigun, Josef Jain, Anil K. University of Sassari Computer Vision Laboratory DIBEV Via Muroni 25 Sassari Italy Halmstad University Kristian IVs vag Halmstad Sweden Michigan State University Department of Computer Science and Engineering 3115 Engineering Bldg East LansingMI United States

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Neural network models for recognition of consonant-vowel (C/sup n/V) utterances

Neural network models for recognition of consonant-vowel (C/...

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International Joint Conference on Neural Networks (IJCNN)

作者： S.V. Gangashetty B. Yegnanarayana Speech and Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

ISBN: (纸本)0780370449

In this paper, we present an approach based on neural network models for recognition of utterances of syllable-like units in Indian languages. The distribution capturing ability of an autoassociative neural network model is exploited to perform nonlinear principal component analysis for compressing the size of the feature vector. A constraint satisfaction model is proposed to incorporate the acoustic-phonetic knowledge and to combine the outputs of subnets to arrive at the overall decision on the class of an input utterance.

关键词： Neural networks speech recognition Natural languages Vectors Principal component analysis speech analysis Feature extraction Laboratories computer science Information analysis

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Source and system features for speaker recognition using AANN models

Source and system features for speaker recognition using AAN...

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International Conference on Acoustics, speech, and Signal Processing (ICASSP)

作者： B. Yegnanarayana K. Sharat Reddy S.P. Kishore Speech and Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

We study the effectiveness of the features extracted from the source and system components of the speech production process for the purpose of speaker recognition. The source and system components are derived using linear prediction (LP) analysis of short segments of speech. The source component is the LP residual derived from the signal, and the system component is a set of weighted linear prediction cepstral coefficients. The features are captured implicitly by a feedforward autoassociative neural network (AANN). Two separate speaker models are derived by training two AANN models using feature vectors corresponding to source and system components. A speaker recognition system for 20 speakers is built and tested using both the models to evaluate the performance of source and system features. The study demonstrates the complementary nature of the two components.

关键词： Speaker recognition Cepstral analysis speech analysis Feature extraction speech processing Production systems Neural networks Feedforward neural networks Vectors System testing

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A new camera calibration method for robotic applications

A new camera calibration method for robotic applications

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： Jun-Sik Kim In-So Kweon Department of Electrical Engineering and Computer Science Robotics and Computer Vision Laboratory KAIST South Korea Department of Electrical Engineering and Computer Science KAIST South Korea

In this paper, we present a camera calibration method using two views of three pairs of concentric circles with known sizes. A new invariant property for circle is introduced to determine the position of the center of the projected circle. Given two image ellipses and their corresponding centers, a cross-ratio based method estimates the center of projected circles using the new invariant property. The accurate center of projected concentric circles provides correct correspondences between ellipses in the image plane and circles in 3D plane. We also demonstrate that two views of three pairs of coplanar concentric circles are enough to determine the intrinsic camera parameters, such as the focal length, the aspect ratio, and the principal point. We validate the performance of the method using both synthetic and real images. Our method shows a comparable performance with respect to similar calibration methods using planes. The use of concentric circles, however, provides correct correspondences between 3D target points and their image points, and greatly simplifies the calibration problem.

关键词： Robot vision systems Cameras Calibration Mobile robots Application software computer vision Laboratories computer science Layout Robot kinematics

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AIM: an attentionally-based system for the interpretation of angiography

AIM: an attentionally-based system for the interpretation of...

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International Workshop on Medical Imaging and Augmented Reality

作者： F.K.H. Quek C. Kirbas F. Charbel Vision Interfaces and Systems Laboratory Computer Science and Engineering Wright State University Dayton OH USA Department of Neurosurgery University of Illinois Chicago USA

ISBN: (纸本)0769511139

We propose a model for the interactive interpretation of medical images pertaining to human neurovascular system. This attentionally-based interactive model, ATM, is founded upon human selective attention. AIM combines human operator's high level reasoning with machine perception and exploits human interaction as part of the solution. AIM defines two channels of interaction: context ("what to look for"), and focus-of-attention ("where to look") by which the user directs the attention of the machine perception. AIM facilitates varying degrees of human intervention in the process by providing four levels of abstraction for the context information. This hierarchy of context abstractions permits the system to function more autonomously (doing high-level tasks like extracting an arterial vessel) in routine interpretation, and to require more user intervention (e.g. locating arterial wall boundaries) as the image complexity increases or data quality worsen. This is important in medical imaging where the users demand ultimate control and confidence in the system. Such technology can contribute significantly on the design of radiological imaging systems.

关键词： Angiography Humans Image analysis computer science Biomedical imaging Neurosurgery computer errors Man machine systems Machine vision Laboratories

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Multispeaker speech activity detection for the ICSI meeting recorder

Multispeaker speech activity detection for the ICSI meeting ...

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IEEE Workshop on Automatic speech Recognition and Understanding

作者： T. Pfau D.P.W. Ellis A. Stolcke International Computer Science Institute Berkeley CA USA Department of Electrical Engineering Columbia University NY USA Speech Technology and Research Laboratory SRI International Inc. Menlo Park CA USA

As part of a project into speech recognition in meeting environments, we have collected a corpus of multichannel meeting recordings. We expected the identification of speaker activity to be straightforward given that the participants had individual microphones, but simple approaches yielded unacceptably erroneous labelings, mainly due to crosstalk between nearby speakers and wide variations in channel characteristics. Therefore, we have developed a more sophisticated approach for multichannel speech activity detection using a simple hidden Markov model (HMM). A baseline HMM speech activity detector has been extended to use mixtures of Gaussians to achieve robustness for different speakers under different conditions. Feature normalization and crosscorrelation processing are used to increase the channel independence and to detect crosstalk. The use of both energy normalization and crosscorrelation based postprocessing results in a 35% relative reduction of the frame error rate. speech recognition experiments show that it is beneficial in this multispeaker setting to use the output of the speech activity detector for presegmenting the recognizer input, achieving word error rates within 10% of those achieved with manual turn labeling.

关键词： Crosstalk speech recognition Microphones Hidden Markov models Labeling Detectors Microwave integrated circuits Noise level Silicon compounds computer science

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UNDERSTANDING GESTURES IN MULTIMODAL HUMAN computer INTERACTION

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International Journal on Artificial Intelligence Tools 2000年第2期9卷 205-223页

作者： SANSHZAR KETTEBEKOV RAJEEV SHARMA Vision Interface Laboratory Department of Computer Science and Engineering Pennsylvania State University 220 Pond Laboratory University Park PA 16802 USA

In recent years because of the advances in computer vision research, free hand gestures have been explored as a means of human-computer interaction (HCI). Gestures in combination with speech can be an important step toward natural, multimodal HCI. However, interpretation of gestures in a multimodal setting can be a particularly challenging problem. In this paper, we propose an approach for studying multimodal HCI in the context of a computerized map. An implemented testbed allows us to conduct user studies and address issues toward understanding of hand gestures in a multimodal computer interface. Absence of an adequate gesture classification in HCI makes gesture interpretation difficult. We formalize a method for bootstrapping the interpretation process by a semantic classification of gesture primitives in HCI context. We distinguish two main categories of gesture classes based on their spatio-temporal deixis. Results of user studies revealed that gesture primitives, originally extracted from weather map narration, form patterns of co-occurrence with speech parts in association with their meaning in a visual display control system. The results of these studies indicated two levels of gesture meaning: individual stroke and motion complex. These findings define a direction in approaching interpretation in natural gesture-speech interfaces.

关键词： Gesture multimodal natural interface gesture semantics

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

Complementary linear biases in spatial derivative estimation...

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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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Counting bicycles using computer vision

Counting bicycles using computer vision

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International Conference on Intelligent Transportation

作者： S. Rogers N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA

This paper describes a system for monitoring bicycle activity in sequences of gray scale images acquired by a stationary camera. The system is suitable for use in applications that aim to increase the efficiency and safety of existing traffic systems. One such application is to determine usage and congestion of bicycle path. The output of the system is a count of the number of bicycles detected in the image sequence. The system is model-based in the sense that it uses a simple model of two circular objects separated by a relatively known distance. Our system uses four levels of abstraction: raw images, blobs, edge images, and the bicycle model. The system was implemented on a dual Pentium computer equipped with a Matrox imaging board and achieved a peak performance of 8 frames per second. Experimental results based on outdoor scenes have shown promising results for a variety of weather conditions.

关键词： Bicycles computer vision Application software computerized monitoring Cameras Safety Traffic control Image sequences Image edge detection Layout

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