Presents a method to obtain room transfer functions for applications in the design of intelligent systems for video conferencing. In these applications the acoustical dynamics, for example, affects the quality of the ...
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(纸本)0780366387
Presents a method to obtain room transfer functions for applications in the design of intelligent systems for video conferencing. In these applications the acoustical dynamics, for example, affects the quality of the recorded speech signal and the ability to achieve accurate source localization. The first part of the paper gives results on the identification of parameterized room transfer functions. The second part presents a method to extrapolate room transfer functions from the identified models. The extrapolation is achieved by getting a functional form of the transfer function parameters which depends on the speaker and the microphone locations.
A system for face model adaptation combining active tracking is presented. Input from an active camera is used for MPEG4 model based coding. First, the background is compensated considering a moving camera (tilt or pa...
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A system for face model adaptation combining active tracking is presented. Input from an active camera is used for MPEG4 model based coding. First, the background is compensated considering a moving camera (tilt or pan). Second, the talking face is segmented from the compensated background using fusion of frame differences. A morphological filter is then applied to make the system less sensitive to noise. Third, Hough transform and deformable template coupled with color information are exploited to detect the facial features, e.g., eyes, mouth. Fourth, a wireframe model is adapted to the extracted face by an extended dynamic mesh. The feasibility of the proposed system is demonstrated using several real active video sequences.
The paper addresses the problem of choosing automatically, and without user intervention, the best possible 2D image that should be used to map as a texture on polygons of a geometric model of complex scenes made of s...
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The paper addresses the problem of choosing automatically, and without user intervention, the best possible 2D image that should be used to map as a texture on polygons of a geometric model of complex scenes made of s...
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The paper addresses the problem of choosing automatically, and without user intervention, the best possible 2D image that should be used to map as a texture on polygons of a geometric model of complex scenes made of several objects. The geometric models and the texture data are obtained separately: the models are built from range data while the texture information is obtained from 2D color images of the scene. The system does not make any assumption on the way the texture data has been acquired. Texture selection results are presented for an indoor scene.
This paper presents a summary of a comprehensive research project involving development of a completely autonomous multirobot system for railway track maintenance applications. The specifications of this "real-wo...
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This paper presents a summary of a comprehensive research project involving development of a completely autonomous multirobot system for railway track maintenance applications. The specifications of this "real-world" application required us to consider a multirobot system conceptualization, rather than a single robot system.
This paper characterizes how well the modular neuro-fuzzy controller, MoNiF, can perform in robotic applications by testing it in a race-car simulator. The performance of this controller is compared with that of a neu...
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This paper characterizes how well the modular neuro-fuzzy controller, MoNiF, can perform in robotic applications by testing it in a race-car simulator. The performance of this controller is compared with that of a neural network controller (NNC) and the best programmed car (the ideal car). During the phase of learning from the ideal car, MoNiF reaches its stable performance (when it demonstrates good control) at lap 300; while the NNC requires 700 laps. Without further learning and when competing in the same learning track, MoNiF completes 20 laps of the track about 24 seconds later than the ideal car but 29 second ahead of the NNC car. However, when they all are competing in different unfamiliar tracks, MoNiF performs comparably to the ideal car, while NNC has completely lost its ability to drive. Observing the behaviors of NNC and MoNiF, we conclude that MoNiF learns to refine the output of expert rules and thus can drive in most tracks. Meanwhile NNC learns to drive in a particular track but loses its generality for other tracks. It is shown that this modular control method, equipped with the pretrained knowledge of only few simple expert rules, learns much faster than a NNC without any apriori knowledge.
In many industrial applications such as autonomous vehicle guidance and robotic manipulations, reliable 3-D informations must be extracted from the scene in order to provide a robust description of the environment in ...
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In many industrial applications such as autonomous vehicle guidance and robotic manipulations, reliable 3-D informations must be extracted from the scene in order to provide a robust description of the environment in which the system evolves. This paper presents the TRID sensor, a simple, fast, robust and accurate 3-D sensing device based on the Biris technology. TRID is limited to a lateral resolution of one point. Experimental results show that an accuracy better than 0.15% can be obtained for the depth component (Z) of 3-D points at a distance of 1.3 meter from a wooden black painted beam surface with an acquisition rate of 8.5 points/s.
In this paper we present a computational approach for extracting three-dimensional structure of controllable resolution, depth of field, and accuracy, all made available at real-time speeds, This approach utilizes the...
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In this paper we present a computational approach for extracting three-dimensional structure of controllable resolution, depth of field, and accuracy, all made available at real-time speeds, This approach utilizes the spatial and the temporal gradients of the streams of images acquired using an actively controlled camera, Depending on the requirements of a particular task, appropriate parameters such as disparity value sought, interframe camera displacement, and number of frames in a stream are chosen to control the resolution, depth of field, and accuracy, The acquisition and processing of the image stream are done in real time on a pipeline architecture based processor. Extensive experiments are presented to demonstrate the system's accuracy, controllability of depth of field and resolution, and ability to perform successfully in a variety of scenes. The system operated with no latency between image acquisition and processing. The total acquisition and processing time in these experiments is in the range from 0.27 to 1.56 s. The depth results have an accuracy of 85% to 92%. (C) 1996 Academic Press, Inc.
A novel paradigm for the synthesis of convergent-axis stereo geometries is presented. This paradigm incorporates constraints that represent task-oriented properties that can be easily derived in many applications of s...
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