A system of multisensor image fusion and enhancement for visibility improvement is proposed in this paper for helping drivers driving at night or under bad weather conditions. Video stream captured by a CCD camera is ...
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A system of multisensor image fusion and enhancement for visibility improvement is proposed in this paper for helping drivers driving at night or under bad weather conditions. Video stream captured by a CCD camera is enhanced, then aligned and fused with another stream captured by a thermal camera to improve the visibility of roads in extremely low lighting conditions. A nonlinear image enhancement technique capable of dynamic range compression and contrast enhancement is developed to enhance the visible images prior to fusion. The thermal image and the enhanced visible image are then aligned based on prior information obtained on image registration process. Pixel-level multiresolution based image fusion method is applied to merge source images. After image fusion, a color restoration is performed on fused images with the chromatic information of visible images. The entire image processing and analysis system is being installed in an FPGA environment. Preliminary results obtained in various experiments conducted with the proposed system are encouraging
Development of a visibility improvement system for helping drivers with poor vision during night and bad weather conditions is proposed in this paper. Video streams captured by a CCD camera and an infrared camera are ...
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Development of a visibility improvement system for helping drivers with poor vision during night and bad weather conditions is proposed in this paper. Video streams captured by a CCD camera and an infrared camera are registered and fused to obtain image details in extremely low lighting conditions. A novel image enhancement technique employing a nonlinear expansion function is developed for enhancing the fused images. The slope characteristics of the expansion function at an individual pixel location is based on the statistical properties of its neighborhood pixels. The expansion function is also capable of reducing the intensity of the overly bright image regions due to the presence of head lights from vehicles in opposite direction. The enhanced video stream is then subjected to a color restoration process to introduce natural color using the color information gathered from the CCD camera image. The entire image processing and analysis system is being installed in an FPGA environment for performing the processing in real-time. The processed video stream is displayed on a Head-Up Display located directly in front of the driver to assist him/her to drive safely in poor visibility environments. Preliminary results obtained in various experiments conducted with the proposed system are encouraging.
The reflectance of fabric surface is commonly represented by a 4D bidirectional reflectance distribution function (BRDF). To generate the BRDF from measured data by a gonioreflectometer with 2 degrees of freedom of th...
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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 hen...
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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.
The research on analysis of three-dimensional motion by using a monocular camera instead of a stereo camera has important applications for making the microscopes used in microbiology or constructing the autonomous rob...
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A modified approach on modular PCA for face recognition is presented in this paper. The proposed changes aim to improve the recognition rates for modular PCA for face images with large variation in light and facial ex...
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A modified approach on modular PCA for face recognition is presented in this paper. The proposed changes aim to improve the recognition rates for modular PCA for face images with large variation in light and facial expression. The eyes form one of the most invariant regions on the face. A sub-image from this region is considered. Weight vectors from this region are appended to the existing weight vector for modular PCA. The accuracies for the modified method, the original method and PCA method are evaluated under conditions of varying pose, illumination and expressions using standard face databases.
Decision trees represent a simple and powerful method of induction from labeled examples. Univariate decision trees consider the value of a single attribute at each node, leading to the splits that are parallel to the...
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Decision trees represent a simple and powerful method of induction from labeled examples. Univariate decision trees consider the value of a single attribute at each node, leading to the splits that are parallel to the axes. In linear multivariate decision trees, all the attributes are used and the partition at each node is based on a linear discriminate (a hyperplane). Nonlinear multivariate decision trees are able to divide the input space arbitrarily based on higher order parameterizations of the discriminate, though one should be aware of the increase of the complexity and the decrease in the number of examples available as moves further down the tree. In omnivariate decision trees, the decision node may be univariate, linear, or nonlinear. Such architecture frees the designer from choosing the appropriate tree type for a given problem. In this paper, we propose to do the model selection at each decision node based on a novel classifiability measure when building omnivariate decision trees. The classifiability measure captures the possible sources of misclassification with relative ease and is able to accurately reflect the complexity of subproblems at each node. The proposed approach does not require the time consuming statistic tests at each node and therefore does not suffer from as high computational burden as typical model selection algorithm. Our simulation results over several data sets indicate that our approach can achieve at least as good classification accuracy as statistical tests based model select algorithms, but in much faster speed.
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