An emotion recognition system based on processing.of multiple physiological signals is presented. Our algorithm is developed and verified based on multiple subjects by presenting multimodal stimuli that were elaborate...
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An emotion recognition system based on processing.of multiple physiological signals is presented. Our algorithm is developed and verified based on multiple subjects by presenting multimodal stimuli that were elaborated to effectively induce emotions, and thus not dependent on single specific user. The system utilizes physiological signals that can be acquired without discomfort from body surface. Support vector machine was introduced as pattern classifier to overcome the difficulty of large overlap among clusters and large variance within cluster. Correct classification ratio was 77.8 and 61.2%, for emotion recognition problem of three and four categories, respectively. Another advantage of our system is that it requires shorter signal monitoring time than previous ones, and thus better suited for practical use.
The proceedings contain 87 papers. The topics discussed include: evaluating integrated speech- and image understanding;a tracking framework for collaborative human computer interaction;lecture and presentation trackin...
ISBN:
(纸本)0769518346
The proceedings contain 87 papers. The topics discussed include: evaluating integrated speech- and image understanding;a tracking framework for collaborative human computer interaction;lecture and presentation tracking in an intelligent meeting room;3-D N-best search for simultaneous recognition of distant-talking speech of multiple talkers;integration of tone related feature for Chinese speech recognition;talking heads: which matching between faces and synthetic voices?;robust noisy speech recognition with adaptive frequency bank selection;context-based multimodal input understanding in conversational systems;referring to objects with spoken and haptic modalities;human - robot interaction: engagement between humans and robots for hosting activities;viewing and analyzing multimodal human-computer tutorial dialogue: a database approach;prosody based co-analysis for continuous recognition of coverbal gestures;and a multi-class patternrecognition system for practical finger spelling translation.
Lung cancer is one of the deadly and most common diseases in the world. Radiologists fail to diagnose small pulmonary nodules in as many as 30% of positive cases. Many methods have been proposed in the literature such...
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Lung cancer is one of the deadly and most common diseases in the world. Radiologists fail to diagnose small pulmonary nodules in as many as 30% of positive cases. Many methods have been proposed in the literature such as neural network algorithms. Recently, support vector machines (SVM)'s had received an increasing attention for patternrecognition. The advantage of SVM lies in better modeling the recognition process. The objective of this paper is to apply support vector machines (SVM)'s for classification of lung nodules. The SVM classifier is trained with features extracted from 30 nodule images and 20 non-nodule images, and is tested with features out of 16 nodule/non-nodule images. The sensitivity of SVM classifier is found to be 87.5%. We intend to automate the pre-processing.detection process to further enhance the overall classification.
We propose a text scanner, which detects wide text strings in a sequence of scene images. For scene text detection, we use a multiple-CAMShift algorithm on a text probability image produced by a multi-layer perceptron...
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We propose a text scanner, which detects wide text strings in a sequence of scene images. For scene text detection, we use a multiple-CAMShift algorithm on a text probability image produced by a multi-layer perceptron...
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An innovative inspection method to assess the condition of sewer pipes is proposed in this paper. The standard sewer inspection technique, based on closed-circuit television systems, has a relatively poor performance;...
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An innovative inspection method to assess the condition of sewer pipes is proposed in this paper. The standard sewer inspection technique, based on closed-circuit television systems, has a relatively poor performance;a video camera is mounted on a robot and the video recording is provided off-line to an engineer who classifies any defects. The focus of this research is the automated identification and location of discontinuities in the internal surface of sewers. The transducer used is an assembly of a CCD camera and optical elements to generate a ring-shaped laser pattern. The automated inspection method consists of several stages including the segmentation of the image into characteristic geometric features and potential defect regions. Automatic recognition, rating and classification of pipe defects are carried out by means of the computation of a partial histogram based on adaptive imageprocessing.techniques. Experiments in a realistic environment have been conducted and results are presented.
Edge detection is important in many fields such as patternrecognition and computer vision. Many edge detection methods are sensitive to noises because gradients are used to enhance edges. To solve this problem, a new...
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ISBN:
(纸本)0819447145
Edge detection is important in many fields such as patternrecognition and computer vision. Many edge detection methods are sensitive to noises because gradients are used to enhance edges. To solve this problem, a new edge detection method is proposed in the paper based on local self-similarity. For any pixel in an image, a metric, called as local self-similar coefficient, is defined on its square neighborhood. The square neighborhood blocks are classified into three types: edge block, smooth block and random block. Two theorems have been proven according to the self-similar metric definition and the image block classification. The theorems and experimental results demonstrate that the local self-similar coefficients on edge blocks and smooth blocks are much greater than that on random blocks. Fortunately, it is quite easy to distinguish edge blocks from smooth blocks. A new edge detection algorithm based on these properties is provided in the paper. Several kinds of images, including human pictures and natural scenery, are used to detect edges with the new algorithm, and satisfactory results are obtained. The results show that under noisy conditions, the new algorithm extracts better edges than Sobel method.
A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss func...
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Methods for mobile robot localization that use eigen spaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which...
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Analysis and understanding of medical images has important clinical values for patient diagnosis and treatment, as well as technical implications for computer vision and patternrecognition. One of the most fundamenta...
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ISBN:
(纸本)0819445576
Analysis and understanding of medical images has important clinical values for patient diagnosis and treatment, as well as technical implications for computer vision and patternrecognition. One of the most fundamental issues is the detection of object boundaries or singularities, which is often the basis for further processes such as organ/tissue recognition, image registration, motion analysis, measurement of anatomical and physiological parameters, etc. The focus of this work involved taking a correlation based approach toward edge detection, by exploiting some of desirable properties of wavelet analysis. This leads to the possibility of constructing a bank of detectors, consisting of multiple wavelet basis functions of different scales which are optimal for specific types of edges, in order to optimally detect all the edges in an image. Our work involved developing a set of wavelet functions which matches the shape of the ramp and pulse edges. The matching algorithm used focuses on matching the edges in the frequency domain. It was proven that this technique could create matching wavelets applicable at all scales. Results have shown that matching wavelets can be obtained for the pulse edge while the ramp edge requires another matching algorithm.
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