Anomalous sound detection (ASD) encounters difficulties with domain shift, where the sounds of machines in target domains differ significantly from those in source domains due to varying operating conditions. Existing...
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We propose a new technique in which line segments and elliptical arcs are used as features for recognizing image patterns. By using this approach, the process of locating a model in a given image is efficient since th...
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We propose a technique to inpaint large missing regions in range images. Such a technique can be used to restore degraded/occluded range maps. It can also serve to reconstruct dense depth maps from sparse measurements...
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This paper presents the performance evaluation of different segmentation algorithms for medical images. Accuracy and clarity are very important issues for medical imaging and same in the case with segmentation. In thi...
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Given an off-the-shelf camera, one has the freedom to move the camera or play around with its intrinsic parameters such as zoom or aperture settings. We propose a framework for depth estimation from a set of calibrate...
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When used for tracking, the combination of infrared (IR) and an internal measurement unit (IMU) allows researchers and industry to locate objects to within 1 cm at over 200 Hz with a latency less than 2 ms. This novel...
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In this paper we address the issue of locating non-standard Malaysian car license plate. Instead of searching the region for the plate, we directly locate the alphanumeric characters of the car plate. In this manner, ...
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Spectral clustering has been used in computervision successfully in recent years, which refers to the algorithm that the global optima is found in the relaxed continuous domain obtained by eigen decomposition, and th...
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Spectral clustering has been used in computervision successfully in recent years, which refers to the algorithm that the global optima is found in the relaxed continuous domain obtained by eigen decomposition, and then a multi-class clustering problem should be solved by traditional clustering algorithm such as k-means. In this paper, we propose a novel spectral clustering algorithm based on particle swarm optimization (PSO). The major contribution of this work is to combine PSO technique with spectral clustering. In the multi-class clustering stage, the PSO is applied in the feature space to cluster the new data, each of which is a characterization of the original data. Experimental studies on PSO-based spectral clustering algorithm demonstrate that the proposed algorithm provides global convergence, steady performance and better accuracy.
Domes are architectural structural elements typical for ecclesiastical and secular grand buildings, like churches, mosques, palaces, capitols and city halls. The current paper targets the problem of segmentation of do...
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In this paper, we present a gender estimation technique that will determine whether a person looking at the camera is a male or female. Several facial features extracted from a face are passed to post-image-processing...
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