Self-supervised learning has gained significant attention in contemporary applications, particularly due to the scarcity of labeled data. While existing SSL methodologies primarily address feature variance and linear ...
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A 1.5V resistive fuse for image smoothing and segmentation using bulk-driven MOSFETs is presented. The circuit switches on only if the differential voltage applied across its input terminals is less than a set voltage...
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A 1.5V resistive fuse for image smoothing and segmentation using bulk-driven MOSFETs is presented. The circuit switches on only if the differential voltage applied across its input terminals is less than a set voltage;it switches off if the differential voltage is higher than the set value. The useful operation range of the circuit is 0.4V with a supply voltage of 1.5V and threshold voltages of V-Tn = 0.828V and V-Tp = -0.56V for n and g channel MOSFETs, respectively.
This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies' four-tap filter, an original image is decomposed into three detail images and one approximate i...
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This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies' four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.
This paper discusses the relationship between the zero crossings or zeros of band-limited signals and their nonlinear transformations. It is proved that the bandwidth of a signal can be compressed by a ratio of 1/ n i...
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This paper discusses the relationship between the zero crossings or zeros of band-limited signals and their nonlinear transformations. It is proved that the bandwidth of a signal can be compressed by a ratio of 1/ n if and only if the signal has n th-order zero crossings or zeros (if complex). Also, a monotonic nonlinearity in the observation of a band-limited signal can be identified from the zero crossings (or zeros) of the derivative of the observed signal. (The results are for one-dimensional signals. Extensions to two-dimensional signals remain to be addressed.)
Moving object segmentation is an important step toward development of any computervision systems. In the present work, we have proposed a new method for segmentation of moving objects, which is based on single change...
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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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The ability to perceive human emotions is one of the key elements that may promise a natural, genuine and more reliable human robot interaction. Though emotional perception in human robot interaction has been challeng...
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Skew and motion blur are significant challenges when camera and scene of interest are in two different media. Skew occurs due to spatially varying refraction on a dynamic water surface, whereas motion blur results fro...
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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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In this paper, we present a new system to segment and label CT/MRI Brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled ...
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ISBN:
(纸本)0780341236
In this paper, we present a new system to segment and label CT/MRI Brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be apdated to real-time imaging scenarios.
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