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Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifie r. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on...
In image classification, multi-scale information is usually combined by concatenating features or selecting scales. Their main drawbacks are that concatenation increases the feature dimensionality by the number of scales and scale selection typically loses the information from other scales. We propose to solve this problem by the dissimilarity representation as it enables to combine various sources...
Image segmentation is very essential and critical to image processing and pattern recognition. It is known that, color image segmentation approaches are based on monochrome segmentation approaches operating in different color spaces. So in this paper, an improved method which uses the FSVM (fuzzy support vector machines) algorithm for color image segmentation in the HSI (hue-saturation-intensity)...
In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learn assignment, slow convergence, and local minimal in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, it has been proved that the model is time-consuming...
Since non-linearity is obviously characteristic of natural gas analysis, and few ideal spectrum data samples can be actually obtained from the mass natural gas, the accuracy level of concentrating each component in the natural gas turns out to be far from high. In response to the dilemma above, a multi-level- and SVM-subset- based infrared spectrum analyzing method is proposed for the analysis of...
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