CEPST algorithm is an important step in imageprocessing, which is widely used in computervision, patternrecognition, and machine learning. In order to improve the efficiency and performance of CEPST algorithm, more...
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This work introduces clustering to electrical tomography (ET) and first compares the effectiveness of different unsupervised clustering categories in tomographic object detection. First, the one-step linear back proje...
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ISBN:
(数字)9798331540043
ISBN:
(纸本)9798331540050
This work introduces clustering to electrical tomography (ET) and first compares the effectiveness of different unsupervised clustering categories in tomographic object detection. First, the one-step linear back projection (LBP) algorithm is applied to reconstruct a rough image. Then, clustering is introduced to detect and refine the objects in the image, and a higher-quality image is obtained based on the clustering results. Six unsupervised clustering algorithms belonging to different categories, including K-means, Mini Batch K-means, Agglomerative, density peak clustering (DPC), statistical information grid (STING), gaussian mixture model (GMM), are compared from the aspects of clustering evaluation index, reconstruction quality and reconstruction efficiency. Simulation was carried out based on a capacitively coupled electrical resistance tomography (CCERT) system to collect the projection data under the cases of one to three objects to be detected in the sensing region. The research results show that the quality of object detection and reconstruction can be effectively improved by post-processing the image with clustering. It is found that for the single-object distribution, the grid-based clustering algorithm STING provides images with the highest quality. While for the multi-objects distributions (two or three objects), the Agglomerative algorithm belonging to the hierarchy clustering category shows advantage in achieving good images. Concerning the real-time performance, Agglomerative clustering algorithm is also preferred with less computational time than most of the other clustering algorithms. Therefore, among the combinations investigated in this work, LBP + Agglomerative clustering has the overall best performance in topographic object detection, in regarding of the reconstruction quality and efficiency.
According to the relevant provisions of the "Election Law of the People's Republic of China", the engineering software required for the implementation of large-scale conference elections can set voting r...
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Malaysia is the largest exporter of tropical woods in the world, accounting for 70 percent of the world's supply of raw-logs. Sabah and Sarawak, the two Malaysian states on the island of Borneo, occupies some of t...
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ISBN:
(纸本)9780769541587
Malaysia is the largest exporter of tropical woods in the world, accounting for 70 percent of the world's supply of raw-logs. Sabah and Sarawak, the two Malaysian states on the island of Borneo, occupies some of the oldest and the most diverse rain forest in the world. Malaysia has a rich variety of tree species, and the wood produced from each of these has unique structure, physical and mechanical properties. The differences in woods structure and properties allow for the manufacture of woods based products with many different appearances and uses. In order to use this precious material efficiently, proper species must be used in the appropriate places. Intelligent Woods species recognition is a new application studied in the computervision field to help prevent misclassifying of woods species in woods industries. Woods recognition is an implementation on identifying the different species of woods provided with the images captured for the woods samples or the characteristics observed. In this study, the features from the enhanced woods images are extracted using the LBP histogram, which determines the classification between the various woods species. The recognition is performed using a nearest neighbor classifier in the computed feature space with Chi square as a dissimilarity measure. The intelligent woods recognition system is designed to explore the possibility of developing a system which is able to perform automated woods recognition based on woods anatomy. The result thus obtained shows a high rate of recognition accuracy proving that the techniques due to its rotation invariance and robustness to gray-scale variations are very promising for practical applications.
It is not a surprise that imageprocessing is a growing research field. vision in general andimages in particular have always played an important and essential role in human life. Not only as a way to communicate, bu...
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ISBN:
(纸本)9783540770459
It is not a surprise that imageprocessing is a growing research field. vision in general andimages in particular have always played an important and essential role in human life. Not only as a way to communicate, but also for commercial, scientific, industrial and military applications. Many techniques have been introduced and developed to deal with all the challenges involved with imageprocessing. In this paper, we will focus on techniques that find their origin in fuzzy set theory and fuzzy logic. We will show the possibilities of fuzzy logic in applications such as image retrieval, morphology and noise reduction by discussing some examples. Combined with other state-of-the-art techniques they deliver a useful contribution to current research.
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