The time complexity of the adaptive mean shift is related to the dimension of data and the number of iterations. The computational complexity will increase proportionally with the increase of the data dimension. An ap...
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The time complexity of the adaptive mean shift is related to the dimension of data and the number of iterations. The computational complexity will increase proportionally with the increase of the data dimension. An approximate neighborhood queries method is presented for the computation of high dimensional data, in which, the localitysensitive hashing(LSH) is used to reduce the computational complexity of the adaptive mean shift algorithm. Experimental results show that the proposed algorithm can reduce the complexity of the adaptive mean shift algorithm and can produce a more accurate classification than the fixed bandwidth mean shift algorithm.
An effective shape deformation method derived from a PCA-based statistical shape model (SSM) using the Golden Section Search (GSS) method is presented. The PCA-based SSM has proved to be a simple and effective method ...
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Interactive object segmentation is widely used for extracting any user-interested objects from natural images. A common problem with many interactive segmentation approaches is that the object segmentation quality is ...
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The Structural SIMilarity Measure (SSIM) combined with the sequential Monte Carlo approach has been shown [1] to achieve more reliable video object tracking performance, compared with similar methods based on colour a...
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ISBN:
(纸本)9781457705380
The Structural SIMilarity Measure (SSIM) combined with the sequential Monte Carlo approach has been shown [1] to achieve more reliable video object tracking performance, compared with similar methods based on colour and edge histograms and Bhattacharyya distance. However, the combined use of the structural similarity and a particle filter results in increased computational complexity of the algorithm. In this paper, a novel fast approach for video tracking based on the structural similarity measure is presented. The tracking algorithm proposed determines the state of the target (location, size) based on the gradient ascent procedure applied to the structural similarity surface of the video frame, thus avoiding computationally expensive sampling of the state space. The new method, while being computationally less expensive, has shown higher accuracy compared with the standard mean shift algorithm and the SSIM Particle Filter (SSIM-PF) [1] and its performance is illustrated over real video sequences.
With the explosion of protein sequences generated in the Post-Genomic Age, it is urgent to develop an automated method to predict protein quaternary structure. To explore this problem, we adopted an approach based on ...
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With the explosion of protein sequences generated in the Post-Genomic Age, it is urgent to develop an automated method to predict protein quaternary structure. To explore this problem, we adopted an approach based on a sequence encoding descriptor by fusing PseAA (Pseudo Amino Acid) and DC (Dipeptide Composition) representing a protein sample. Here, a completely different approach, manifold learning algorithm MVP (Maximum variance projection) is introduced to extract the key features from the high-dimensional feature space. The dimension-reduced descriptor vector thus obtained is a compact representation of the original high dimensional vector. Our jackknife test results indicate that it is very promising to use the dimensionality reduction approaches to cope with complicated problems in biological systems, such as predicting the quaternary structure of proteins.
In this paper, two novel soft subspace clustering algorithms, namely fuzzy weighting subspace clustering with competitive agglomeration (FWSCA) and entropy weighting subspace clustering with competitive agglomeration ...
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In this paper, two novel soft subspace clustering algorithms, namely fuzzy weighting subspace clustering with competitive agglomeration (FWSCA) and entropy weighting subspace clustering with competitive agglomeration (EWSCA), are proposed to overcome the problems of the unknown number of clusters and the initialization of prototypes for soft subspace clustering. The main advantage of FWSCA and EWSCA lies in the fact that they effectively integrate the merits of soft subspace clustering and the good properties of fuzzy clustering with competitive agglomeration. This makes it possible to obtain the appropriate number of clusters during the clustering progress. Moreover, FWSCA and EWSCA algorithms can converge regardless of the initial number of clusters and initialization. Substantial experimental results on both synthetic and real data sets demonstrate the effectiveness of FWSCA and EWSCA in addressing the two problems.
Object recognition from images is one of the essential problems in automatic imageprocessing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not...
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Object recognition from images is one of the essential problems in automatic imageprocessing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not necessarily related to image data. It has recently come to attention that high dimensional data also exhibit high hubness, which essentially means that some very influential data points appear and these points are referred to as hubs. Unsurprisingly, hubs play a very important role in the nearest neighbor classification. We examine the hubness of various image data sets, under several different feature representations. We also show that it is possible to exploit the observed hubness and improve the recognition accuracy.
To manage the increasing volume of data per time unit, achievements in information processing and artificial intelligence were made. But still the complex processes of human perception and scenario recognition are not...
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To manage the increasing volume of data per time unit, achievements in information processing and artificial intelligence were made. But still the complex processes of human perception and scenario recognition are not fully understood and still far from implementation in technical applications. The contribution of this article to the field of cognitive automation is the concept of prediction for perceptual- and scenario-recognition frameworks. It is a model where prediction originates from neuro-psychoanalytical theories. Inspired by experience-based planning, which is used by the psychoanalytical decision unit, the prediction of possible outcomes from scenarios can be used for proactive acting. It results in a higher detection rate and a faster performance for recognition-units. This first implementation shows the possibilities of the concept and gives an outlook of the performance as soon as the system is fully integrated in the decision-unit.
Coherence-enhancing diffusion (CED), based on analysis of oriented structures, has been extensively used in imageprocessing. This diffusion filtering can keep some junctions and close broken linear structures, but it...
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Coherence-enhancing diffusion (CED), based on analysis of oriented structures, has been extensively used in imageprocessing. This diffusion filtering can keep some junctions and close broken linear structures, but it also destroys crease points and deforms nonlinear structures. In this paper, we proposed an improved algorithm for CED based on analysis of structure tensor and Hessian matrix. This approach can not only denoise and enhance linear structures such as edges, but also preserve nonlinear structures such as creases. Experiments with fingerprint image show that the improved CED outperforms classical CED.
Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Tr...
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Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Traditional deblurring algorithms have been proposed to work for natural-scene images. However the natural-scene images are not consistent with document images. In this paper, the distinct characteristics of document images are investigated. We propose a content-aware prior for document image deblurring. It is based on document image foreground segmentation. Besides, an upper-bound constraint combined with total variation based method is proposed to suppress the rings in the deblurred image. Comparing with the traditional general purpose deblurring methods, the proposed deblurring algorithm can produce more pleasing results on document images. Encouraging experimental results demonstrate the efficacy of the proposed method.
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