Weak boundary contrast, inhomogeneous background and overlapped intensity distributions of the object and background are main causes that may lead to failure of boundary detection for many traditional active contour m...
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ISBN:
(纸本)9781479923427
Weak boundary contrast, inhomogeneous background and overlapped intensity distributions of the object and background are main causes that may lead to failure of boundary detection for many traditional active contour methods. In this paper, we propose a region-based active contour model to address these problems in both local and global ways. A localized active contour framework is developed, in which two local boundary measures are introduced for the evolution of the level set function. These measures are used to select the boundary candidates for boundary preservation such that the evolution of the contour is guided in a reasonable way. The object boundary is determined by a global boundary measure which evaluates the boundary completeness during the entire evolution process. The experiments demonstrate that our method works well against weak boundary contrast, inhomogeneous background and overlapped intensity distributions.
Aiming at the disadvantages of the traditional off-line vector-based learning algorithm, this paper proposes a kind of Incremental Tensor Principal Component Analysis (ITPCA) algorithm. It represents an image as a ten...
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DNA sequences recognition is a key problem in bioinformatics and biomedical informatics. In this paper, we solve this problem by use of the probability method and metric instead of traditional frequency metric because...
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ISBN:
(纸本)9781479927623
DNA sequences recognition is a key problem in bioinformatics and biomedical informatics. In this paper, we solve this problem by use of the probability method and metric instead of traditional frequency metric because the characters in DNA alphabet set meet the Markov properties. For this purpose, transition probabilities, transition matrixes, and log odds ratios are defined. And then, we put forward our sequence recognition algorithm based on the Markov model (SRM), which has better performance on time complexity than some sequence alignment algorithms in the same field. The results of the contrast experiments show that our SRM algorithm can recognize DNA sequences correctly and effectively without any ambiguities.
In order to settle incremental learning and preserve the space information of images, this paper proposes an incremental tensor discriminant analysis for facial image detection. The proposed algorithm employs tensor r...
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pattern matching is a fundamental application text retrieval, string query, biological sequence analysis, etc. Therefore, the effective algorithm performing this kind of matching is in great need. In this paper, the w...
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pattern matching is a fundamental application text retrieval, string query, biological sequence analysis, etc. Therefore, the effective algorithm performing this kind of matching is in great need. In this paper, the wildcard is defines to match any one character in a sequence. Multiple wildcards form a gap. The length of a flexible gap is arbitrary. We design CLPM algorithm by use of cross list index structure to realize pattern matching with flexible wildcard gaps. The preprocessing algorithm is designed to initialize cross list so as to reduce searching space. In CLPM algorithm, the effective intervals is defined and computed based on the start positions of each sub pattern in each string, which help to obtain matching result set. Moreover, the approximate pattern matching is converted to short extract pattern matching. The contrast experiments are done based on DBLP tile data set. The results show that CLMP algorithm has better performance in the same fields.
In this paper, we present a novel audio fingerprinting method based on N-grams, which can quickly identify a segment of audio even when the audio signals are seriously distorted. We make use of N peaks in spectrum to ...
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In this paper, we present a novel audio fingerprinting method based on N-grams, which can quickly identify a segment of audio even when the audio signals are seriously distorted. We make use of N peaks in spectrum to form the audio fingerprint, which accelerates the retrieval speed greatly. We take advantage of the initial robust peaks to calculate the similarity between candidates and the input audio, which improves the retrieval accuracy significantly. The effectiveness of the N-gram method was evaluated on a music database of 10,000 songs. Experimental results show that the proposed approach outperforms two state-of-the-art algorithms (Shazam and Philips Robust Hash) in both effectiveness (in terms of retrieval accuracy) and efficiency (in terms of average retrieval time).
pattern Mining is a popular issue in biological sequence analysis. With the introduction of wildcard gaps, more interesting patterns can be mined. In this paper, we propose a new definition related to pattern frequenc...
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pattern Mining is a popular issue in biological sequence analysis. With the introduction of wildcard gaps, more interesting patterns can be mined. In this paper, we propose a new definition related to pattern frequency, under which the Apriori property holds. We define a pattern mining problem called Ming top-K Frequent patterns (MFP), where gaps are mined instead of specified. Compared with existing problems, MFP does not require any domain knowledge of the user. However, theoretical analysis and experimental results show that MFP favors inflexible patterns. We then define another problem where the flexibility threshold of each gap is specified by the user. The problem is called Mining top-K Frequent and Flexible patterns (MF 2 P). We develop algorithm with polynomial complexities for both problems. patterns can grow from both sides. Some interesting biological patterns mined by our algorithms are discussed.
Raisins grade identification in China still relies on photoelectric sorting and manual separation, also, the function of management system for the production, processing, and sales of raisin is traditional and simple....
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This paper focuses mainly on adaptive dictionary updating and abnormality detection via weighted space coding in video surveillance. Generally, abnormality analysis conducted on a large amount of video data is very co...
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ISBN:
(纸本)9781479923427
This paper focuses mainly on adaptive dictionary updating and abnormality detection via weighted space coding in video surveillance. Generally, abnormality analysis conducted on a large amount of video data is very complicated, time-consuming and time-variant. However, our dictionary is very efficient at following up on shifted contents in video and abandoning old inactive information in time. The adaptability characteristic also helps reduce the dictionary's size to a small scale, since it only needs to keep recent or active information. We also introduce a simple, but effective, judgement criterion for abnormal detection based on sparse coding over weighted bases. Because of the condensed dictionary and the simplified judgment criterion, our algorithm performs online learning and online detection with a high speed and a high accuracy in various scenes.
The study provides a design scheme for the mountain landslide monitoring system based on wireless sensor networks. Different sensors are mainly used to collect data about the water depth in the soil and the sloping an...
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