作者：
Perales, F.J.Fisher, R.UIB
Dept. of Computer Science and Mathematics Computer Graphics and Vision Group C/ Valldemossa Km. 7.5 PC 07122 Palma de Mallorca Spain University of Edinburgh
School of Informatics James Clerk Maxwell Building Mayfield Road Edinburgh EH9 3JZ United Kingdom
Typical gene expression clustering algorithms are restricted to a specific underlying pattern model while overlooking the possibility that other information carrying patterns may co-exist in the data. this may potenti...
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ISBN:
(纸本)0769525210
Typical gene expression clustering algorithms are restricted to a specific underlying pattern model while overlooking the possibility that other information carrying patterns may co-exist in the data. this may potentially lead to a large bias in the results. In this paper we discuss a new method that is able to cluster simultaneously various types of patterns. Our method is based on the observation that many of the patterns that are considered significant to infer gene function and regulatory mechanisms all share the geometry of linear manifolds.
there are a large number of reusable patterns in the software development process. these patterns are a summary of the experience of solving some problems in the software development process. Using these patterns can ...
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ISBN:
(纸本)9781728161365
there are a large number of reusable patterns in the software development process. these patterns are a summary of the experience of solving some problems in the software development process. Using these patterns can improve the efficiency of software development. this paper proposes a method of patternrecognition based on conditional constraints, which provides a feasible idea for procedure patternrecognition. this method uses the process blueprint tool to preprocess the Java source code, analyzes the structural characteristics of the process pattern and the variable reference position relationship characteristics, and uses regular expressions to construct feature matching rules. this paper verifies the accuracy and effectiveness of the procedure patternrecognition method based on conditional constraints by testing the procedure pattern examples.
this paper presents an effective approach for the application of Face recognition using Local Binary pattern operator. the face image is firstly divided in to the sub regions to generate the locally enhanced Local Bin...
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ISBN:
(纸本)9781479999910
this paper presents an effective approach for the application of Face recognition using Local Binary pattern operator. the face image is firstly divided in to the sub regions to generate the locally enhanced Local Binary Histogram, which provide the features information on pixel level by creating LBP labels for histogram. Global Local Binary Histogram for the entire face image is obtained by concatenating all the individual local histograms. As a pre-processing technique the differential excitation of pixel is used to make the algorithm invariant to the illumination changes. the performance of the algorithm is verified under constrains like pose, illumination and expression variation.
Electromyographic control is a technique that involve with withthe detection, processing and classification of the electromyogaphic signal that could be applied in human-assisting robots, prosthesis application or re...
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ISBN:
(纸本)9783642217289
Electromyographic control is a technique that involve with withthe detection, processing and classification of the electromyogaphic signal that could be applied in human-assisting robots, prosthesis application or rehabilitation devices. this paper reviews recent research and development in patternrecognition based electromyographic control systems. with an emphasis on patternrecognition control for prosthesis application. the various methods used in the different stages of the patternrecognition based control system are discussed in details.
Object detection is the most important algorithm in patternrecognition. However, there is plenty of challenging issue as the gap for algorithm improvement. In the case of the small object and partial occlusion detect...
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ISBN:
(纸本)9781728167916
Object detection is the most important algorithm in patternrecognition. However, there is plenty of challenging issue as the gap for algorithm improvement. In the case of the small object and partial occlusion detection in patternrecognition, it can be considered as the main interference for detector improvement. Our method is basing on the one-stage method, we are improving the detector performance by reconstructing the network withthe deconvolution method. Our work showed robustness results of small object detection challenges. the experiment is conducting by the one-stage and two-stage detector algorithms and datasets benchmark, the application utilized in the real scene for method validation and improvement.
A EEG signal-based emotion recognition system was designed. the system was developed to operate as a user-independent system, based on MAPL (minority affective picture library), EEG-signal database obtained from multi...
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ISBN:
(纸本)9781479932795
A EEG signal-based emotion recognition system was designed. the system was developed to operate as a user-independent system, based on MAPL (minority affective picture library), EEG-signal database obtained from multiple ethnic objections. the system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted. a simple experiment was carried out, and the classification result is about 56.4%, which indicated that minorities emotion problems can be studied based MPAL emotion recognition system.
In this paper, we present the ATM (Awesome Translation Machine), which translates handwriting texts in English into Chinese, and then provides its pronunciations in boththe two languages. Specifically, two types of t...
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ISBN:
(纸本)9781479942848
In this paper, we present the ATM (Awesome Translation Machine), which translates handwriting texts in English into Chinese, and then provides its pronunciations in boththe two languages. Specifically, two types of the databases that contain characters and sentences for training the ATM are constructed. Various signal processing techniques are employed sequentially for processing and analyzing the image raw data. After all the preparation stages, we apply multiple patternrecognition techniques, i.e., principle component analysis, linear discriminant analysis, and support vector machines, for the purpose of character recognition. the identified characters are thereby automatically linked to their actual meanings stored. Extensive experiments are conducted to gauge the performance for different techniques.
Discovering patterns from sequence data has significant impact in genomics, proteomics and business. A problem commonly encountered is that the patterns discovered often contain many redundancies resulted from fake si...
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ISBN:
(纸本)9783642160004
Discovering patterns from sequence data has significant impact in genomics, proteomics and business. A problem commonly encountered is that the patterns discovered often contain many redundancies resulted from fake significant patterns induced by their strong statistically significant subpatterns. the concept of statistically induced patterns is proposed to capture these redundancies. An algorithm is then developed to efficiently discover non-induced significant patterns from a large sequence dataset. For performance evaluation, two experiments were conducted to demonstrate a) the seriousness of the problem using synthetic data and b) top non-induced significant patterns discovered from Saccharomyces cerevisiae (Yeast) do correspond to the transcription factor binding sites found by the biologists. the experiments confirm the effectiveness of our method in generating a relatively small set of patterns revealing interesting, unknown information inherent in the sequences.
Associative models are Artificial Intelligence tools and have been used in many applications such as patternrecognition, classification, encryption, among others. In this paper we applied these models to trace a pers...
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ISBN:
(纸本)9781450361064
Associative models are Artificial Intelligence tools and have been used in many applications such as patternrecognition, classification, encryption, among others. In this paper we applied these models to trace a person in an indoor environment by the means of the power of a Wi-Fi signal. We deal withthis problem as a classification task. We used a preprocessing for the data to improve the results. Our performance was 95.75%.
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