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Robust Statistical Approach for Extraction of Moving Human Silhouettes from Videos  [PDF]
Oinam Binarani Devi,Nissi S. Paul,Y. Jayanta Singh
Computer Science , 2014, DOI: 10.5121/ijit.2014.3306
Abstract: Human pose estimation is one of the key problems in computer vision that has been studied in the recent years. The significance of human pose estimation is in the higher level tasks of understanding human actions applications such as recognition of anomalous actions present in videos and many other related applications. The human poses can be estimated by extracting silhouettes of humans as silhouettes are robust to variations and it gives the shape information of the human body. Some common challenges include illumination changes, variation in environments, and variation in human appearances. Thus there is a need for a robust method for human pose estimation. This paper presents a study and analysis of approaches existing for silhouette extraction and proposes a robust technique for extracting human silhouettes in video sequences. Gaussian Mixture Model (GMM) A statistical approach is combined with HSV (Hue, Saturation and Value) color space model for a robust background model that is used for background subtraction to produce foreground blobs, called human silhouettes. Morphological operations are then performed on foreground blobs from background subtraction. The silhouettes obtained from this work can be used in further tasks associated with human action interpretation and activity processes like human action classification, human pose estimation and action recognition or action interpretation.
Building a protein name dictionary from full text: a machine learning term extraction approach
Lei Shi, Fabien Campagne
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-88
Abstract: We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text.This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.Knowledge discovery and data mining in the biological literature have been attracting more and more interest [1,2]. Automated text mining can facilitate the efforts of both biological database curators [2], and of biologists who consult the literature to acquire novel information both within and outside of their immediate expertise. Text mining applications come in various styles. Some rely on statistical methods to detect unusually strong co-occurrences between genes or gene products (e.g., PubGene [3] and as described by Wilkinson et al. [4]). Other applications aim to extract precise information from the text, for instance protein mutations [5] or interactions [6,7]. A new promising type of application, pioneered by Textpresso [8], consists of portals that help end-users locate information more effectively.Most text mining applications require the ability to identify and classify words, or multi-word terms, that authors use in an article to refer to biological entities (biological entities include, but are not limited to, genes and their products, cell types, and biological processes). This task is called named entity recognition and has been well studied in computer science for problems
An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification
G. Sofia, P. Tarolli, F. Cazorzi,G. Dalla Fontana
Hydrology and Earth System Sciences (HESS) & Discussions (HESSD) , 2011,
Abstract: A statistical approach to LiDAR derived topographic attributes for the automatic extraction of channel network and for the choice of the scale to apply for parameter evaluation is presented in this paper. The basis of this approach is to use distribution analysis and statistical descriptors to identify channels where terrain geometry denotes significant convergences. Two case study areas with different morphology and degree of organization are used with their 1 m LiDAR Digital Terrain Models (DTMs). Topographic attribute maps (curvature and openness) for various window sizes are derived from the DTMs in order to detect surface convergences. A statistical analysis on value distributions considering each window size is carried out for the choice of the optimum kernel. We propose a three-step method to extract the network based (a) on the normalization and overlapping of openness and minimum curvature to highlight the more likely surface convergences, (b) a weighting of the upslope area according to these normalized maps to identify drainage flow paths and flow accumulation consistent with terrain geometry, (c) the standard score normalization of the weighted upslope area and the use of standard score values as non subjective threshold for channel network identification. As a final step for optimal definition and representation of the whole network, a noise-filtering and connection procedure is applied. The advantage of the proposed methodology, and the efficiency and accurate localization of extracted features are demonstrated using LiDAR data of two different areas and comparing both extractions with field surveyed networks.
Correction: A linear classifier based on entity recognition tools and a statistical approach to method extraction in the protein-protein interaction literature  [cached]
Louren?o Anália,Conover Michael,Wong Andrew,Nematzadeh Azadeh
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-180
Abstract: Correction to A. Louren o, M. Conover, A. Wong, A. Nematzadeh, F. Pan, H. Shatkay, and L.M. Rocha."A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature". BMC Bioinformatics 2011, 12(Suppl 8):S12. doi:http://10.1186/1471-2105-12-S8-S12.
Preference Learning in Terminology Extraction: A ROC-based approach  [PDF]
Jér?me Azé,Mathieu Roche,Yves Kodratoff,Michèle Sebag
Computer Science , 2005,
Abstract: A key data preparation step in Text Mining, Term Extraction selects the terms, or collocation of words, attached to specific concepts. In this paper, the task of extracting relevant collocations is achieved through a supervised learning algorithm, exploiting a few collocations manually labelled as relevant/irrelevant. The candidate terms are described along 13 standard statistical criteria measures. From these examples, an evolutionary learning algorithm termed Roger, based on the optimization of the Area under the ROC curve criterion, extracts an order on the candidate terms. The robustness of the approach is demonstrated on two real-world domain applications, considering different domains (biology and human resources) and different languages (English and French).
A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature  [PDF]
Anália Louren?o,Michael Conover,Andrew Wong,Azadeh Nematzadeh,Fengxia Pan,Hagit Shatkay,Luis M. Rocha
Computer Science , 2011,
Abstract: We participated, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challenge. For the ACT, we pursued an extensive testing of available Named Entity Recognition and dictionary tools, and used the most promising ones to extend our Variable Trigonometric Threshold linear classifier. For the IMT, we experimented with a primarily statistical approach, as opposed to employing a deeper natural language processing strategy. Finally, we also studied the benefits of integrating the method extraction approach that we have used for the IMT into the ACT pipeline. For the ACT, our linear article classifier leads to a ranking and classification performance significantly higher than all the reported submissions. For the IMT, our results are comparable to those of other systems, which took very different approaches. For the ACT, we show that the use of named entity recognition tools leads to a substantial improvement in the ranking and classification of articles relevant to protein-protein interaction. Thus, we show that our substantially expanded linear classifier is a very competitive classifier in this domain. Moreover, this classifier produces interpretable surfaces that can be understood as "rules" for human understanding of the classification. In terms of the IMT task, in contrast to other participants, our approach focused on identifying sentences that are likely to bear evidence for the application of a PPI detection method, rather than on classifying a document as relevant to a method. As BioCreative III did not perform an evaluation of the evidence provided by the system, we have conducted a separate assessment; the evaluators agree that our tool is indeed effective in detecting relevant evidence for PPI detection methods.
Pattern Based Term Extraction Using ACABIT System  [PDF]
Koichi Takeuchi,Kyo Kageura,Teruo Koyama,Béatrice Daille,Laurent Romary
Computer Science , 2009,
Abstract: In this paper, we propose a pattern-based term extraction approach for Japanese, applying ACABIT system originally developed for French. The proposed approach evaluates termhood using morphological patterns of basic terms and term variants. After extracting term candidates, ACABIT system filters out non-terms from the candidates based on log-likelihood. This approach is suitable for Japanese term extraction because most of Japanese terms are compound nouns or simple phrasal patterns.
Ontoloy Relationship Extraction Research Based on HowNet and Term Relevancy Degree
基于知网和术语相关度的本体关系抽取研究*

Fu JibinLiu JieJia KeliangMao Jintao,
傅继彬刘杰
,贾可亮,毛金涛

现代图书情报技术 , 2008,
Abstract: The paper proposes a relationship extraction method based on HowNet and term relevancy degree.Firstly syntax parsing tools are used to extract context feature of terms,and natural language feature and statistical mutual information measure are integrated to compute relevancy degree of terms,then dynamic role and sememe are used as key to seek the relationship in HowNet semantic relationship framework,and explicit semantic lable is designated to the relationship.Experimental results show that the approach is effective.
Indium recovery from acidic aqueous solutions by solvent extraction with D2EHPA: a statistical approach to the experimental design
Fortes, M.C.B.;Martins, A.H.;Benedetto, J.S.;
Brazilian Journal of Chemical Engineering , 2003, DOI: 10.1590/S0104-66322003000200005
Abstract: this experimental work presents the optimization results of obtaining a high indium concentration solution and minimum iron poisoning by solvent extraction with d2ehpa solubilized in isoparaffin and exxsol. the variables studied in the extraction step were d2ehpa concentration, acidity of the aqueous phase and time of contact between phases. different hydrochloric and sulfuric acid concentrations were studied for the stripping step. the optimum experimental conditions resulted in a solution with 99% indium extraction and less than 4% iron. the construction of a mccabe-thiele diagram indicated two theoretical countercurrent stages for indium extraction and at least six stages for indium stripping. finally, the influence of associated metals found in typical sulfate leach liquors from zinc plants was studied. under the experimental conditions for maximum indium extraction, 96% indium extraction was obtained, iron extraction was about 4% and no ga, cu and zn were co-extracted.
Indium recovery from acidic aqueous solutions by solvent extraction with D2EHPA: a statistical approach to the experimental design  [cached]
Fortes M.C.B.,Martins A.H.,Benedetto J.S.
Brazilian Journal of Chemical Engineering , 2003,
Abstract: This experimental work presents the optimization results of obtaining a high indium concentration solution and minimum iron poisoning by solvent extraction with D2EHPA solubilized in isoparaffin and exxsol. The variables studied in the extraction step were D2EHPA concentration, acidity of the aqueous phase and time of contact between phases. Different hydrochloric and sulfuric acid concentrations were studied for the stripping step. The optimum experimental conditions resulted in a solution with 99% indium extraction and less than 4% iron. The construction of a McCabe-Thiele diagram indicated two theoretical countercurrent stages for indium extraction and at least six stages for indium stripping. Finally, the influence of associated metals found in typical sulfate leach liquors from zinc plants was studied. Under the experimental conditions for maximum indium extraction, 96% indium extraction was obtained, iron extraction was about 4% and no Ga, Cu and Zn were co-extracted.
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