1,721,017 research outputs found
Multicomponent image segmentation: A comparative analysis between a hybrid genetic algorithm and self-organizing maps
No full textImage segmentation is an essential process in image analysis. Several methods have been developed to segment multicomponent images and the success of these methods depends on the characteristics of the acquired image and the percentage of imperfections in the process of its acquisition. Many of the segmentation methods are parametric, which means that many parameters need to be computed or provided before the segmentation process, and any method that works on one type of multicomponent image cannot necessarily work on another. In addition, many segmentation methods are supervised, where a priori knowledge is needed, such as the number of classes. To overcome these obstacles, a self-organizing map (SOM), which is an unsupervised nonparametric method, was used to segment four different types of multicomponent images (Landsat, SPOT, IKONOS and CASI), and the results compared to those of a new nonparametric unsupervised genetic algorithm (GA) for image segmentation. To improve the performance of the GA, a hill-climbing process and another random heuristic module were added to escape the local-minima trap and to improve the speed of the GA; the new algorithm is called the hybrid genetic algorithm (HGA). Verification of the results was performed using two different techniques: field verification and the functional model. These verification techniques show that the HGA is more accurate in multicomponent image segmentation than the SOM.ARIA E, 1973, P 20 INT SOC PHOT RE, P117; BAKER EB, 1987, P 2 INT C GEN ALG L, P14; BHANU B, 1995, IEEE T SYST MAN CYB, V25, P1543, DOI 10.1109-21.478442; BRICE CR, 1970, ARTIF INTELL, V1, P205, DOI 10.1016-0004-3702(70)90008-1; CHANG YL, 1994, IEEE T IMAGE PROCESS, V3, P868; Chun DN, 1996, PATTERN RECOGN, V29, P1195, DOI 10.1016-0031-3203(95)00148-4; Cohen J, 1960, EDUC PSYCHOL MEAS, V20, P46; COLLET C, 1995, GRESTI STUDY RES GRO, V2, P569; CONGALTON RG, 1991, REMOTE SENS ENVIRON, V37, P35, DOI 10.1016-0034-4257(91)90048-B; Cormen T., 2001, INTRO ALGORITHMS; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; Haupt R L, 2004, PRACTICAL GENETIC AL; Holland J. H., 1975, ADAPTATION NATURAL A; Jiang T., 2001, ELECT NOTES THEORETI, V46, P1; KHUNKAY S, 1997, P 1997 INT C INF COM, V2, P713; Kim EY, 2000, IEEE SIGNAL PROC LET, V7, P301, DOI 10.1109-97.873564; KIM HJ, 1998, ELECTRON LETT, V34, P1394; Kohavi R., 1998, APPL MACHINE LEARNIN, V30, P271; Kohonen T., 2001, SPRINGER SERIES INFO, V30; Levine M. D., 1985, VISION MAN MACHINE; Lo Bosco G, 2001, 11TH INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING, PROCEEDINGS, P262; Ng SC, 1996, IEEE SIGNAL PROC MAG, V13, P38, DOI 10.1109-79.543974; OHLANDER R, 1978, COMPUT VISION GRAPH, V8, P313, DOI 10.1016-0146-664X(78)90060-6; OJOLA T, 1998, PATTERN RECOGN, V19, P1213; PARZEN E, 1962, ANN MATH STAT, V33, P1065, DOI 10.1214-aoms-1177704472; Pham DL, 2000, ANNU REV BIOMED ENG, V2, P315, DOI 10.1146-annurev.bioeng.2.1.315; Pratt WK, 1991, DIGITAL IMAGE PROCES; Schalkoff R.J, 1992, PATTERN RECOGNITION; Shapiro L., 2001, COMPUTER VISION; Xu BG, 2002, AATCC REV, V2, P42; Yao KC, 2000, PATTERN RECOGN, V33, P1575, DOI 10.1016-S0031-3203(99)00135-1; YIN HJ, 1995, NEURAL COMPUT, V7, P1178, DOI 10.1162-neco.1995.7.6.1178; Yoshimura M, 1999, PATTERN RECOGN, V32, P2041, DOI 10.1016-S0031-3203(99)00004-7; ZHANG P, 2003, P IEEE C EV COMP CEC, P634; Zouagui T, 2004, PATTERN RECOGN, V37, P1785, DOI 10.1016-j.patcog.2003.12.01462
Multicomponent image segmentation using a genetic algorithm and artificial neural network
No full textImage segmentation is an essential process for image analysis. Several methods were developed to segment multicomponent images, and the success of these methods depends on several factors including 1) the characteristics of the acquired image and 2) the percentage of imperfections in the process of image acquisition. The majority of these methods require a priori knowledge, which is difficult to obtain. Furthermore, they assume the existence of models that can estimate its parameters and fit to the given data. However, such a parametric approach is not robust, and its performance is severely affected by the correctness of the utilized parametric model. In this letter, a new multicomponent image segmentation method is developed using a nonparametric unsupervised artificial neural network called Kohonen's self-organizing map (SOM) and hybrid genetic algorithm (HGA). SOM is used to detect the main features that are present in the image; then, HGA is used to cluster the image into homogeneous regions without any a priori knowledge. Experiments that are performed on different satellite images confirm the efficiency and robustness of the SOM-HGA method compared to the Iterative Self-Organizing DATA analysis technique (ISODATA). © 2007 IEEE.ARIA EH, 2004, P 20 ISPRS C IST TUR, P117; AWAD M, IN PRESS INT J REMOT; BACAO F, 2005, P ICCS 2005 C, P476; Baker J. E., 1987, P 2 INT C GEN ALG, P14; CHEN Q, 2004, LECT NOTES COMPUT SC, V33, P621; Chun DN, 1996, PATTERN RECOGN, V29, P1195, DOI 10.1016-0031-3203(95)00148-4; Fauzi M., 2003, P BRIT MACH VIS C, P519; HOLLLAND J, 1975, ADAPT NATURAL ARTIFI; HUAPT R, 2004, PRACTICAL GENETIC AL; Jensen J. R., 1996, INTRO DIGITAL IMAGE; Kohavi R., 1998, APPL MACHINE LEARNIN, V30, P271; Levine M. D., 1985, VISION MAN MACHINE; NEVATIA R, 1980, COMPUT VISION GRAPH, V13, P257, DOI 10.1016-0146-664X(80)90049-0; Ng SC, 1996, IEEE SIGNAL PROC MAG, V13, P38, DOI 10.1109-79.543974; PARZEN E, 1962, ANN MATH STAT, V33, P1065, DOI 10.1214-aoms-1177704472; PERKINS S, 2000, FUZZY SYST EVOL COMP, V3, P52; Pina P, 2003, INT GEOSCI REMOTE SE, P3516; PRATT W, 1991, DIGITA IMAGE PROCESS; Tou J.T., 1974, PATTERN RECOGNITION; Wang X., 2004, P IEEE C ROB AUT MEC, P991; Xiaoying Jin, 2003, Proceedings of the 12th IEEE International Conference on Fuzzy Systems (Cat. No.03CH37442); Xu BG, 2002, AATCC REV, V2, P42; Yao KC, 2000, PATTERN RECOGN, V33, P1575, DOI 10.1016-S0031-3203(99)00135-1; YIN HJ, 1995, NEURAL COMPUT, V7, P1178, DOI 10.1162-neco.1995.7.6.117834232
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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