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Artículos de revistas sobre el tema "Region of interest"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 29 de enero de 2023

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1

Huang, Mingming, Zhichun Mu y Hui Zeng. "Local interest region description using multiple support regions". Journal of Optics 44, n.º 3 (24 de abril de 2015): 290–97. http://dx.doi.org/10.1007/s12596-015-0257-6.

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2

Rudin, Stephen y Daniel R. Bednarek. "Region of interest fluoroscopy". Medical Physics 19, n.º 5 (septiembre de 1992): 1183–89. http://dx.doi.org/10.1118/1.596792.

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3

Lin, Huibao. "Hierarchical region-of-interest detection". Optical Engineering 45, n.º 7 (1 de julio de 2006): 077201. http://dx.doi.org/10.1117/1.2219104.

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4

Nister, David y Charilaos Christopoulos. "Lossless region of interest coding". Signal Processing 78, n.º 1 (octubre de 1999): 1–17. http://dx.doi.org/10.1016/s0165-1684(99)00044-4.

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5

Poldrack, Russell A. "Region of interest analysis for fMRI". Social Cognitive and Affective Neuroscience 2, n.º 1 (marzo de 2007): 67–70. http://dx.doi.org/10.1093/scan/nsm006.

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6

Liu, Jun Qing, Lei Ma, Yan Xiang, San Li Yi, Hong Lei Chen, Qian Zhang y Jian Feng He. "Image Evaluation Based on Region of Interest". Applied Mechanics and Materials 339 (julio de 2013): 253–58. http://dx.doi.org/10.4028/www.scientific.net/amm.339.253.

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Image quality assessment has broad applications in many fields, how to assess the quality of the image is an attractive research topic. In this paper, a ROIMDE method is proposed based on region of interest (ROI) and dual-scale edge structure similarity (SSIM), the quality assessment of the image is a weighted combination of ROI and non-ROI, the dual-scale edge structure similarity is used in ROI, and the classical structure similarity is applied in non-ROI. Experimental results show that, considering the influence of ROI, our model is more consistent with human subjective visual evaluation.

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7

Li, Dejun, Guiyang Zhou, Kang Cheng, Cheng Wang, Yifan Wang y Guanglong Li. "Spinneret Image Region of Interest Segmentation Algorithm". Journal of Physics: Conference Series 2068, n.º 1 (1 de octubre de 2021): 012023. http://dx.doi.org/10.1088/1742-6596/2068/1/012023.

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Abstract In order to improve the accuracy of spinneret defect detection, a spinneret image region of interest segmentation algorithm is proposed for the problem that the complex background of spinneret image interferes seriously with the subsequent detection. The mask image is obtained by separating the fixed plate area and the spinneret wall area using the diffuse water filling method, and the minimum external circle and the maximum internal circle in the mask image are found using contour detection to obtain the mask image of the spinneret area, and then the spinneret area, i.e. the Region of Interest (ROI), is extracted. The experimental results show that this method can effectively separate the spinneret region and reduce the background interference.

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8

Popko, Maciej y Władysław Żakowski. "The region of interest — an historical survey". Miscellanea Geographica 8, n.º 1 (1 de marzo de 1998): 219–24. http://dx.doi.org/10.2478/mgrsd-1998-080127.

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9

Sanchez, V., A. Basu y M. K. Mandal. "Prioritized Region of Interest Coding in JPEG2000". IEEE Transactions on Circuits and Systems for Video Technology 14, n.º 9 (septiembre de 2004): 1149–55. http://dx.doi.org/10.1109/tcsvt.2004.833168.

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10

Jerbi, A., Jian Wang y S. Shirani. "Error-resilient region-of-interest video coding". IEEE Transactions on Circuits and Systems for Video Technology 15, n.º 9 (septiembre de 2005): 1175–81. http://dx.doi.org/10.1109/tcsvt.2005.852619.

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11

Kalukin, A. R., D. T. Keane y W. G. Roberge. "Region-of-interest microtomography for component inspection". IEEE Transactions on Nuclear Science 46, n.º 1 (1999): 36–41. http://dx.doi.org/10.1109/23.747765.

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12

Saikia, Manob Jyoti y Rajan Kanhirodan. "Region-of-interest diffuse optical tomography system". Review of Scientific Instruments 87, n.º 1 (enero de 2016): 013701. http://dx.doi.org/10.1063/1.4939054.

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13

Salti, Samuele, Alioscia Petrelli, Federico Tombari, Nicola Fioraio y Luigi Di Stefano. "Traffic sign detection via interest region extraction". Pattern Recognition 48, n.º 4 (abril de 2015): 1039–49. http://dx.doi.org/10.1016/j.patcog.2014.05.017.

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14

MARCHETTE, D. J., J. L. SOLKA, R. GUIDRY y J. GREEN. "THE ADVANCED DISTRIBUTED REGION OF INTEREST TOOL". Pattern Recognition 31, n.º 12 (diciembre de 1998): 2103–18. http://dx.doi.org/10.1016/s0031-3203(98)00025-9.

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15

Wang, Qiyue, Yao Lu, Xiaoke Zhang y James Hahn. "Region of interest selection for functional features". Neurocomputing 422 (enero de 2021): 235–44. http://dx.doi.org/10.1016/j.neucom.2020.10.009.

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16

Gentle, D. J. y N. M. Spyrou. "Region of interest tomography in industrial applications". Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 299, n.º 1-3 (diciembre de 1990): 534–37. http://dx.doi.org/10.1016/0168-9002(90)90838-w.

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17

Askelöf, Joel, Mathias Larsson Carlander y Charilaos Christopoulos. "Region of interest coding in JPEG 2000". Signal Processing: Image Communication 17, n.º 1 (enero de 2002): 105–11. http://dx.doi.org/10.1016/s0923-5965(01)00026-1.

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18

Rani, T. Johncy y S. Suja Priyadharsini. "Region of Interest Tracking In Video Sequences". International Journal of Computer Applications 3, n.º 7 (10 de junio de 2010): 32–36. http://dx.doi.org/10.5120/740-1046.

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19

Hansen, Michael S., Souheil J. Inati y Peter Kellman. "Noise propagation in region of interest measurements". Magnetic Resonance in Medicine 73, n.º 3 (13 de marzo de 2014): 1300–1308. http://dx.doi.org/10.1002/mrm.25194.

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20

Karger, Graeme y Matt Symes. "Learning Disability Special Interest Group (Southern Region)". FPID Bulletin: The Bulletin of the Faculty for People with Intellectual Disabilities 9, n.º 4 (diciembre de 2011): 15–17. http://dx.doi.org/10.53841/bpsfpid.2011.9.4.15.

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21

Symes, Matt. "Learning Disability Special Interest Group (Southern Region)". FPID Bulletin: The Bulletin of the Faculty for People with Intellectual Disabilities 8, n.º 1-2 (2010): 35–36. http://dx.doi.org/10.53841/bpsfpid.2010.8.1-2.35.

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22

Bondarenko, Yulia, Solomiya Ohinok, Artur Kisiołek y Oleh Karyy. "Interest in universities based on search queries on the Internet". Innovative Marketing 17, n.º 3 (28 de septiembre de 2021): 179–90. http://dx.doi.org/10.21511/im.17(3).2021.15.

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The improvement of global Internet access and the COVID-19 pandemic, which necessitated mass testing of online teaching methods, have forwarded the competition between higher education institutions from the regional level and the struggle for the rich student into the competition for students in all countries. The paper aims to determine the influence of the rating of higher education institutions on the interest of Internet users by conducting a comparative analysis of the popularity of the official names of higher education institutions in search queries in Ukraine and Poland. To do this, a comparative analysis of the change in the interest in leading higher education institutions in Ukraine and Poland in search queries in the Google search engine is carried out. The analysis is performed using the Google Trends web application. As a result, it is found that a high position of the university in the national ranking does not guarantee more search queries about it on the Internet by both national Internet users and users from the neighboring country. In general, Internet users continue to be most interested in universities located in their region at the time of the search.

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23

Wu, Ya-lin y Soon-kak Kwon. "Transcoding Method of H.264 Coded Bitstream for Interest Region". International Journal of Virtual Reality 8, n.º 3 (1 de enero de 2009): 45–50. http://dx.doi.org/10.20870/ijvr.2009.8.3.2741.

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We propose a transcoding method of H.264 coded bitsteam to control the picture quality dependently on the interest region. In the proposed method, first we find the model of quantization step-size and bitrate. Then a classification method according to the subjectively interest region within a video sequence is suggested. Also we propose a method that assigns a specific quantization step-size differentially according to the interest region within a video. In general, the subjective picture quality can be increased by applying the quantization step-size as a small value relatively for the interest region compared with the other regions. From the simulation, we show that the proposed method can make better subjective picture quality relatively in parts of interest region.

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24

Yun, Chi-Hwan, Sun-Woo Ko y Geun-Ho Lee. "The Study about the Differential compression based on the ROI(Region Of Interest)". Journal of the Korea Institute of Information and Communication Engineering 18, n.º 3 (31 de marzo de 2014): 679–86. http://dx.doi.org/10.6109/jkiice.2014.18.3.679.

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25

Шамарова, Дарья Сергеевна y Светлана Ивановна Яковлева. "ASSESSMENT OF INTEREST IN TOURISM IN THE NORTHERN REGION". Вестник Тверского государственного университета. Серия: География и геоэкология, n.º 2(38) (17 de junio de 2022): 79–92. http://dx.doi.org/10.26456/2226-7719-2022-2-79-92.

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Цель исследования - методом онлайн-опроса выявить степень интереса жителей центральных районов России к туризму Мурманской области (Кольского полуострова). Научная новизна исследования: разработка анкеты, в которой совмещены вопросы, отражающие пространственные знания о северном регионе и интерес к арктическому региональному туризму. The purpose of the study is to use an online survey to identify the degree of interest of residents of the central regions of Russia in tourism in the Murmansk region (Kola Peninsula). Scientific novelty of the research: the development of a questionnaire that combines questions reflecting spatial knowledge about the northern region and interest in Arctic regional tourism.

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26

Högenauer, Anna-Lena. "All by themselves? Legislative regions and the use of unmediated access to the European level". European Political Science Review 6, n.º 3 (11 de noviembre de 2013): 451–75. http://dx.doi.org/10.1017/s1755773913000180.

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Since the 1980s, the level of activism of regions in European Union policy-making has greatly increased, leading to the emergence of claims that regional governments can and do bypass national government in European negotiations. However, two decades after the emergence of the concept, the debate about the ability of regions to engage successfully in this process of continuous negotiation and to represent their interests on the European stage is ongoing. Due to the scarcity of research looking at regional interest representation in concrete cases of policy-making, it has been difficult to establish to what extent and under which circumstances regions do rely on unmediated channels of interest representation on the European level. This article examines these questions through the activities of seven legislative regions during two negotiations of European Directives, as legislative regions have a wider choice of channels of interest representation. Overall, extensive use of unmediated access in regulatory policy-making is rare and can best be explained with reference to domestic conflict and the level of influence of a region in domestic European policy-making. Differences in the size of a region also influence the ability of a region to represent its interests in the coordination of the national position and at the European level.

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27

Md Jan, Mardhiyah, Nasharuddin Zainal y Shahrizan Jamaludin. "Region of interest-based image retrieval techniques: a review". IAES International Journal of Artificial Intelligence (IJ-AI) 9, n.º 3 (1 de septiembre de 2020): 520. http://dx.doi.org/10.11591/ijai.v9.i3.pp520-528.

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<span lang="EN-US">This paper presents a review of the region of interest-based (ROI) image retrieval techniques. In this study, the techniques, the performance evaluation parameters, and databases used in image retrieval process are being reviewed. A part of an image that is considered important or a selected certain area of the image is what defines a region of interest. Retrieval performance in large databases can be improved with the application of content-based image retrieval systems which deals with the extraction of global and region features of images. The capability of reflecting users' specific interests with greater accuracy has shown to be more effective when using region-based features compared to global features. Segmentation, feature extraction, indexing, and retrieval of an image are the tasks required in retrieving images that contain similar regions as specified in a query. The idea of the region of interest-based image retrieval concepts is presented in this paper and it is expected to accommodate researchers that are working in the region-based image retrieval system field. This paper reviews the work of image retrieval researchers in the span of twenty years. The main goal of this paper is to provide a comprehensive reference source for scholars involved in image retrieval based on ROI.</span>

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28

Su, Dongliang, Jian Wu, Zhiming Cui, Victor S. Sheng y Shengrong Gong. "CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor". Measurement Science Review 13, n.º 3 (1 de junio de 2013): 132–41. http://dx.doi.org/10.2478/msr-2013-0022.

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This paper proposes a novel invariant local descriptor, a combination of gradient histograms with contrast intensity (CGCI), for image matching and object recognition. Considering the different contributions of sub-regions inside a local interest region to an interest point, we divide the local interest region around the interest point into two main sub-regions: an inner region and a peripheral region. Then we describe the divided regions with gradient histogram information for the inner region and contrast intensity information for the peripheral region respectively. The contrast intensity information is defined as intensity difference between an interest point and other pixels in the local region. Our experimental results demonstrate that the proposed descriptor performs better than SIFT and its variants PCA-SIFT and SURF with various optical and geometric transformations. It also has better matching efficiency than SIFT and its variants PCA-SIFT and SURF, and has the potential to be used in a variety of realtime applications.

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29

SADR, Ali y Raziyeh Sadat OKHOVAT. "Extracting the region of interest from MFL signals". TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 427–34. http://dx.doi.org/10.3906/elk-1305-70.

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30

Zhang, Runzhe, Eric Maggard, Yousun Bang, Minki Cho y Jan Allebach. "Region of interest extraction for image quality assessment". Electronic Imaging 2020, n.º 9 (26 de enero de 2020): 321–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.9.iqsp-321.

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Print quality (PQ) is most important in the printing industry. To detect and analyze print defects is an effective solution to improve print quality. As the different types of print defects appear in different regions of interest (ROI) in the digital image of a scanned page, extracting the different ROIs helps to detect and analyze the printer defect. This paper proposes a method to extract different ROIs based on the digital image object map [1], which includes three different labels: raster (images or pictures), vector (background and smooth gradient color areas), and symbol (symbols and texts). Our ROI extraction method will extract four kinds of ROIs based on these three labeled objects. So we need to distinguish the background area and smooth gradient color area (color vector) from other vector objects. The process of the ROI extraction method includes four parts; and each part will extract one kind of ROI. For the color vector and background ROI extraction part, we develop two approaches: one is to obtain the maximum area rectangular ROI; and the other approach is to extract the deepest rectangular ROI. With both of these two methods, we use a greedy algorithm to gather additional useful ROIs. In the final result of the ROI extraction process, we only save the left top and right bottom positions for each ROI. In the end, we design a Matlab GUI Tool and label the ROI ground truth manually. We calculate the intersection over union (IoU)) between the ROI extraction result and the ROI manually labeled ground truth to evaluate our ROI extraction algorithm, and check whether it is good enough to crop different ROIs from the image of the scanned page to detect and analyze print defects.

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31

Chen, O. T. C. y Chih-Chang Chen. "Automatically-Determined Region of Interest in JPEG 2000". IEEE Transactions on Multimedia 9, n.º 7 (noviembre de 2007): 1333–45. http://dx.doi.org/10.1109/tmm.2007.906572.

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32

Kokare, Parmeshwar y Dr MasoodhuBanu. N.M. "Review on using Region of interest for HEVC". International Journal of Engineering & Technology 7, n.º 2.4 (10 de marzo de 2018): 93. http://dx.doi.org/10.14419/ijet.v7i2.4.11173.

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High efficiency video coding (HEVC) is the latest video compression standard. The coding efficiency of HEVC is 50% more than the preceding standard Advanced video coding (AVC). HEVC has gained this by introducing many advanced techniques such as adaptive block partitioning system known as quadtree, tiles for parallelization, improved entropy coding called Context-Adaptive Binary Arithmetic Coding (CABAC), 35 intra prediction modes (IPMs), etc. all these techniques have increased the complexity of encoding process due to which real time application of HEVC for video transfer is not yet convenient. The main objective of this paper is to provide a review of the recent developments in HEVC, particularly focusing on using region of interest (ROI) for reducing the encoding process time. Summaries of the different approaches to identify the ROI are discussed and a new method is explained.

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33

Tai-Chiu Hsung y D. P. K. Lun. "New sampling scheme for region-of-interest tomography". IEEE Transactions on Signal Processing 48, n.º 4 (abril de 2000): 1154–63. http://dx.doi.org/10.1109/78.827548.

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34

Yang, H., M. Long y H. M. Tai. "Region-of-interest image coding based on EBCOT". IEE Proceedings - Vision, Image, and Signal Processing 152, n.º 5 (2005): 590. http://dx.doi.org/10.1049/ip-vis:20041164.

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35

Boyle, Justin R. "Region-of-interest processing for electronic visual prostheses". Journal of Electronic Imaging 17, n.º 1 (1 de enero de 2008): 013002. http://dx.doi.org/10.1117/1.2841708.

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36

Schafer, Sebastian, Peter B. Noël, Alan M. Walczak y Kenneth R. Hoffmann. "Filtered region of interest cone-beam rotational angiography". Medical Physics 37, n.º 2 (21 de enero de 2010): 694–703. http://dx.doi.org/10.1118/1.3284540.

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37

Piccand, S., R. Noumeir y E. Paquette. "Region of Interest and Multiresolution for Volume Rendering". IEEE Transactions on Information Technology in Biomedicine 12, n.º 5 (septiembre de 2008): 561–68. http://dx.doi.org/10.1109/titb.2007.907986.

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38

Wang, Yue, Zeng Gang Lin y Zi Cheng Liao. "Image Quality Assessment Based on Region of Interest". Applied Mechanics and Materials 596 (julio de 2014): 350–54. http://dx.doi.org/10.4028/www.scientific.net/amm.596.350.

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In this paper a new No-Reference (NR) image quality assessment (IQA) method based on the point wise statistics of local normalized luminance signals using region of interest (ROI) processing is proposed. This algorithm firstly extracts the ROI which is relative to human subjectivity by using the image gradient and phase congruency, and then extracts the image quality feature in spatial domain. Particularly, most of the present IQA methods mainly focus on predicting the image quality with respect to human perception, yet, in some other image domains, the final receiver of a digital image may not a human. Thus, we propose a method which can assess the image quality relative to edge detection algorithm. In addition, experimental results on LIVE database are provided to justify the superior compared to the significant image quality metrics.

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39

Feng, Wu-Chi, Thanh Dang, John Kassebaum y Tim Bauman. "Supporting region-of-interest cropping through constrained compression". ACM Transactions on Multimedia Computing, Communications, and Applications 7, n.º 3 (agosto de 2011): 1–16. http://dx.doi.org/10.1145/2000486.2000491.

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40

Muntean, G. M., G. Ghinea y T. N. Sheehan. "Region of Interest-Based Adaptive Multimedia Streaming Scheme". IEEE Transactions on Broadcasting 54, n.º 2 (junio de 2008): 296–303. http://dx.doi.org/10.1109/tbc.2008.919012.

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41

Sahiner, B. y A. E. Yagle. "Region-of-interest tomography using exponential radial sampling". IEEE Transactions on Image Processing 4, n.º 8 (1995): 1120–27. http://dx.doi.org/10.1109/83.403417.

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42

de Crespigny, Alexander J. S., T. Adrian Carpenter y Laurance D. Hall. "Region-of-interest selection by outer-volume saturation". Journal of Magnetic Resonance (1969) 85, n.º 3 (diciembre de 1989): 595–603. http://dx.doi.org/10.1016/0022-2364(89)90250-3.

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43

Maaß, Clemens, Michael Knaup y Marc Kachelrieß. "New approaches to region of interest computed tomography". Medical Physics 38, n.º 6Part1 (25 de mayo de 2011): 2868–78. http://dx.doi.org/10.1118/1.3583696.

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44

Ebrahimi-Moghadam, Abbas y Shahram Shirani. "Matching Pursuit-Based Region-of-Interest Image Coding". IEEE Transactions on Image Processing 16, n.º 2 (febrero de 2007): 406–15. http://dx.doi.org/10.1109/tip.2006.888333.

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45

Senthil Rajan, A. "Image Reduction using Edge Based Region of Interest". IOP Conference Series: Materials Science and Engineering 225 (agosto de 2017): 012248. http://dx.doi.org/10.1088/1757-899x/225/1/012248.

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46

Ahmed, Sk Arif, Debi Prosad Dogra, Samarjit Kar, Byung-Gyu Kim, Paul Hill y Harish Bhaskar. "Localization of region of interest in surveillance scene". Multimedia Tools and Applications 76, n.º 11 (20 de julio de 2016): 13651–80. http://dx.doi.org/10.1007/s11042-016-3762-y.

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47

Eswaraiah, Rayachoti y Edara Sreenivasa Reddy. "Robust medical image watermarking technique for accurate detection of tampers inside region of interest and recovering original region of interest". IET Image Processing 9, n.º 8 (1 de agosto de 2015): 615–25. http://dx.doi.org/10.1049/iet-ipr.2014.0986.

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48

Li, Qianwen, Zhihua Wei y Cairong Zhao. "Optimized Automatic Seeded Region Growing Algorithm with Application to ROI Extraction". International Journal of Image and Graphics 17, n.º 04 (octubre de 2017): 1750024. http://dx.doi.org/10.1142/s0219467817500243.

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Region of interest (ROI) is the most important part of an image that expresses the effective content of the image. Extracting regions of interest from images accurately and efficiently can reduce computational complexity and is essential for image analysis and understanding. In order to achieve the automatic extraction of regions of interest and obtain more accurate regions of interest, this paper proposes Optimized Automatic Seeded Region Growing (OASRG) algorithm. The algorithm uses the affinity propagation (AP) clustering algorithm to extract the seeds automatically, and optimizes the traditional region growing algorithm by regrowing strategy to obtain the regions of interest where target objects are contained. Experimental results show that our algorithm can automatically locate seeds and produce results as good as traditional region growing with seeds selected manually. Furthermore, the precision is improved and the extraction effect is better after the optimization with regrowing strategy.

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49

Lavazza, Luigi y Sandro Morasca. "Considerations on the region of interest in the ROC space". Statistical Methods in Medical Research 31, n.º 3 (20 de diciembre de 2021): 419–37. http://dx.doi.org/10.1177/09622802211060515.

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Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

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Akram, Farhan, Jeong Heon Kim, Chan-Gun Lee y Kwang Nam Choi. "Segmentation of Regions of Interest Using Active Contours with SPF Function". Computational and Mathematical Methods in Medicine 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/710326.

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Segmentation of regions of interest is a well-known problem in image segmentation. This paper presents a region-based image segmentation technique using active contours with signed pressure force (SPF) function. The proposed algorithm contemporaneously traces high intensity or dense regions in an image by evolving the contour inwards. In medical image modalities these high intensity or dense regions refer to tumor, masses, or dense tissues. The proposed method partitions an image into an arbitrary number of subregions and tracks down salient regions step by step. It is implemented by enforcing a new region-based SPF function in a traditional edge-based level set model. It partitions an image into subregions and then discards outer subregion and partitions inner region into two more subregions; this continues iteratively until a stopping condition is fulfilled. A Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed segmentation algorithm has been applied to different images in order to demonstrate the accuracy, effectiveness, and robustness of the algorithm.

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