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Books on the topic 'And Object Segmentation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Xu, Ning, Weiyao Lin, Xiankai Lu, and Yunchao Wei. Video Object Segmentation. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-44656-6.

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2

Grace, Alan Edward. Adaptive segmentation for aspect invariant object recognition. Universityof Birmingham, 1993.

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3

Fisher, Robert B. Identity independent object segmentation in 2.5D sketch data. University of Edinburgh Departmentof Artificial Intelligence, 1986.

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4

Guo, Ju, and C. C. Jay Kuo. Semantic Video Object Segmentation for Content-Based Multimedia Applications. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1503-6.

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5

Jay, Kuo C. C., ed. Semantic video object segmentation for content-based multimedia applications. Kluwer Academic Publishers, 2001.

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6

Guo, Ju. Semantic video object segmentation for content-based multimedia applications. Kluwer Academic Publishers, 2001.

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7

Guo, Ju. Semantic Video Object Segmentation for Content-Based Multimedia Applications. Springer US, 2002.

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8

Yi, Li. Object segmentation in image sequences using motion and color information. National Library of Canada, 1999.

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9

Batra, Dhruv, Adarsh Kowdle, Devi Parikh, Jiebo Luo, and Tsuhan Chen. Interactive Co-segmentation of Objects in Image Collections. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1915-0.

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10

Batra, Dhruv. Interactive Co-segmentation of Objects in Image Collections. Springer Science+Business Media, LLC, 2011.

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11

name, No. From fragments to objects: Segmentation and grouping in vision. Elsevier Science, 2001.

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12

F, Shipley Thomas, and Kellman Philip J, eds. From fragments to objects: Segmentation and grouping in vision. Elsevier, 2001.

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13

Ahsan, Unaiza, Hanyan Li, and Matthew Hagen. Comprehensive Review of Modern Object Segmentation Approaches. Now Publishers, 2022.

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14

Lin, Weiyao, Xiankai Lu, and Yunchao Wei. Video Object Segmentation: Tasks, Datasets, and Methods. Springer International Publishing AG, 2023.

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15

Ngan, King Ngi, and Hongliang Li. Video Segmentation and Its Applications. Springer, 2011.

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16

Cooray, Saman Hemantha. Semi-automatic video object segmentation for multimedia applications. 2003.

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17

Wang, Xiaogang. Deep Learning in Object Recognition, Detection, and Segmentation. Now Publishers, 2016.

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18

Ngan, King Ngi, and Hongliang Li. Video Segmentation and Its Applications. Springer, 2011.

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19

Ngan, King Ngi, and Hongliang Li. Video Segmentation and Its Applications. Springer, 2014.

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20

Teutsch, Michael. Moving Object Detection and Segmentation for Remote Aerial Video Surveillance. Saint Philip Street Press, 2020.

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21

Moving Object Detection and Segmentation for Remote Aerial Video Surveillance. KIT Scientific Publishing, 2015.

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22

Teutsch, Michael. Moving Object Detection and Segmentation for Remote Aerial Video Surveillance. Saint Philip Street Press, 2020.

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23

Zhou, Kevin. Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches. Elsevier Science & Technology Books, 2015.

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24

Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches. Elsevier Science & Technology Books, 2015.

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25

MacLean, Wallace James. Recovery of egomotion and segmentation of independent object motion using the EM-algorithm. 1996.

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26

Shiffrar, Maggie. The Aperture Problem. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0076.

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The accurate visual perception of an object’s motion requires the simultaneous integration of motion information arising from that object along with the segmentation of motion information from other objects. When moving objects are seen through apertures, or viewing windows, the resultant illusions highlight some of the challenges that the visual system faces as it balances motion segmentation with motion integration. One example is the barber pole Illusion, in which lines appear to translate orthogonally to their true direction of emotion. Another is the illusory perception of incoherence whe

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27

Kuo, C. C. Jay, and Ju Guo. Semantic Video Object Segmentation for Content- Based Multimedia Applications (The Springer International Series in Engineering and Computer Science). Springer, 2001.

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28

Pham, Van Vung, and Tommy Dang. Hands-On Computer Vision with Detectron2: Develop Object Detection and Segmentation Models with a Code and Visualization Approach. de Gruyter GmbH, Walter, 2023.

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29

Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, Deep RL, Unsupervised Learning, Object Detection and Segmentation, and More. de Gruyter GmbH, Walter, 2020.

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30

Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, Deep RL, Unsupervised Learning, Object Detection and Segmentation, and More, 2nd Edition. Packt Publishing, Limited, 2020.

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31

Edwards, Martin R. Employer Branding and Talent Management. Edited by David G. Collings, Kamel Mellahi, and Wayne F. Cascio. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198758273.013.7.

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This chapter explores the intersection between employer branding and talent management. In considering this intersection, it reflects upon the phenomenon of human resources (HR) practice differentiation in the context of both employer branding and talent management. In particular, it considers some similarities between brand management programs that are likely to differentiate HR practices based on perceived talent versus employer-brand segmentation that is more likely to differentiate HR practices on the basis of employee needs and wants. The chapter also reflects upon the potential implicati

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32

Sun, Changming, Hugues Talbot, Sebastien Ourselin, and Tony Adriaansen, eds. Digital Image Computing: Techniques and Applications. CSIRO Publishing, 2003. http://dx.doi.org/10.1071/9780643090989.

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Digital Image Computing: Techniques and Applications is the premier biennial conference in Australia on the topics of image processing and image analysis. This seventh edition of the proceedings has seen an unprecedented level of submission, on such diverse areas as: Image processing; Face recognition; Segmentation; Registration; Motion analysis; Medical imaging; Object recognition; Virtual environments; Graphics; Stereo-vision; and Video analysis. These two volumes contain all the 108 accepted papers and five invited talks that were presented at the conference. These two volumes p

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33

Batra, Dhruv, Adarsh Kowdle, and Devi Parikh. Interactive Co-segmentation of Objects in Image Collections. Springer, 2011.

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34

From Fragments to Objects - Segmentation and Grouping in Vision. Elsevier, 2001. http://dx.doi.org/10.1016/s0166-4115(01)x8019-9.

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35

Field, Chris, ed. How Humans Recognize Objects: Segmentation, Categorization and Individual Identification. Frontiers Media SA, 2016. http://dx.doi.org/10.3389/978-2-88919-940-2.

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36

Kellman, Philip J., and Thomas F. Shipley. From Fragments to Objects: Segmentation and Grouping in Vision. Elsevier Science & Technology Books, 2001.

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37

Caplovitz, Gideon P., Alex Boswell, and Kyle Killebrew. The Bar-Cross-Ellipse Illusion. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0012.

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This chapter describes a multistable stimulus that reveals the complexity of visual processing that underlies the determination of an object’s form and motion. The stimulus is constructed by placing an ellipse on a uniform background and then partially occluding it with four squares, each with the same color as the background. When the ellipse is made to rotate, it can be perceived in any of four distinct ways, and, over time, the percept will switch between them. Each percept corresponds to a distinct figure-ground segmentation that is determined on the basis of contour ownership and how diff

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