Result 1 to 20 of 50 total
A parallel bi-directional self-organizing neural network (PBDSONN) architecture for color image extraction and segmentation. (English)
Neurocomputing 86, 1-23 (2012).
1
The visual extent of an object suppose we know the object locations. (English)
Int. J. Comput. Vis. 96, No. 1, 46-63 (2012).
2
A comparative study of object-level spatial context techniques for semantic image analysis. (English)
Comput. Vis. Image Underst. 115, No. 9, 1288-1307 (2011).
3
Learning image context for segmentation of prostate in CT-guided radiotherapy. (English)
Fichtinger, Gabor (ed.) et al., Medical image computing and computer-assisted intervention ‒ MICCAI 2011. 14th international conference, Toronto, Canada, September 18‒22, 2011. Proceedings, Part III. Berlin: Springer (ISBN 978-3-642-23625-9/pbk). Lecture Notes in Computer Science 6893, 570-578 (2011).
4
Latent visual context learning for web image applications. (English)
Pattern Recognition 44, No. 10-11, 2263-2273 (2011).
5
Spatiotemporal context integration in robot vision. (English)
Int. J. Humanoid Robotics 7, No. 3, 357-377 (2010).
6
A quadsection algorithm for grammar-based image compression. (English)
Kim, Tai-hoon (ed.) et al., Future generation information technology. Second international conference, FGIT 2010, Jeju Island, Korea, December 13‒15, 2010. Proceedings. Berlin: Springer (ISBN 978-3-642-17568-8/pbk). Lecture Notes in Computer Science 6485, 234-248 (2010).
7
Context-based embedded image compression using binary wavelet transform. (English)
Image Vis. Comput. 28, No. 6, 991-1002 (2010).
8
Operator context scanning to support high segmentation rates for real time license plate recognition. (English)
Pattern Recognition 43, No. 11, 3866-3878 (2010).
9
A visual shape descriptor using sectors and shape context of contour lines. (English)
Inf. Sci. 180, No. 16, 2925-2939 (2010).
10
CAPIRE: A context-aware points of interest recognition system using a CBIR approach. (English)
Zeadally, Sherali (ed.) et al., Future multimedia networking. Third international workshop, FMN 2010, Kraków, Poland, June 17‒18, 2010. Proceedings. Berlin: Springer (ISBN 978-3-642-13788-4/pbk). Lecture Notes in Computer Science 6157, 112-122 (2010).
11
Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation. (English)
Boll, Susanne (ed.) et al., Advances in multimedia modeling. 16th international multimedia modeling conference, MMM 2010, Chongqing, China, January 6‒8, 2010. Proceedings. Berlin: Springer (ISBN 978-3-642-11300-0/pbk). Lecture Notes in Computer Science 5916, 229-239 (2010).
12
SAR image segmentation based on mixture context and wavelet hidden-class-label Markov random field. (English)
Comput. Math. Appl. 57, No. 6, 961-969 (2009).
13
TextonBoost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. (English)
Int. J. Comput. Vis. 81, No. 1, 2-23 (2009).
14
Structural context for object categorization. (English)
Muneesawang, Paisarn (ed.) et al., Advances in multimedia information processing ‒ PCM 2009. 10th Pacific Rim conference on multimedia, Bangkok, Thailand, December 15‒18, 2009. Proceedings. Berlin: Springer (ISBN 978-3-642-10466-4/pbk). Lecture Notes in Computer Science 5879, 280-291 (2009).
15
Spatial error concealment: a novel exemplar-based approach using segmentation. (English)
Comput. Electr. Eng. 35, No. 4, 536-548 (2009).
16
Statistical image modeling in the contourlet domain using contextual hidden Markov models. (English)
Signal Process. 89, No. 5, 946-951 (2009).
17
Combining global, regional and contextual features for automatic image annotation. (English)
Pattern Recognition 42, No. 2, 259-266 (2009).
18
Context-dependent segmentation of retinal blood vessels using hidden Markov models. (English)
Sarbazi-Azad, Hamid (ed.) et al., Advances in computer science and engineering. 13th international CSI computer conference, CSICC 2008, Kish Island, Iran, March 9‒11, 2008. Revised selected papers. Berlin: Springer (ISBN 978-3-540-89984-6/pbk; 978-3-540-89985-3/ebook). Communications in Computer and Information Science 6, 348-355 (2008).
19
Multi-stage FCM-based intensity inhomogeneity correction for MR brain image segmentation. (English)
Kůrková, Véra (ed.) et al., Artificial neural networks ‒ ICANN 2008. 18th international conference, Prague, Czech Republic, September 3‒6, 2008. Proceedings, Part II. Berlin: Springer (ISBN 978-3-540-87558-1/pbk). Lecture Notes in Computer Science 5164, 527-536 (2008).
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