S. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and bayes/MDL for multiband image segmentation, " PAMI, vol. 18, pp. 884--900, september 1996. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
First 50 documents Next 50
On Region Merging: The Statistical Soundness of Fast Sorting.. - Nielsen, Nock (Correct)
....so far a tantalizing and central problem for image processing. Roughly speaking, the problem can be presented as the transformation of the collection of pixels of an image into a meaningful arrangement of regions and objects. There are four large categories of approaches to image segmentation [1], one of which is of direct interest to us : region growing and merging techniques. In region merging, regions are sets of pixels with homogeneous properties and they are iteratively grown by combining smaller regions or pixels, pixels being elementary regions. Region growing merging techniques ....
....techniques. In region merging, regions are sets of pixels with homogeneous properties and they are iteratively grown by combining smaller regions or pixels, pixels being elementary regions. Region growing merging techniques usually work with a statistical test to decide the merging of regions [1]. A merging predicate uses this test, and builds the segmentation on the basis of (essentially) local decisions. Figure 1. A natural RGB image and the segmentation found by our segmentation method (regions white bordered averaged inside) This locality in decisions has to preserve global ....
[Article contains additional citation context not shown here]
S.-C. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and bayes/MDL for multiband image segmentation," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, pp. 884--900, 1996.
Automatic Analysis of Fetal Echographic Images - Jardim, Figueiredo (2002) (Correct)
.... a great amount of research [10] 11] 12] 13] 14] Although most limitations of the original formulation have been successfully addressed, only special purpose approaches have been able to deal with ultrasound images [7] Active contour models (or their probabilistic reformulations [7, 12, 15]) require careful tuning of several involved parameters, such as those controlling the trade off between smoothness robustness and estimation accuracy. This fact limits the use of these methods for practical medical imaging applications. Moreover, the quality of fetal ultra sound images is often ....
....the parameter vector #, that is ######, where # is a set of parameters describing the contour shape. Under the maximum likelihood criterion, the best estimate of #, denoted # ML ,isgivenby # ML # ### ### ####### (1) To derive the likelihood function ######, we adopt a regionbased model [7, 9, 12, 14, 15], which is an approach known to be robust with respect to local artifacts and poor image quality. Region based models are based on spatial characteristics of homogeneity, where the term homogeneity does not necessarily mean that the pixels in a given region have identical intensities, but that the ....
S. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 884--900, 1996.
Generating Discriminating Cartoon Faces Using Interacting Snakes - Hsu, Jain (Correct)
....topology of close curves. Besides the edge energy, region energy has been introduced to improve the segmentation results for homogeneous objects in both the parametric and the GAC approaches (e.g. region and edge [16] GAC without edge [6] statistical region snake [7] region competition [27], and active region model [12] Multiple active contours [2] 5] have been proposed to extract partition multiple homogeneous regions that do not overlap with each other in an image. We utilize face detection results (face and eye locations) to initialize multiple snakes that represent the ....
S.C. Zhu and A. Yuille, "Region competition - unifying snakes, region growing, and Bayes/MDL for multiband image segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, 1996.
Complexity Regularized Shape Estimation From Noisy Fourier Data - Natalia Schmid Yoram (2002) (Correct)
....there in) are based on penalized likelihoodtechniques. The major limitation of many of these methods is their non adaptiveness. In this work, we use Rissanen s MDL criterion [6] It is one of fully automated estimation principles which are broadly applied in signal, image, and contour estimation [3 5]. Since both functions composing are periodic on , the boundary estimate can be represented using a Fourier series. The Fourier coefficients that describe a two dimensional boundary, are called Fourier descriptors (FD) cf. 7] and references there in) and the parametrization can be used ....
Song Chun Zhu and Alan Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multi-band Image Segmentation," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, 1996.
General Scheme of Region Competition Based on Scale Space - Tang, Ma (Correct)
....[21] proposed a morphological segmentation approach based on watershed. Besl and Jain [3] presented a segmentation method through variable order surface fitting. Pavlidis and Liow [25] designed a method to integrate region growing and edge detection to produce smoother contours. Zhu and Yuille [31] proposed a statistical algorithm called region competition to segment images. Their algorithm combines the attractive aspects of both active contour and region growing to achieve segmentation with regular boundaries, yet, without small holes. 0162 8828 01 10.00 2001 IEEE In this paper, we ....
....growing to achieve segmentation with regular boundaries, yet, without small holes. 0162 8828 01 10.00 2001 IEEE In this paper, we present a general scheme of region competition (GSRC) which combines the attractive aspects of scale space based clustering [26] and the region competition algorithm [31]. First, GSRC utilizes a novel classification algorithm to cluster n dimensional image feature data according to the generally defined peaks under a certain scale and makes use of a scale space based classification scheme to classify the pixels by grouping the resultant feature data clusters into ....
[Article contains additional citation context not shown here]
S.C. Zhu and A.L. Yuille, "Region Competition: Unifying Snakes, Region Growing and Bayes/MDL for Multiband Image Segmentation, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
Region-Based Active Contours For Video Object.. - Jehan-Besson.. (2001) (1 citation) (Correct)
....The introduction of such descriptors is interesting for two reasons. First, for a given application like detection of moving objects, various descriptors can be easily tested inside the same theoretical framework. Second, this framework can be applied to other applications [6] Some authors [7, 8, 9, 10] have proposed a way of adding region based terms in the evolution equation of an active contour. These pioneer works are complementary and show the potential of region based approaches. However, they are made for particular applications with particular descriptors. Moreover, all proofs leading to ....
....complementary and show the potential of region based approaches. However, they are made for particular applications with particular descriptors. Moreover, all proofs leading to the evolution equation of the active contour are based on the derivation of the criterion using Euler Lagrange equations [7, 10] and the dynamical scheme is introduced after the computation of the derivative. With such a method, the case of descriptors depending on the evolution of the curve, i.e. depending upon features globally attached to the region, cannot readily be taken into account. In this paper, we introduce a ....
S. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and bayes/MDL for multiband image segmentation," IEEE PAMI, vol. 18, pp. 884--900, september 1996.
Region Growing Colour Image Segmentation Applied To Face - Detection Grinias Mavrikakis (Correct)
....techniques [5]# (e) global optimization approaches based on energy functions or Bayesian [6] and Minimum Description Length (MDL) 7] criteria. An interesting approach to automatic image segmentation using a combination of region based and contour based techniques is proposed byZhu and Yuille [8]. Considering colour segmentation, the problem which arises from the light reflectance on polished surfaces (such as metal objects or the human skin) is that in most of the perceptual colour systems large lightness variations on object surfaces appear, whichmaynot necessarily be accompanied ....
S.C. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation," IEEE Trans. on Pattern Analysis and Machine Intelligence,vol. 18, pp. 884--900, Sept. 1996.
A Region-Based Fuzzy Feature Matching Approach to Content-Based .. - Chen, Wang (2002) (4 citations) (Correct)
....accuracy and robustness (against inaccurate image segmentation) by using a similarity measure capable of representing imprecision that stems from imperfect segmentation 1. 2 Overview of Our Approach Semantically precise image segmentation by an algorithm is very difficult [18] 26] 33] [35]. However, a single glance is sufficient for a human to identify circles, straight lines, and other complex objects in a collection of points and to produce a meaningful assignment between objects and points in the image. Although those points cannot always be assigned unambiguously to objects, ....
S.C. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
Looking Beyond Region Boundaries: Region-Based Image Retrieval.. - Chen, Wang (Correct)
....of inaccurate segmentation by smoothing over the imprecision. The scheme is implemented in the SIMPLIcity system [12] Nevertheless, the inaccuracies (or uncertainties) are not explicitly expressed in the IRM measure. Semantically precise image segmentation by an algorithm is very difficult [13] [14]. However, a single glance is sufficient for human to identify circles, straight lines, and other complex objects in a collection of points and to produce a meaningful assignment between objects and points in the image. Although those points cannot always be assigned unambiguously to objects, ....
S.C. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, no. 9, pp. 884--900, 1996.
Image Segmentation Using Curve Evolution And Region Stability - Baris Sumengen Manjunath (Correct)
....They start with an initial closed contour and modify the curve according to the statistics of the interior and exterior of this contour. Region based methods use global image features as opposed to the local features used in edge based methods. Developments in region based active contours [9] are more recent than their edge based counterparts. Regionbased active contours are less dependent on the initial location of the contour since they don t rely much on the local image features. Also not needing to use the gradient of the image simplifies both the variational formulation and its ....
S.C. Zhu, A. L. Yuille, "Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation", PAMI 1996.
Unsupervised Segmentation of Color-Texture Regions in Images.. - Deng, Manjunath (2001) (9 citations) (Correct)
....the problem of unsupervised segmentation is ill defined because seman tic objects do not usually correspond to homogeneous spatio temporal regions in color, texture, or motion. Some of the recent work in image segmentation include stochastic model based approaches [1] 6] 13] 17] 24] [25], morphological watershed based region growing [18] energy diffusion [ 14] and graph partitioning [20] The work on video segmentation include motion based segmentation [3] 19] 21] 23] spatial segmentation and motion tracking [8] 22] moving objects extraction [12] 15] and region ....
S.C. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and Bayes/ MDL for multiband image segmentation," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no. 9, p. 884-900. 17
Applications of the "Creep--and--Merge" System: Corner.. - Antranig Basman Joan (1998) (Correct)
....currently suffer from poor computational performance and generality, and often fail to deliver the level of robustness and automation which this alternative technology promises. Two region based segmentation paradigms (the snakes balloons framework put forward in [7, 4] and generalised in [13], and split and merge link, presented in [8, 1] and generalised in [11] can be used to classify the majority of region based approaches, but these suffer from various problems. The former are still predominantly local methods, and also suffer from troublesome discretisation of curvature and ....
....number of recursive passes through the image, it is easy for them to overlook important minima in their energy functions. More recent developments ameliorate these problems, but [11] still has problems with regularisation of the number of regions, and resolution of extracted boundaries, as does [13], even though it belongs to the second camp. The CAM system may either be viewed as an explicit discretisation of a contour based scheme, or as an iterative enhancement to region splitting methods it shares in the good properties of both. 590 British Machine Vision Conference 2 Framework ....
[Article contains additional citation context not shown here]
S.C. Zhu and A. Yuille. `Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation'. IEEE Trans. Pattern Analysis and Machine Intell., 18(9):884--900, September 1996.
Visual Communications of Tomorrow: Natural, Efficient and Flexible - Konrad (2001) (Correct)
....through regularization Markov minimum description length models to active contours. The segmentation problem, ill posed and even ill defined 4 , is notoriously difficult to solve and will challenge researchers for years to come. Some recent results based on region competition and active contours [12] show an interesting research direction. This direction is particularly interesting when coupled with level sets as a solution mechanism [13] An example of motion segmentation using region competition and level sets [14] is shown in Fig. 3. Note that the method is fully automatic and, although ....
S. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, " IEEE Trans. Pattern Anal. Machine Intell., vol. 18, pp. 884--900, Sept. 1996.
Level Set Evolution with Region Competition: Automatic 3-D.. - Ho, Bullitt, Gerig (2002) (2 citations) (Correct)
....the internal force is often strong enough to counteract global smoothness and leaks through these gaps. Thus, there is no convergence and the evolution has to be halted manually. This observation led to a new concept of region competition, where two adjacent regions compete for the common boundary [6], additionally constrained by a smoothness term. The driving problem discussed in this paper is the segmentation of 3 D brain tumors from magnetic resonance image data. Tumors vary in shape, size, location, and internal texture, and tumor segmentation is therefore known to be a very challenging ....
S. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and Bayes/MDL for multi-band image segmentation, " in International Conference on Computer Vision (ICCV'95), 1995, pp. 416--423.
Statistical Edge Detection: Learning and Evaluating Edge.. - Konishi, Yuille.. (2003) (4 citations) Self-citation (Zhu Yuille) (Correct)
No context found.
S.C. Zhu, T.S. Lee, and A.L. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, Sept. 1996.
Prior Learning and Gibbs Reaction-Diffusion - Zhu, Mumford (1997) (34 citations) Self-citation (Zhu) (Correct)
No context found.
S.C. Zhu and A.L. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multi-Band Image Segmentation, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
Image Parsing: Unifying Segmentation, Detection, and.. - Tu, Chen, Yuille, Zhu (2005) Self-citation (Zhu Yuille) (Correct)
No context found.
S. C. Zhu and A. L. Yuille, "Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation," IEEE Trans. PAMI, vol. 18, no. 9, 1996.
Range Image Segmentation by an Effective Jump-Diffusion Method - Han, Tu, Zhu Self-citation (Zhu) (Correct)
No context found.
S. C. Zhu and A. L. Yuille. "Region competition: unifying snakes, region growing, and Bayes/MDL for multiband Image Segmentation". IEEE Trans. PAMI. vol. 18, No. 9, pp. 884-900, 1996.
Shape Gradient for Multi-Modal Image Segmentation.. - Herbulot.. (Correct)
No context found.
S. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and bayes/MDL for multiband image segmentation, " PAMI, vol. 18, pp. 884--900, september 1996.
Segmentation for Robust Tracking - In The Presence (Correct)
No context found.
S. C. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and bayes/MDL for mulitband image segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, pp. 884--900, Sept. 1996.
Salient Closed Boundary Extraction with Ratio Contour - Wang, Kubota, Siskind, Wang (2005) (Correct)
No context found.
S.C. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multi-Band Image Segmentation, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
Two-level MRF Models for Image Restoration and Segmentation - Rivera, Gee (Correct)
No context found.
S.C. Zhu and A. Yuille, "The region competition: Unifying snakes, region growing and Bayes/MDL for multiband image segmentation," PAMI 18, 884-900, 1996.
Watersnakes: Energy-Driven Watershed Segmentation - Nguyen, Worring, van den.. (2003) (3 citations) (Correct)
No context found.
S. C. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, " IEEE Trans. on PAMI, vol. 18, no. 9, pp. 884--900, 1996.
Active Contours without Edges - Chan, Vese (2001) (55 citations) (Correct)
No context found.
S. C. Zhu, T. S. Lee, and A. L. Yuille, "Region competition: unifying snakes, region growing, energy/bayes/MDL for multi-band image segmentation, " in Proc. IEEE 5th Int. Conf. Computer Vision, Cambridge, MA, 1995.
International Journal of Pattern Recognition and Artificial.. - Vol No World (Correct)
No context found.
S.C. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, no. 9, pp. 884--900, Sept. 1996.
Contour Based Object Tracking with Occlusion Handling in.. - Yilmaz, Li, Shah (2004) (2 citations) (Correct)
No context found.
S. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
Statistical Region Merging - Nock, Nielsen (2004) (Correct)
No context found.
S.-C. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, pp. 884-900, 1996.
CLUE: Cluster-based Retrieval of Images by Unsupervised.. - Chen, Wang, Krovetz (2003) (2 citations) (Correct)
No context found.
S.C. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, no. 9, pp. 884--900, 1996.
Automatic Linguistic Indexing of Pictures by a Statistical.. - Li, Wang (2003) (11 citations) (Correct)
No context found.
S. Zhu and A.L. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
Looking Beyond Region Boundaries: A Robust Image Similarity.. - Chen, Wang (Correct)
No context found.
S. C. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation, " IEEE Trans. Pattern Anal. Machine Intell., 18(9):884-- 900, 1996.
Cortex Segmentation: A Fast Variational Geometric Approach - Goldenberg, Kimmel.. (2002) (4 citations) (Correct)
No context found.
S. C. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and bayes/MDL for multiband image segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, pp. 884--900, Sept. 1996.
Topics in Variational PDE Image Segmentation, Inpainting and.. - Song (2003) (Correct)
No context found.
S.C. Zhu and A. Yuille. "Region Competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation." IEEE Trans on Pattern Analysis and Machine Intelligence, 18(9):884--900, September 1996. 78
Automatic Linguistic Indexing of Pictures by a Statistical.. - Li, Wang (2003) (11 citations) (Correct)
No context found.
S. Zhu and A.L. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
Region-Based Active Contours Using Geometrical and .. - Jehan-Besson.. (2003) (Correct)
No context found.
S. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and bayes/MDL for multiband image segmentation, " PAMI, vol. 18, pp. 884--900, september 1996.
Image Segmentation and Residual Coding for Very Low Bit Rate.. - Comaniciu, Meer (Correct)
No context found.
S.C. Zhu, A. Yuille, "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation", IEEE Trans. Pattern Anal. Machine Intell.,Vol. 18, 884--900, 1996. 2 (a) (b) (c) (d)
Kinetic Minimum-Power Routing and Clustering in - Mobile Ad-Hoc Networks (Correct)
No context found.
S.C. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation," IEEE Trans. on PAMI, Vol. 18, No. 9, pp. 994-900, Sept. 1996.
Content-Based Image Retrieval by Clustering - Chen, Wang, Krovetz (2003) (Correct)
No context found.
S. C. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, no. 9, pp. 884--900, 1996.
Active Contours for Tracking Distributions - Freedman, Zhang (2004) (Correct)
No context found.
S. C. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, pp. 884--900, Sept. 1996.
Automatic Contour Estimation In Fetal Ultrasound Images - Sandra Jardim Escola (2003) (Correct)
No context found.
S. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation," IEEE Trans. on Patt. Anal. and Machine Intell. , vol. 18, pp. 884--900, 1996.
Smoothing B-Spline Active Contour or Fast and Robust.. - Precioso, Barlaud, al. (2003) (Correct)
No context found.
S. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and bayes/MDL for multiband image segmentation," PAMI, vol. 18, pp. 884--900, 1996.
Watersnakes: Energy-Driven Watershed Segmentation - Nguyen, Worring, van den.. (2003) (3 citations) (Correct)
No context found.
S. C. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, " IEEE Trans. on PAMI, vol. 18, no. 9, pp. 884--900, 1996.
B-Spline Active Contours for Fast Video Segmentation - Precioso, Barlaud (2001) (Correct)
No context found.
C. Zhu et A. Yuille, "Region competition: Unifying snakes, region growing and Bayes/MDL for multiband image segmentation", IEEE PAMI, vol. 18, n 9, pp. 884-900,1996.
Image Segmentation and Residual Coding for Very Low Bit Rate.. - Comaniciu, Meer (Correct)
No context found.
S.C. Zhu, A. Yuille, "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation", IEEE Trans. Pattern Anal. Machine Intell., Vol. 18, 884--900, 1996. 2 (a) (b) (c) (d)
Watersnakes: Energy-Driven Watershed Segmentation - Nguyen, Worring, van den.. (2003) (3 citations) (Correct)
No context found.
S.C. Zhu and A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
B-SPLINE ACTIVE CONTOUR with HANDLING of - Topology Changes For (2002) (Correct)
No context found.
S. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and bayes/MDL for multiband image segmentation," IEEE PAMI, vol. 18, 1996.
Affine Invariant Detection: Edges, Active Contours, and Segments - Olver (1996) (Correct)
No context found.
S. C. Zhu, T. S. Lee, and A. L. Yuille, "Region competition: Unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation," Proc. ICCV, Cambridge, June 1995. 36
Unsupervised Contour Representation and Estimation.. - Figueiredo.. (2000) (5 citations) (Correct)
No context found.
S. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, pp. 884--900, Sept. 1996.
Affine Invariant Detection: Edge Maps, Anisotropic.. - Olver, Sapiro.. (Correct)
No context found.
S. C. Zhu, T. S. Lee, and A. L. Yuille, "Region competition: Unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation," Proc. ICCV, Cambridge, MA, June 1995. 44
Image Segmentation by Curve Evolution with Clustering - Nilanjan Ray And (2000) (Correct)
No context found.
S.C. Zhu and A. Yuille, "Region Competition: Unifying snakes, region growing, and bayes/MDL for multiband image segmentation," IEEE Trans. on Prat. Anal. Mach. lntell. 18, 88&-900 (1996).
Segmentation based Image Retrieval - Siebert (1998) (1 citation) (Correct)
No context found.
S. Zhu, A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/ MDL for Multiband Image Segmentation", [EEE Trans. PAM[, Vol.18, No.9, pp.884-900, Sept.1996.
First 50 documents Next 50
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC