Extraction of Volumetric Structures in an Illuminance Image

An original method is proposed to extract the most significant volumetric structures in an illuminance image. The method proceeds in three levels of organization managed by generic grouping principles: (i) from the illu- minance image to a more compact representation of its contents by generic structural information extraction leading to a basic contour primitive map; (ii) grouping of the basic primitives in order to form intermediate primitives, the contour junctions; (iii) grouping of these junctions in order to build the high-level contour primi- tives, the generic volumetric structures. Experimental results for various images of cluttered scenes show an ability to properly extract the structures of volumetric objects or parts with planar and curved surfaces.

[1]  Wolfgang Förstner,et al.  Polymorphic grouping for image segmentation , 1995, Proceedings of IEEE International Conference on Computer Vision.

[2]  Marielle Mokhtari,et al.  Robust Extraction of 3D Structures by Fusion of Intensity-Based and Contour-Based Junction Features , 1998, MVA.

[3]  T. Pun,et al.  Asynchronous Perceptual Grouping: From Contours to Relevant 2-D Structures , 1997, Comput. Vis. Image Underst..

[4]  Andrew K. C. Wong,et al.  Corner detector for 3D object recognition , 1992, Other Conferences.

[5]  Marielle Mokhtari,et al.  Generic Multi-scale Segmentation and Curve Approximation Method , 2001, Scale-Space.

[6]  Gérard G. Medioni,et al.  The Challenge of Generic Object Recognition , 1994, Object Representation in Computer Vision.

[7]  Giorgio Quaglia,et al.  Saliency-based line grouping for structure detection , 1994, Other Conferences.

[8]  I. Biederman,et al.  Dynamic binding in a neural network for shape recognition. , 1992, Psychological review.

[9]  Robert Bergevin,et al.  Generic Object Recognition: Building and Matching Coarse Descriptions from Line Drawings , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Radu Horaud,et al.  Finding Geometric and Relational Structures in an Image , 1990, ECCV.

[11]  Jiri Matas,et al.  Junction detection using probabilistic relaxation , 1993, Image Vis. Comput..

[12]  Daniel P. Huttenlocher,et al.  Finding convex edge groupings in an image , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Antti Ylä-Jääski,et al.  Grouping Symmetrical Structures for Object Segmentation and Description , 1996, Comput. Vis. Image Underst..

[14]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Gérard G. Medioni,et al.  Perceptual grouping for generic recognition , 2004, International Journal of Computer Vision.

[16]  Josef Kittler,et al.  Low-level Grouping of Straight Line Segments , 1991 .

[17]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[18]  Robert Bergevin,et al.  Shape description using geons as a 3D primitives , 1992 .

[19]  I. Biederman.  Human image understanding: Recent research and a theory , 1985, Computer Vision Graphics and Image Processing.

[20]  Jake K. Aggarwal,et al.  Applying perceptual organization to the detection of man-made objects in non-urban scenes , 1992, Pattern Recognit..