Part decomposition of objects from single view line drawings

Abstract We describe an algorithm for segmenting a 3D object into its constituent volumetric parts from a single 2D line drawing. This algorithm is part of the bottom-up line drawing description process of PARVO, a generic object recognition system. In PARVO, an object is recognized as being a member of a class defined by qualitative geometrical properties and topological structure. Similarity between different 3D views of any member of a given class is a result of building coarse part-based structural descriptions from limited information. A preliminary stage to part segmentation of an object consists of extracting viewpoint-invariant and nonaccidental features from the line drawing. The segmentation process itself is described as a hierarchical decomposition of the line drawing on the basis of pairs of high-concavity points in its silhouette. The pairing of these so-called segmentation points rests on (i) an object visibility model describing the generic structures present in any line drawing satisfying our assumptions, and (ii) the feature instances extracted from the specific line drawing analyzed. First, we discuss the properties of the line drawings in the domain of PARVO. From them, geometrical models of important line drawing features may be developed. The latter are used both as cues in the segmentation process and as precursors to the representation from which part structure is understood.

[1]  Robert Bergevin Primal access recognition of visual objects , 1989 .

[2]  Robert Bergevin,et al.  Generic object recognition: building coarse 3D descriptions from line drawings , 1989, [1989] Proceedings. Workshop on Interpretation of 3D Scenes.

[3]  Ramesh C. Jain,et al.  Three-dimensional object recognition , 1985, CSUR.

[4]  Robert Bergevin,et al.  Extraction of line drawing features for object recognition , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[5]  J. G. Snodgrass,et al.  A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. , 1980, Journal of experimental psychology. Human learning and memory.

[6]  A. Pentland Recognition by Parts , 1987 .

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

[8]  Yunde Jia Description and recognition of curved objects , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

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

[10]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..