View-based 3-D object recognition using shock graphs

By D. Macrini, Ali Shokoufandeh, Sven J. Dickinson, Kaleem Siddiqi, Steven W. Zucker. 2002

86 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-view Convolutional Neural Networks for 3D Shape Recognition (2015, background): “Alternatively, the silhouette of an object can be decomposed into parts and then represented by a directed acyclic graph (shock graph) [23].”
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation (2018, neutral): “Multi-View Representation Our work connects to multi-view representations which capture the characteristics of a 3D object from multiple viewpoints in 2D [18, 27, 42, 33, 12], such as decomposing image silhouettes [24], Light Field Descriptors [2], and 2D panoramic mapping [38].”
A survey of content based 3D shape retrieval methods (2004, methodology): “[77] apply indexing using topological signature vectors to implement view based similarity matching more efficiently.”
50 Years of object recognition: Directions forward (2013, background): “More recent work has expanded the scope of graph based algorithms using shock graphs [181], [210], [211], [212], [213], [214], [215].”
Analyzing appearance and contour based methods for object categorization (2003, background): “deformable prototypes [21] or shock graphs [24], to name but a few.”
Sketch-based 3D model retrieval using diffusion tensor fields of suggestive contours (2010, methodology): “In comparison, Funkhouser et al. used 13 orthographic viewpoints [18], Chen et al. used 10 shaded boundary images from 20 viewpoints [19], and Macrini et al. [ 20 ] used 128 projected images to 3D model retrieval.”