A Survey Of Free-Form Object Representation and Recognition Techniques

By Richard J. Campbell, Patrick J. Flynn. Computer Vision and Image Understanding, 2001

616 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/c54292843486dbc3555186184b7c931b0c405d96

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Citations in the corpus

Least squares 3D surface and curve matching (2005, methodology): “One of the most popular methods is the Iterative Closest Point (ICP) algorithm developed by Besl andMcKay (1992), Chen and Medioni (1992), and Zhang (1994).”
Recent development in CNC machining of freeform surfaces: A state-of-the-art review (2010, background): “The definitions of freeform or sculptured surfaces are intuitive rather than formal [1].”
3D free-form object recognition in range images using local surface patches (2004, background): “…representation for free-from surfaces and researchers have used a number of representations, including B-Splines (Bhanu and Ho, 1987), surface curvatures, superquadrics (Solina and Bajcsy, 1990) and deformable models to recognize free-form objects in range images (Campbell and Flynn, 2001).”
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes (2006, methodology): “Registration is performed in two steps, namely, coarse and fine registration [6].”
A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer. (2009, background): “Bladder (13) 36% 0 [0,1] 42% 2 [1,4] 12% 6 [5,10] 9% 8 [7,10]”
Fully automatic registration of multiple 3D data sets (2003, methodology): “We begin with a review of the relevant registration algorithms according to the taxonomy in Fig. 1. A survey by Campbell and Flynn provides additional details on these algorithms [ 2 ].”
Explicit three dimensional topology optimization via Moving Morphable Void (MMV) approach (2017, background): “Actually, the construction of parameterized TDFs have been studied intensively in the field of computer graphics [25-27].”
A survey of content based 3D shape retrieval methods (2004, methodology): “Survey papers to this literature have been provided by Besl and Jain [11], Loncaric [50] and Campbell and Flynn [16].”
Intrinsic shape signatures: A shape descriptor for 3D object recognition (2009, background): “3D object representation and matching has been a fundamental part of computer vision research with applications in modeling, visualization, recognition, classification, and scene understanding [3][4][12].”
Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST (2010, background): “From early work on detecting objects on a conveyer belt — the “binpicking” task [24] — to more recent work on recognizing vehicles and other objects in urban environments [31,61], a successful strategy to object instance recognition has evolved [15].”
Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation (2006, methodology): “The paper focuses on indexing models from a database, i.e., on pruning the database into a small set of candidates containing the correct model type with a high probability.”
On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes (2009, background): “Campbell and Flynn (2001) give a survey of 3D freeform object recognition techniques and classify them into appearance based recognition, recognition from 2D silhouettes and free-form object recognition in range data.”
State of research in automatic as-built modelling (2015, background): “Complete representations, as opposed to discriminatory representations [28], fully describe the geometry of an object, and they can be explicit or implicit; explicit representations can be further subdivided into parametric, and non-parametric.”
Three-dimensional shape searching: state-of-the-art review and future trends (2005, neutral): “[45,46,86].”
3-D Object Retrieval and Recognition With Hypergraph Analysis (2012, background): “2199502 paper refers to 3-D object retrieval [1] and recognition [9] as the tasks of finding relevant objects for a given query and categorizing an object into one of a set of classes, respectively.”
Human Ear Recognition in 3D (2007, background): “Researchers have proposed various surface signatures for recognizing 3D free-form objects which are reviewed in [6], [40].”