Shape-based recognition of wiry objects

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge information. We first use example images of a target object in typical environments to train a classifier cascade that determines whether edge pixels in an image belong to an instance of the desired object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels and group the object edge pixels into overall detections of the object. The features used for the edge pixel classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of a set of complex objects in a variety of cluttered indoor scenes under arbitrary out-of-image-plane rotation. Furthermore, our experiments suggest that the technique is robust to variations between training and testing environments and is efficient at runtime.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

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

[3]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[4]  William Grimson,et al.  Object recognition by computer - the role of geometric constraints , 1991 .

[5]  W. Eric L. Grimson,et al.  On the sensitivity of geometric hashing , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[6]  Thomas G. Dietterich,et al.  Learning with Many Irrelevant Features , 1991, AAAI.

[7]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[8]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[9]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[10]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[11]  David G. Lowe,et al.  Vista: a software environment for computer vision research , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Thomas G. Dietterich,et al.  Learning Boolean Concepts in the Presence of Many Irrelevant Features , 1994, Artif. Intell..

[13]  I. Kononenko,et al.  INDUCTION OF DECISION TREES USING RELIEFF , 1995 .

[14]  Jezekiel Ben-Arie,et al.  Iconic recognition with affine-invariant spectral signatures , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[15]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[16]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[17]  Ron Kohavi,et al.  Data Mining Using MLC a Machine Learning Library in C++ , 1996, Int. J. Artif. Intell. Tools.

[18]  Yali Amit,et al.  Joint Induction of Shape Features and Tree Classifiers , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  John Krumm.  Object detection with vector quantized binary features , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Stan Z. Li,et al.  A Two-Stage Probabilistic Approach for Object Recognition , 1998, ECCV.

[21]  Carla E. Brodley,et al.  Pruning Decision Trees with Misclassification Costs , 1998, ECML.

[22]  Ron Kohavi,et al.  The Case against Accuracy Estimation for Comparing Induction Algorithms , 1998, ICML.

[23]  Takeo Kanade,et al.  Probabilistic modeling of local appearance and spatial relationships for object recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[24]  James L. Crowley,et al.  Visual Recognition Using Local Appearance , 1998, ECCV.

[25]  Andrea Salgian,et al.  A Perceptual Grouping Hierarchy for Appearance-Based 3D Object Recognition , 1999, Comput. Vis. Image Underst..

[26]  David G. Lowe,et al.  Indexing without Invariants in 3D Object Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Daniel P. Huttenlocher,et al.  A new Bayesian framework for object recognition , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[28]  Ronen Basri,et al.  Projective alignment with regions , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[29]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[30]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[31]  Andreas Buja,et al.  Data mining criteria for tree-based regression and classification , 2001, KDD '01.

[32]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[33]  Daniel Keren,et al.  Antifaces: A Novel, Fast Method for Image Detection , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Bianca Zadrozny,et al.  Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.

[35]  Harry Shum,et al.  Statistical Learning of Multi-view Face Detection , 2002, ECCV.

[36]  Martial Hebert,et al.  Object Recognition by a Cascade of Edge Probes , 2002, BMVC.

[37]  Luhong Liang,et al.  A detector tree of boosted classifiers for real-time object detection and tracking , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[38]  James M. Rehg,et al.  Learning a Rare Event Detection Cascade by Direct Feature Selection , 2003, NIPS.

[39]  Martial Hebert,et al.  Shape-based recognition of wiry objects , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[40]  Cordelia Schmid,et al.  Shape recognition with edge-based features , 2003, BMVC.

[41]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[42]  Björn Stenger,et al.  Shape context and chamfer matching in cluttered scenes , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[43]  William T. Freeman,et al.  Exploiting the generic viewpoint assumption , 1996, International Journal of Computer Vision.

[44]  Takeo Kanade,et al.  Object Detection Using the Statistics of Parts , 2004, International Journal of Computer Vision.

[45]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[46]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[47]  Owen Carmichael,et al.  Word: Wiry object recognition database , 2004 .

[48]  David J. Kriegman,et al.  What Is the Set of Images of an Object Under All Possible Illumination Conditions? , 1998, International Journal of Computer Vision.

[49]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

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