Automated evaluation of interest point detectors

Interest point detectors are important components in a variety of computer vision systems. This paper demonstrates an automated virtual 3D environment for controlling and measuring detected interest points on 2D images in an accurate and rapid manner. Real-time affine transform tools enable easy implementation and full automation of complex scene evaluations without the time-cost of a manual setup. Nine detectors are tested and compared using evaluation and testing methods based on Schmid [18]. Each detector is tested on the BSDS500 image set using rotation in the X, Y, and Z axis as well as scale in the X, Y axis. Results demonstrate the differing performance and behaviour of each detector across the evaluated transformations, which may assist computer vision practitioners in choosing the right detector for their application.

[1]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[2]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[3]  Tobias Höllerer,et al.  Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking , 2011, International Journal of Computer Vision.

[4]  Karl Rohr,et al.  Modelling and identification of characteristic intensity variations , 1992, Image Vis. Comput..

[5]  Paolo Tonella,et al.  Supporting Ontology-Based Semantic Annotation of Business Processes with Automated Suggestions , 2010, Int. J. Inf. Syst. Model. Des..

[6]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[7]  Benjamin Bustos,et al.  A Robust 3D Interest Points Detector Based on Harris Operator , 2010, 3DOR@Eurographics.

[8]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[9]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[10]  Christian Berger.  Cloud-based Testing for Context-Aware Cyber-Physical Systems , 2012 .

[11]  Tom Drummond,et al.  Fusing points and lines for high performance tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[12]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[13]  Óscar Martínez Mozos,et al.  A comparative evaluation of interest point detectors and local descriptors for visual SLAM , 2010, Machine Vision and Applications.

[14]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[15]  Paul Beaudet,et al.  Rotationally invariant image operators , 1978 .

[16]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[17]  Leonardo Trujillo,et al.  Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming , 2011, Image Vis. Comput..

[18]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[19]  Guillaume Gales,et al.  Complementarity of Feature Point Detectors , 2010, VISAPP.

[20]  Raul Valverde,et al.  Component-Based Modeling for Information Systems Reengineering , 2012 .