Memory Efficient 3D Integral Volumes

Integral image data structures are very useful in computer vision applications that involve machine learning approaches based on ensembles of weak learners. The weak learners often are simply several regional sums of intensities subtracted from each other. In this work we present a memory efficient integral volume data structure, that allows reduction of required RAM storage size in such a supervised learning framework using 3D training data. We evaluate our proposed data structure in terms of the tradeoff between computational effort and storage, and show an application for 3D object detection of liver CT data.

[1]  Vincent Lepetit,et al.  Fast Keypoint Recognition Using Random Ferns , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Dorin Comaniciu,et al.  Constrained marginal space learning for efficient 3D anatomical structure detection in medical images , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Rodolfo S. Lima,et al.  GPU-efficient recursive filtering and summed-area tables , 2011, SA '11.

[4]  Zhuowen Tu,et al.  A Learning Based Approach for 3D Segmentation and Colon Detagging , 2006, ECCV.

[5]  Derrick G. Kourie,et al.  Performance of C++ bit-vector implementations , 2010, SAICSIT '10.

[6]  Horst Bischof,et al.  Global localization of 3D anatomical structures by pre-filtered Hough Forests and discrete optimization , 2013, Medical Image Anal..

[7]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[8]  Antonio Criminisi,et al.  Decision Forests for Computer Vision and Medical Image Analysis , 2013, Advances in Computer Vision and Pattern Recognition.

[9]  Antonio Criminisi,et al.  Regression forests for efficient anatomy detection and localization in computed tomography scans , 2013, Medical Image Anal..

[10]  Andrew S. Glassner,et al.  Multidimensional sum tables , 1990 .

[11]  Franklin C. Crow,et al.  Summed-area tables for texture mapping , 1984, SIGGRAPH.

[12]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[13]  Dimitris N. Metaxas,et al.  Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images , 2011, IPMI.

[14]  Roberto Cipolla,et al.  Semantic texton forests for image categorization and segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[16]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[17]  Anselmo Lastra,et al.  Fast Summed‐Area Table Generation and its Applications , 2005, Comput. Graph. Forum.

[18]  H.J.W. Belt,et al.  Storage Size Reduction for the Integral Image , 2007 .

[19]  Chiou-Shann Fuh,et al.  Segmenting Highly Articulated Video Objects with Weak-Prior Random Forests , 2006, ECCV.

[20]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[21]  Luc Van Gool,et al.  Hough Forests for Object Detection, Tracking, and Action Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.