Int J Comput Vis (2014) 107:99–100DOI 10.1007/s11263-014-0706-8

Guest Editorial: Geometry, Lighting, Motion, and Learning

A. L. Yuille · Jiebo Luo

Published online: 5 March 2014© Springer Science+Business Media New York 2014

Computer vision is starting to become practical and success-ful. The academic computer vision community has grownimmensely in recent years and there is a rapidly growingcomputer vision industry ranging from high-tech giants tohumble start-ups. In particular, the technology that computervision relies on—computers, the internet, and cameras—isever-improving in quality and power. Indeed the advancesin technology alone may lead future historians of computervision to conclude that researchers in the 1970’s and 1980’swere crazy romantics to address such a difficult problem withthe hardware available at that time.

Nevertheless the rapid growth of computer vision andits freewheeling interdisciplinary nature, which continuallyborrows and adapts techniques from different disciplines,has led to its own dangers. The field risks being faction-alized into groups of researchers using different techniquesand with the same ideas being re-invented under differentnames. The experimental methodology can also be criticizedby sometimes valuing complex models which yield gains ofa few percentage points in performance on a dataset ratherthan simpler and more insightful models. Hence, as com-puter vision matures there is growing need for papers whichaddress the fundamental issues of computer vision in a rig-orous mathematics manner and which are carefully tested bywell-designed experiments.

A. L. Yuille (B)Department of Statistics, UCLA, University of California,Los Angeles, 8967 Math Sciences Building, Los Angeles,CA 90095-1554, USAe-mail: alan.l.yuille@gmail.com

J. Luo (B)Department of Computer Science, University of Rochester,611 Computer Studies Building, Rochester, NY 14627, USAe-mail: jiebo.luo@gmail.com

This special issue contains seven papers which satisfythese requirements. They range over some of the major topicsin computer vision—geometry, lighting, learning, probabilis-tic modeling—and help illustrate the richness of the field, thepower and sophistication of the techniques being used, andthe practical success on real world tasks.

Three of the papers address the fundamental issuesof geometry, lighting, and the interactions between them.Firstly, “A Simple Prior-Free Method for Non-rigid Structure-from-Motion Factorization” (NRSfM) contains an elegantmathematical analysis of the problem of non-rigid structurefrom motion using the factorization approach. This analy-sis shows that the problem can be solved “prior-free” andleads naturally to a practical algorithm for NRSfM. Secondly,“Decomposing global light transport using time of flightimaging” shows how to decompose time of flight videos intodirect, subsurface scattering, and interreflection components.This can be applied to recover projective depth from thedirect component in the presence of global scattering, to iden-tify and label different types of global illumination effects,to measure the parameters of subsurface scattering materi-als from a single point of view; to performi edge detection,and to adjust subsurface scattering to render novel images ofthe scene. Thirdly, “A Closed-Form, Consistent and RobustSolution to Uncalibrated Photometric Stereo Via Local Dif-fuse Reflectance Maxima” addresses the classic GeneralizedBas Relief (GBR) ambiguity which involves both lightingand geometry. This paper contains insightful mathematicalanalysis of the interaction of geometry and lighting. Theyintroduce the concept of LDR maxima and shows that thiscan yield a closed form solution for the GBR parameterswhich is robust, consistent, and gives good results on realworld scenes.

The remaining four papers involve learning which is,arguably, the most important factor in the growing success of

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computer vision systems. The first two papers—“The ShapeBoltzmann Machine: A Strong Model of Object Shape” and“Face Alignment by Explicit Shape Regression”—addressthe topics of shape modeling and object alignment. TheShape Boltzmann Machine (SBM) is a model for shape,based on deep Boltzmann machines, which is learnt fromtraining data and is used for segmentation, detection, inpaint-ing and graphics. The SBM capture shape sufficiently wellthat samples from it look realistic. By contrast, the paper onface alignment uses a regression approach to address thisclassic and important problem. This method significantlyoutperforms alternative methods is speed and accuracy. Thesecond two papers apply learning to motion problems. The

first paper, on “Max-Margin Early Event Detector’, using themaximum-margin framework to train temporal event detec-tors to recognize partial events, enabling early detection. Thiswork extends structured output SVM to sequential data. Thismethod is successfully applied to detect facial expressions,hand gestures, and human activities. The second paper on“Multi-Target Tracking by Online Learning a CRF Modelof Appearance and Motion Patterns” applies online learningto multi-target tracking. The tracking problem is formulatedusing an online learned CRF model containing unary func-tions based on motion and appearance models for discrim-inating targets, together with binary functions which differ-entiate g pairs of tracklets.

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