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72
Action MACH: a spatio-temporal maximum average correlation height filter for action recognition
- In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition
, 2008
"... In this paper we introduce a template-based method for recognizing human actions called Action MACH. Our approach is based on a Maximum Average Correlation Height (MACH) filter. A common limitation of template-based methods is their inability to generate a single template using a collection of examp ..."
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Cited by 237 (10 self)
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In this paper we introduce a template-based method for recognizing human actions called Action MACH. Our approach is based on a Maximum Average Correlation Height (MACH) filter. A common limitation of template-based methods is their inability to generate a single template using a collection of examples. MACH is capable of capturing intra-class variability by synthesizing a single Action MACH filter for a given action class. We generalize the traditional MACH filter to video (3D spatiotemporal volume), and vector valued data. By analyzing the response of the filter in the frequency domain, we avoid the high computational cost commonly incurred in template-based approaches. Vector valued data is analyzed using the Clifford Fourier transform, a generalization of the Fourier transform intended for both scalar and vector-valued data. Finally, we perform an extensive set of experiments and compare our method with some of the most recent approaches in the field by using publicly available datasets, and two new annotated human action datasets which include actions performed in classic feature films and sports broadcast television. 1.
Image Change Detection Algorithms: A Systematic Survey
- IEEE Transactions on Image Processing
, 2005
"... Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. T ..."
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Cited by 236 (3 self)
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Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.
Multi-View Scene Capture by Surfel Sampling: From Video Streams to Non-Rigid 3D Motion, Shape Reflectance
, 2001
"... In this paper we study the problem of recovering the 3D shape, reflectance, and non-rigid motion of a dynamic 3D scene. Because these properties are completely unknown, our approach uses multiple views to build a piecewisecontinuous geometric and radiometric representation of the scene's trace ..."
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Cited by 117 (0 self)
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In this paper we study the problem of recovering the 3D shape, reflectance, and non-rigid motion of a dynamic 3D scene. Because these properties are completely unknown, our approach uses multiple views to build a piecewisecontinuous geometric and radiometric representation of the scene's trace in space-time. Basic primitive of this representation is the dynamic surfel, which (1) encodes the instantaneous local shape, reflectance, and motion of a small region in the scene, and (2) enables accurate prediction of the region's dynamic appearance under known illumination conditions. We show that complete surfel-based reconstructions can be created by repeatedly applying an algorithm called Surfel Sampling that combines sampling and parameter estimation to fit a single surfel to a small, bounded region of space-time. Experimental results with the Phong reflectance model and complex real scenes (clothing, skin, shiny objects) illustrate our method's ability to explain pixels and pixel variations in terms of their physical causes--- shape, reflectance, motion, illumination, and visibility.
Computing Optical Flow with Physical Models of Brightness Variation
"... This paper exploits physical models of time-varying brightness in image sequences to estimate optical flow and physical parameters of the scene. Previous approaches handled violations of brightness constancy with the use of robust statistics or with generalized brightness constancy constraints that ..."
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Cited by 101 (1 self)
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This paper exploits physical models of time-varying brightness in image sequences to estimate optical flow and physical parameters of the scene. Previous approaches handled violations of brightness constancy with the use of robust statistics or with generalized brightness constancy constraints that allow generic types of contrast and illumination changes. Here, we consider models of brightness variation that have time-dependent physical causes, namely, changing surface orientation with respect to a directional illuminant, motion of the illuminant, and physical models of heat transport in infrared images. We simultaneously estimate the optical flow and the relevant physical parameters. The estimation problem is formulated using total least squares (TLS), with confidence bounds on the parameters.
Robust Parameterized Component Analysis: Theory and Applications to 2D Facial Modeling
- Computer Vision and Image Understanding, 91:53 – 71
, 2002
"... Principal Component Analysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion. In particular, PCA has been widely used to model the variation in the appearance of people's faces. We extend previous work on facial modeling for tracking faces in video ..."
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Cited by 53 (12 self)
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Principal Component Analysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion. In particular, PCA has been widely used to model the variation in the appearance of people's faces. We extend previous work on facial modeling for tracking faces in video sequences as they undergo significant changes due to facial expressions. Here we develop person-specific facial appearance models (PSFAM), which use modular PCA to model complex intra-person appearance changes. Such models require aligned visual training data; in previous work, this has involved a time consuming and errorprone hand alignment and cropping process. Instead, we introduce parameterized component analysis to learn a subspace that is invariant to affine (or higher order) geometric transformations. The automatic learning of a PSFAM given a training image sequence is posed as a continuous optimization problem and is solved with a mixture of stochastic and deterministic techniques achieving sub-pixel accuracy.
R.: Dense shape reconstruction of a moving object under arbitrary, unknown lighting
- In: ICCV (2003
"... We present a method for shape reconstruction from several images of a moving object. The reconstruction is dense (up to image resolution). The method assumes that the motion is known, e.g., by tracking a small number of feature points on the object. The object is assumed Lambertian (completely matte ..."
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Cited by 31 (4 self)
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We present a method for shape reconstruction from several images of a moving object. The reconstruction is dense (up to image resolution). The method assumes that the motion is known, e.g., by tracking a small number of feature points on the object. The object is assumed Lambertian (completely matte), light sources should not be very close to the object but otherwise arbitrary, and no knowledge of lighting conditions is required. An object changes its appearance significantly when it changes its orientation relative to light sources, causing violation of the common brightness constancy assumption. While a lot of effort is devoted to deal with this violation, we demonstrate how to exploit it to recover 3D structure from 2D images. We propose a new correspondence measure that enables point matching across views of a moving object. The method has been tested both on computer simulated examples and on a real object. 1.
Parameterized Kernel Principal Component Analysis: Theory and Applications to Supervised and Unsupervised Image Alignment
, 2008
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Active blobs: region-based, deformable appearance models
- Comput. Vis. Image Underst
"... A region-based approach to nonrigid motion tracking is described. Shape is defined in terms of a deformable triangular mesh that captures object shape plus a color texture map that captures object appearance. Photometric variations are also modeled. Nonrigid shape registra-tion and motion tracking a ..."
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Cited by 27 (0 self)
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A region-based approach to nonrigid motion tracking is described. Shape is defined in terms of a deformable triangular mesh that captures object shape plus a color texture map that captures object appearance. Photometric variations are also modeled. Nonrigid shape registra-tion and motion tracking are achieved by posing the problem as an energy-based, robust min-imization procedure. The approach provides robustness to occlusions, wrinkles, shadows, and specular highlights. The formulation is tailored to take advantage of texture mapping hard-ware available in many workstations, PCs, and game consoles. This enables nonrigid tracking at speeds approaching video rate.
Robust Computer Vision: An Interdisciplinary Challenge
- Computer Vision and Image Understanding
, 2000
"... INTRODUCTION Robust Computer Vision: An Interdisciplinary Challenge Peter Meer, Guest Editor Electrical and Computer Engineering Department, Rutgers University, 94 Brett Road, Piscataway, New Jersey 08854-8058 E-mail: meer@caip.rutgers.edu Charles V. Stewart, Guest Editor Computer Science De ..."
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Cited by 22 (0 self)
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INTRODUCTION Robust Computer Vision: An Interdisciplinary Challenge Peter Meer, Guest Editor Electrical and Computer Engineering Department, Rutgers University, 94 Brett Road, Piscataway, New Jersey 08854-8058 E-mail: meer@caip.rutgers.edu Charles V. Stewart, Guest Editor Computer Science Department, Rensselaer Polytechnic Institute, 110 8th Street, Troy, New York 12180-3590 E-mail: stewart@cs.rpi.edu and David E. Tyler, Guest Editor Statistics Department, Rutgers University, 110 Frelinghuysen Road, Piscataway, New Jersey 08854-8018 E-mail: dtyler@caip.rutgers.edu This special issue is dedicated to examining the use of techniques from robust statistics in solving computer vision problems. It represents a milestone of recent progress within a subarea of our field that is nearly as old as the field itself, but has seen rapid growth over the past decade. Our Introduction considers the meaning of robu
Learning from One Example in Machine Vision by Sharing Probability Densities
, 2002
"... Human beings exhibit rapid learning when presented with a small number of images of a new object. A person can identify an object under a wide variety of visual conditions after having seen only a single example of that object. This ability can be partly explained by the application of previously le ..."
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Cited by 20 (1 self)
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Human beings exhibit rapid learning when presented with a small number of images of a new object. A person can identify an object under a wide variety of visual conditions after having seen only a single example of that object. This ability can be partly explained by the application of previously learned statistical knowledge to a new setting. This thesis presents an approach to acquiring knowledge in one setting and using it in another. Specifically, we develop probability densities over common image changes. Given a single image of a new object and a model of change learned from a di#erent object, we form a model of the new object that can be used for synthesis, classification, and other visual tasks. We start by
