Cascaded pose regression

Dollár, Piotr; Welinder, Peter; Perona, Pietro

doi:10.1109/cvpr.2010.5540094

Cited by 484 publications

(413 citation statements)

References 28 publications

(28 reference statements)

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“…This can be considered as a cascaded pose regression similar to [17]. Illustration of the kidney detection is given in Figure 1 and quantitative results are reported in Section 4.…”

Section: Refinement Of the Region Of Interestmentioning

confidence: 99%

Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

Cuingnet

Prevost

Lesage

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

118

101

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Abstract. Kidney segmentation in 3D CT images allows extracting useful information for nephrologists. For practical use in clinical routine, such an algorithm should be fast, automatic and robust to contrast-agent enhancement and fields of view. By combining and refining state-of-theart techniques (random forests and template deformation), we demonstrate the possibility of building an algorithm that meets these requirements. Kidneys are localized with random forests following a coarseto-fine strategy. Their initial positions detected with global contextual information are refined with a cascade of local regression forests. A classification forest is then used to obtain a probabilistic segmentation of both kidneys. The final segmentation is performed with an implicit template deformation algorithm driven by these kidney probability maps. Our method has been validated on a highly heterogeneous database of 233 CT scans from 89 patients. 80 % of the kidneys were accurately detected and segmented (Dice coefficient > 0.90) in a few seconds per volume.

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“…This can be considered as a cascaded pose regression similar to [17]. Illustration of the kidney detection is given in Figure 1 and quantitative results are reported in Section 4.…”

Section: Refinement Of the Region Of Interestmentioning

confidence: 99%

Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

Cuingnet

Prevost

Lesage

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

118

101

View full text Add to dashboard Cite

show abstract

“…Automatically annotating behavior produces large quantities of consistently defined and highly resolved behavioral data, providing biologists with unprecedented power to quantitatively understand general mechanisms and principles underlying behavior [68,71] (Box 1). Automated behavioral analysis is possible with trajectory information alone, such as differentiating between an individual being stationary, walking, or running, or with more detailed pose information, such as head position, contour shape, or appendage position [8,62,68,72] (Movie S2, Movie S14, and Movie S11 in the supplementary material online).…”

Section: Automated Behavioral Analysismentioning

confidence: 99%

“…The situation is more complex when the body is flexible and multiple degrees of freedom are of interest, such as wing angles or head orientation ( Figure IJ). Algorithms for learning and computing an individual's pose is an active area of research, and involves either explicit modeling of the body, or learning associations between image brightness patterns and pose parameters (68,72,76).…”

mentioning

confidence: 99%

Automated image-based tracking and its application in ecology

Dell

Bender²,

Branson

et al. 2014

Trends in Ecology & Evolution

Self Cite

435

484

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The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers. Measuring behaviorIndividual behavior (see Glossary) underlies almost all aspects of ecology [1][2][3][4][5]. Accurate and highly resolved behavioral data are therefore critical for obtaining a mechanistic and predictive understanding of ecological systems [5]. Historically, direct observation by trained biologists was used to quantify behavior [6,7]. However, the extent and resolution to which direct observations can be made is highly constrained [8] and the number of individuals that can be observed simultaneously is small. In addition, an exact record of events is not preserved, only the biologist's subjective account of them.Recent technological advances in tracking now make it possible to collect large amounts of highly precise and accurate behavioral data. For many organisms equipment can be attached that provide information about the Glossary Background subtraction: a method used by software to compare the current video frame with a stored picture of the background; any pixel of the current frame that is significantly different from the corresponding pixel in the background is likely to be associated with the body of an animal. Useful in situations where the background is unchanging, for example, when the surface of the background is rigid and lighting does not change. Behavior: the actions of individuals, often in response to stimuli. Behavior can involve movement of the individual's body through space, such as walking or chasing, or can occur while the animal is stationary, such as grooming or eating. Bio-logging: attachment or implantation of equipment to organisms to provide information about their identity, location, behavior, or physiology (e.g., global positioning systems, accelerometers, video cameras, telemetry tags). Ecological interaction: any interaction between an organism and its environment, or between two organisms (i.e., including interactions between conspecifics). Fingerprinting: a method used to identify unmarked individuals using natural variation in their physical and/or behavioral appearance. The method works by transforming the images of each individual ...

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“…ESR [10] uses shape indexed intensity difference features for face alignment based on CPR [9]. Moreover, SDM extracts shape-indexed SIFT features and learns a sequence of general descent maps from supervised training data, providing a solution when Newton Descent method is hard to be utilized for a not analytically differentiable nonlinear function or Hessian matrix is too large and not positive definite.…”

Section: Cascaded Regression To Face Alignmentmentioning

confidence: 99%

“…The main difference between cascaded regression and related boosting regression is that cascaded regression uses shape-indexed features extracted from the image according to the current estimated shape. Cascaded Pose Regression (CPR) [9] for pose estimation has been widely extended into face alignment in current works, represented by Explicit Shape Regression (ESR) [10], and Supervised Descent Method (SDM) [11]. It is noticed that SDM provides a theoretical explanation of the cascaded regression from the point of view of optimizing a nonlinear problem, as a significant achievement in cascaded regression-based methods.…”

Section: Introductionmentioning

confidence: 99%

Sign-Correlation Partition Based on Global Supervised Descent Method for Face Alignment

Zhang

Liu

Yang

et al. 2017

Computer Vision – ACCV 2016

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Abstract. Face alignment is an essential task for facial performance capture and expression analysis. As a complex nonlinear problem in computer vision, face alignment across poses is still not studied well. Although the state-of-the-art Supervised Descent Method (SDM) has shown good performance, it learns conflict descent direction in the whole complex space due to various poses and expressions. Global SDM has been presented to deal with this case by domain partition in feature and shape PCA spaces for face tracking and pose estimation. However, it is not suitable for the face alignment problem due to unknown ground truth shapes. In this paper we propose a sign-correlation subspace method for the domain partition of global SDM. In our method only one reduced low dimensional subspace is enough for domain partition, thus adjusting the global SDM efficiently for face alignment. Unlike previous methods, we analyze the sign correlation between features and shapes, and project both of them into a mutual sign-correlation subspace. Each pair of projected shape and feature keep sign consistent in each dimension of the subspace, so that each hyperoctant holds the condition that one general descent exists. Then a set of general descent directions are learned from the samples in different hyperoctants. Our sign-correlation partition method is validated in the public face datasets, which includes a range of poses. It indicates that our methods can reveal their latent relationships to poses. The comparison with state-of-the-art methods for face alignment demonstrates that our method outperforms them especially in uncontrolled conditions with various poses, while keeping comparable speed.

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Cascaded pose regression

Cited by 484 publications

References 28 publications

Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

Automated image-based tracking and its application in ecology

Sign-Correlation Partition Based on Global Supervised Descent Method for Face Alignment

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