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2011


Thumb xl ijnmbe1
Computational flow studies in a subject-specific human upper airway using a one-equation turbulence model. Influence of the nasal cavity

Prihambodo Saksono, Perumal Nithiarasu, Igor Sazonov, Si Yong Yeo

International Journal for Numerical Methods in Biomedical Engineering, 87(1-5):96–114, 2011 (article)

Abstract
This paper focuses on the impact of including nasal cavity on airflow through a human upper respiratory tract. A computational study is carried out on a realistic geometry, reconstructed from CT scans of a subject. The geometry includes nasal cavity, pharynx, larynx, trachea and two generations of airway bifurcations below trachea. The unstructured mesh generation procedure is discussed in some length due to the complex nature of the nasal cavity structure and poor scan resolution normally available from hospitals. The fluid dynamic studies have been carried out on the geometry with and without the inclusion of the nasal cavity. The characteristic-based split scheme along with the one-equation Spalart–Allmaras turbulence model is used in its explicit form to obtain flow solutions at steady state. Results reveal that the exclusion of nasal cavity significantly influences the resulting solution. In particular, the location of recirculating flow in the trachea is dramatically different when the truncated geometry is used. In addition, we also address the differences in the solution due to imposed, equally distributed and proportionally distributed flow rates at inlets (both nares). The results show that the differences in flow pattern between the two inlet conditions are not confined to the nasal cavity and nasopharyngeal region, but they propagate down to the trachea.

[BibTex]

2011

[BibTex]


Thumb xl sufacematching ssvm11
Discrete Minimum Distortion Correspondence Problems for Non-rigid Shape Matching

Wang, C., Bronstein, M. M., Bronstein, A. M., Paragios, N.

In International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2011 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl viewpointinvariantmodel iccv11.
Viewpoint Invariant 3D Landmark Model Inference from Monocular 2D Images Using Higher-Order Priors

Wang, C., Zeng, Y., Simon, L., Kakadiaris, I., Samaras, D., Paragios, N.

In IEEE International Conference on Computer Vision (ICCV), 2011 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Correspondence estimation from non-rigid motion information

Wulff, J., Lotz, T., Stehle, T., Aach, T., Chase, J. G.

In Proc. SPIE, Proc. SPIE, (Editors: B. M. Dawant, D. R. Haynor), SPIE, 2011 (inproceedings)

Abstract
The DIET (Digital Image Elasto Tomography) system is a novel approach to screen for breast cancer using only optical imaging information of the surface of a vibrating breast. 3D tracking of skin surface motion without the requirement of external markers is desirable. A novel approach to establish point correspondences using pure skin images is presented here. Instead of the intensity, motion is used as the primary feature, which can be extracted using optical flow algorithms. Taking sequences of multiple frames into account, this motion information alone is accurate and unambiguous enough to allow for a 3D reconstruction of the breast surface. Two approaches, direct and probabilistic, for this correspondence estimation are presented here, suitable for different levels of calibration information accuracy. Reconstructions show that the results obtained using these methods are comparable in accuracy to marker-based methods while considerably increasing resolution. The presented method has high potential in optical tissue deformation and motion sensing.

pdf link (url) DOI [BibTex]

pdf link (url) DOI [BibTex]


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Predicting Articulated Human Motion from Spatial Processes

Soren Hauberg, Kim S. Pedersen

International Journal of Computer Vision, 94, pages: 317-334, Springer Netherlands, 2011 (article)

Publishers site Code Paper site PDF [BibTex]

Publishers site Code Paper site PDF [BibTex]


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An Empirical Study on the Performance of Spectral Manifold Learning Techniques

Peter Mysling, Soren Hauberg, Kim S. Pedersen

In Artificial Neural Networks and Machine Learning – ICANN 2011, 6791, pages: 347-354, Lecture Notes in Computer Science, (Editors: Honkela, Timo and Duch, Włodzisław and Girolami, Mark and Kaski, Samuel), Springer Berlin Heidelberg, 2011 (inproceedings)

Publishers site PDF [BibTex]

Publishers site PDF [BibTex]


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Separation of visual object features and grasp strategy in primate ventral premotor cortex

Vargas-Irwin, C., Franquemont, L., Black, M., Donoghue, J.

Neural Control of Movement, 21st Annual Conference, 2011 (conference)

[BibTex]

[BibTex]

2006


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Finding directional movement representations in motor cortical neural populations using nonlinear manifold learning

WorKim, S., Simeral, J., Jenkins, O., Donoghue, J., Black, M.

World Congress on Medical Physics and Biomedical Engineering 2006, Seoul, Korea, August 2006 (conference)

[BibTex]

2006

[BibTex]


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A non-parametric Bayesian approach to spike sorting

Wood, F., Goldwater, S., Black, M. J.

In International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pages: 1165-1169, New York, NY, August 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Predicting 3D people from 2D pictures

(Best Paper)

Sigal, L., Black, M. J.

In Proc. IV Conf. on Articulated Motion and DeformableObjects (AMDO), LNCS 4069, pages: 185-195, July 2006 (inproceedings)

Abstract
We propose a hierarchical process for inferring the 3D pose of a person from monocular images. First we infer a learned view-based 2D body model from a single image using non-parametric belief propagation. This approach integrates information from bottom-up body-part proposal processes and deals with self-occlusion to compute distributions over limb poses. Then, we exploit a learned Mixture of Experts model to infer a distribution of 3D poses conditioned on 2D poses. This approach is more general than recent work on inferring 3D pose directly from silhouettes since the 2D body model provides a richer representation that includes the 2D joint angles and the poses of limbs that may be unobserved in the silhouette. We demonstrate the method in a laboratory setting where we evaluate the accuracy of the 3D poses against ground truth data. We also estimate 3D body pose in a monocular image sequence. The resulting 3D estimates are sufficiently accurate to serve as proposals for the Bayesian inference of 3D human motion over time

pdf pdf from publisher Video [BibTex]

pdf pdf from publisher Video [BibTex]


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Specular flow and the recovery of surface structure

Roth, S., Black, M.

In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, 2, pages: 1869-1876, New York, NY, June 2006 (inproceedings)

Abstract
In scenes containing specular objects, the image motion observed by a moving camera may be an intermixed combination of optical flow resulting from diffuse reflectance (diffuse flow) and specular reflection (specular flow). Here, with few assumptions, we formalize the notion of specular flow, show how it relates to the 3D structure of the world, and develop an algorithm for estimating scene structure from 2D image motion. Unlike previous work on isolated specular highlights we use two image frames and estimate the semi-dense flow arising from the specular reflections of textured scenes. We parametrically model the image motion of a quadratic surface patch viewed from a moving camera. The flow is modeled as a probabilistic mixture of diffuse and specular components and the 3D shape is recovered using an Expectation-Maximization algorithm. Rather than treating specular reflections as noise to be removed or ignored, we show that the specular flow provides additional constraints on scene geometry that improve estimation of 3D structure when compared with reconstruction from diffuse flow alone. We demonstrate this for a set of synthetic and real sequences of mixed specular-diffuse objects.

pdf [BibTex]

pdf [BibTex]


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An adaptive appearance model approach for model-based articulated object tracking

Balan, A., Black, M. J.

In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, 1, pages: 758-765, New York, NY, June 2006 (inproceedings)

Abstract
The detection and tracking of three-dimensional human body models has progressed rapidly but successful approaches typically rely on accurate foreground silhouettes obtained using background segmentation. There are many practical applications where such information is imprecise. Here we develop a new image likelihood function based on the visual appearance of the subject being tracked. We propose a robust, adaptive, appearance model based on the Wandering-Stable-Lost framework extended to the case of articulated body parts. The method models appearance using a mixture model that includes an adaptive template, frame-to-frame matching and an outlier process. We employ an annealed particle filtering algorithm for inference and take advantage of the 3D body model to predict self occlusion and improve pose estimation accuracy. Quantitative tracking results are presented for a walking sequence with a 180 degree turn, captured with four synchronized and calibrated cameras and containing significant appearance changes and self-occlusion in each view.

pdf [BibTex]

pdf [BibTex]


Thumb xl silly
Measure locally, reason globally: Occlusion-sensitive articulated pose estimation

Sigal, L., Black, M. J.

In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, 2, pages: 2041-2048, New York, NY, June 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Statistical analysis of the non-stationarity of neural population codes

Kim, S., Wood, F., Fellows, M., Donoghue, J. P., Black, M. J.

In BioRob 2006, The first IEEE / RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pages: 295-299, Pisa, Italy, Febuary 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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How to choose the covariance for Gaussian process regression independently of the basis

Franz, M., Gehler, P.

In Proceedings of the Workshop Gaussian Processes in Practice, 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl screen shot 2012 06 06 at 11.30.03 am
The rate adapting poisson model for information retrieval and object recognition

Gehler, P. V., Holub, A. D., Welling, M.

In Proceedings of the 23rd international conference on Machine learning, pages: 337-344, ICML ’06, ACM, New York, NY, USA, 2006 (inproceedings)

project page pdf DOI [BibTex]

project page pdf DOI [BibTex]


Thumb xl screen shot 2012 06 06 at 11.31.38 am
Implicit Wiener Series, Part II: Regularised estimation

Gehler, P., Franz, M.

(148), Max Planck Institute, 2006 (techreport)

pdf [BibTex]


Thumb xl iwcm
Tracking complex objects using graphical object models

Sigal, L., Zhu, Y., Comaniciu, D., Black, M. J.

In International Workshop on Complex Motion, LNCS 3417, pages: 223-234, Springer-Verlag, 2006 (inproceedings)

pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


Thumb xl evatr
HumanEva: Synchronized video and motion capture dataset for evaluation of articulated human motion

Sigal, L., Black, M. J.

(CS-06-08), Brown University, Department of Computer Science, 2006 (techreport)

pdf abstract [BibTex]

pdf abstract [BibTex]


Thumb xl neuralcomp
Bayesian population decoding of motor cortical activity using a Kalman filter

Wu, W., Gao, Y., Bienenstock, E., Donoghue, J. P., Black, M. J.

Neural Computation, 18(1):80-118, 2006 (article)

Abstract
Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications, we developed a real-time system that uses Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons. In this study, we used recordings that were previously made in the arm area of primary motor cortex in awake behaving monkeys using a chronically implanted multielectrode microarray. Bayesian inference involves computing the posterior probability of the hand motion conditioned on a sequence of observed firing rates; this is formulated in terms of the product of a likelihood and a prior. The likelihood term models the probability of firing rates given a particular hand motion. We found that a linear gaussian model could be used to approximate this likelihood and could be readily learned from a small amount of training data. The prior term defines a probabilistic model of hand kinematics and was also taken to be a linear gaussian model. Decoding was performed using a Kalman filter, which gives an efficient recursive method for Bayesian inference when the likelihood and prior are linear and gaussian. In off-line experiments, the Kalman filter reconstructions of hand trajectory were more accurate than previously reported results. The resulting decoding algorithm provides a principled probabilistic model of motor-cortical coding, decodes hand motion in real time, provides an estimate of uncertainty, and is straightforward to implement. Additionally the formulation unifies and extends previous models of neural coding while providing insights into the motor-cortical code.

pdf preprint pdf from publisher abstract [BibTex]

pdf preprint pdf from publisher abstract [BibTex]


Thumb xl bildschirmfoto 2013 01 16 um 10.16.16
Hierarchical Approach for Articulated 3D Pose-Estimation and Tracking (extended abstract)

Sigal, L., Black, M. J.

In Learning, Representation and Context for Human Sensing in Video Workshop (in conjunction with CVPR), 2006 (inproceedings)

pdf poster [BibTex]

pdf poster [BibTex]


Thumb xl springs2
Nonlinear physically-based models for decoding motor-cortical population activity

Shakhnarovich, G., Kim, S., Black, M. J.

In Advances in Neural Information Processing Systems 19, NIPS-2006, pages: 1257-1264, MIT Press, 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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A comparison of decoding models for imagined motion from human motor cortex

Kim, S., Simeral, J., Donoghue, J. P., Hocherberg, L. R., Friehs, G., Mukand, J. A., Chen, D., Black, M. J.

Program No. 256.11. 2006 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Atlanta, GA, 2006, Online (conference)

[BibTex]

[BibTex]


Thumb xl film
Denoising archival films using a learned Bayesian model

Moldovan, T. M., Roth, S., Black, M. J.

In Int. Conf. on Image Processing, ICIP, pages: 2641-2644, Atlanta, 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl bp
Efficient belief propagation with learned higher-order Markov random fields

Lan, X., Roth, S., Huttenlocher, D., Black, M. J.

In European Conference on Computer Vision, ECCV, II, pages: 269-282, Graz, Austria, 2006 (inproceedings)

pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


Thumb xl screen shot 2012 06 06 at 11.15.02 am
Products of “Edge-perts”

Gehler, P., Welling, M.

In Advances in Neural Information Processing Systems 18, pages: 419-426, (Editors: Weiss, Y. and Sch"olkopf, B. and Platt, J.), MIT Press, Cambridge, MA, 2006 (incollection)

pdf [BibTex]

pdf [BibTex]


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Modeling neural control of physically realistic movement

Shaknarovich, G., Kim, S., Donoghue, J. P., Hocherberg, L. R., Friehs, G., Mukand, J. A., Chen, D., Black, M. J.

Program No. 256.12. 2006 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Atlanta, GA, 2006, Online (conference)

[BibTex]

[BibTex]

2005


Thumb xl ivc05
Representing cyclic human motion using functional analysis

Ormoneit, D., Black, M. J., Hastie, T., Kjellström, H.

Image and Vision Computing, 23(14):1264-1276, December 2005 (article)

Abstract
We present a robust automatic method for modeling cyclic 3D human motion such as walking using motion-capture data. The pose of the body is represented by a time-series of joint angles which are automatically segmented into a sequence of motion cycles. The mean and the principal components of these cycles are computed using a new algorithm that enforces smooth transitions between the cycles by operating in the Fourier domain. Key to this method is its ability to automatically deal with noise and missing data. A learned walking model is then exploited for Bayesian tracking of 3D human motion.

pdf pdf from publisher DOI [BibTex]

2005

pdf pdf from publisher DOI [BibTex]


Thumb xl pets 2005 copy
A quantitative evaluation of video-based 3D person tracking

Balan, A. O., Sigal, L., Black, M. J.

In The Second Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS, pages: 349-356, October 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl embs05
Inferring attentional state and kinematics from motor cortical firing rates

Wood, F., Prabhat, , Donoghue, J. P., Black, M. J.

In Proc. IEEE Engineering in Medicine and Biology Society, pages: 1544-1547, September 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl arma
Motor cortical decoding using an autoregressive moving average model

Fisher, J., Black, M. J.

In Proc. IEEE Engineering in Medicine and Biology Society, pages: 1469-1472, September 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl cvpr2005
Fields of Experts: A framework for learning image priors

Roth, S., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition, 2, pages: 860-867, June 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl picture for seq 15 stabilization
A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video

Yalcin, H. C. R. B. M. J. H. M.

IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Video Proceedings,, pages: 1202, 2005 (patent)

YouTube pdf [BibTex]

YouTube pdf [BibTex]


Thumb xl iccv05roth
On the spatial statistics of optical flow

(Marr Prize, Honorable Mention)

Roth, S., Black, M. J.

In International Conf. on Computer Vision, International Conf. on Computer Vision, pages: 42-49, 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl nips05
Modeling neural population spiking activity with Gibbs distributions

Wood, F., Roth, S., Black, M. J.

In Advances in Neural Information Processing Systems 18, pages: 1537-1544, 2005 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Energy-based models of motor cortical population activity

Wood, F., Black, M.

Program No. 689.20. 2005 Abstract Viewer/Itinerary Planner, Society for Neuroscience, Washington, DC, 2005 (conference)

abstract [BibTex]

abstract [BibTex]

2003


Thumb xl iccv2003 copy
Image statistics and anisotropic diffusion

Scharr, H., Black, M. J., Haussecker, H.

In Int. Conf. on Computer Vision, pages: 840-847, October 2003 (inproceedings)

pdf [BibTex]

2003

pdf [BibTex]


Thumb xl switching2003
A switching Kalman filter model for the motor cortical coding of hand motion

Wu, W., Black, M. J., Mumford, D., Gao, Y., Bienenstock, E., Donoghue, J. P.

In Proc. IEEE Engineering in Medicine and Biology Society, pages: 2083-2086, September 2003 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Learning the statistics of people in images and video

Sidenbladh, H., Black, M. J.

International Journal of Computer Vision, 54(1-3):183-209, August 2003 (article)

Abstract
This paper address the problems of modeling the appearance of humans and distinguishing human appearance from the appearance of general scenes. We seek a model of appearance and motion that is generic in that it accounts for the ways in which people's appearance varies and, at the same time, is specific enough to be useful for tracking people in natural scenes. Given a 3D model of the person projected into an image we model the likelihood of observing various image cues conditioned on the predicted locations and orientations of the limbs. These cues are taken to be steered filter responses corresponding to edges, ridges, and motion-compensated temporal differences. Motivated by work on the statistics of natural scenes, the statistics of these filter responses for human limbs are learned from training images containing hand-labeled limb regions. Similarly, the statistics of the filter responses in general scenes are learned to define a “background” distribution. The likelihood of observing a scene given a predicted pose of a person is computed, for each limb, using the likelihood ratio between the learned foreground (person) and background distributions. Adopting a Bayesian formulation allows cues to be combined in a principled way. Furthermore, the use of learned distributions obviates the need for hand-tuned image noise models and thresholds. The paper provides a detailed analysis of the statistics of how people appear in scenes and provides a connection between work on natural image statistics and the Bayesian tracking of people.

pdf pdf from publisher code DOI [BibTex]

pdf pdf from publisher code DOI [BibTex]


Thumb xl delatorreijcvteaser
A framework for robust subspace learning

De la Torre, F., Black, M. J.

International Journal of Computer Vision, 54(1-3):117-142, August 2003 (article)

Abstract
Many computer vision, signal processing and statistical problems can be posed as problems of learning low dimensional linear or multi-linear models. These models have been widely used for the representation of shape, appearance, motion, etc., in computer vision applications. Methods for learning linear models can be seen as a special case of subspace fitting. One draw-back of previous learning methods is that they are based on least squares estimation techniques and hence fail to account for “outliers” which are common in realistic training sets. We review previous approaches for making linear learning methods robust to outliers and present a new method that uses an intra-sample outlier process to account for pixel outliers. We develop the theory of Robust Subspace Learning (RSL) for linear models within a continuous optimization framework based on robust M-estimation. The framework applies to a variety of linear learning problems in computer vision including eigen-analysis and structure from motion. Several synthetic and natural examples are used to develop and illustrate the theory and applications of robust subspace learning in computer vision.

pdf code pdf from publisher Project Page [BibTex]

pdf code pdf from publisher Project Page [BibTex]


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Guest editorial: Computational vision at Brown

Black, M. J., Kimia, B.

International Journal of Computer Vision, 54(1-3):5-11, August 2003 (article)

pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


Thumb xl cviu91teaser
Robust parameterized component analysis: Theory and applications to 2D facial appearance models

De la Torre, F., Black, M. J.

Computer Vision and Image Understanding, 91(1-2):53-71, July 2003 (article)

Abstract
Principal component analysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion in images. 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 consider 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 error-prone hand alignment and cropping process. Instead, the main contribution of this paper is to 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. We illustrate the use of the 2D PSFAM model with preliminary experiments relevant to applications including video-conferencing and avatar animation.

pdf [BibTex]

pdf [BibTex]


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A Gaussian mixture model for the motor cortical coding of hand motion

Wu, W., Mumford, D., Black, M. J., Gao, Y., Bienenstock, E., Donoghue, J. P.

Neural Control of Movement, Santa Barbara, CA, April 2003 (conference)

abstract [BibTex]

abstract [BibTex]


Thumb xl bildschirmfoto 2013 01 15 um 09.35.12
Connecting brains with machines: The neural control of 2D cursor movement

Black, M. J., Bienenstock, E., Donoghue, J. P., Serruya, M., Wu, W., Gao, Y.

In 1st International IEEE/EMBS Conference on Neural Engineering, pages: 580-583, Capri, Italy, March 2003 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 15 um 09.44.01
A quantitative comparison of linear and non-linear models of motor cortical activity for the encoding and decoding of arm motions

Gao, Y., Black, M. J., Bienenstock, E., Wu, W., Donoghue, J. P.

In 1st International IEEE/EMBS Conference on Neural Engineering, pages: 189-192, Capri, Italy, March 2003 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Accuracy of manual spike sorting: Results for the Utah intracortical array

Wood, F., Fellows, M., Vargas-Irwin, C., Black, M. J., Donoghue, J. P.

Program No. 279.2. 2003, Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2003, Online (conference)

abstract [BibTex]

abstract [BibTex]