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2015


Thumb xl grassmanteaser
Scalable Robust Principal Component Analysis using Grassmann Averages

Hauberg, S., Feragen, A., Enficiaud, R., Black, M.

IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), December 2015 (article)

Abstract
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

preprint pdf from publisher supplemental Project Page [BibTex]

2015


Thumb xl splitbodieswebteaser2
SMPL: A Skinned Multi-Person Linear Model

Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M. J.

ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:1-248:16, ACM, New York, NY, October 2015 (article)

Abstract
We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity-dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. We quantitatively evaluate variants of SMPL using linear or dual-quaternion blend skinning and show that both are more accurate than a Blend-SCAPE model trained on the same data. We also extend SMPL to realistically model dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

pdf video code/model errata DOI Project Page Project Page [BibTex]

pdf video code/model errata DOI Project Page Project Page [BibTex]


Thumb xl dynateaser
Dyna: A Model of Dynamic Human Shape in Motion

Pons-Moll, G., Romero, J., Mahmood, N., Black, M. J.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 34(4):120:1-120:14, ACM, August 2015 (article)

Abstract
To look human, digital full-body avatars need to have soft tissue deformations like those of real people. We learn a model of soft-tissue deformations from examples using a high-resolution 4D capture system and a method that accurately registers a template mesh to sequences of 3D scans. Using over 40,000 scans of ten subjects, we learn how soft tissue motion causes mesh triangles to deform relative to a base 3D body model. Our Dyna model uses a low-dimensional linear subspace to approximate soft-tissue deformation and relates the subspace coefficients to the changing pose of the body. Dyna uses a second-order auto-regressive model that predicts soft-tissue deformations based on previous deformations, the velocity and acceleration of the body, and the angular velocities and accelerations of the limbs. Dyna also models how deformations vary with a person’s body mass index (BMI), producing different deformations for people with different shapes. Dyna realistically represents the dynamics of soft tissue for previously unseen subjects and motions. We provide tools for animators to modify the deformations and apply them to new stylized characters.

pdf preprint video data DOI Project Page Project Page [BibTex]

pdf preprint video data DOI Project Page Project Page [BibTex]


Thumb xl objs2acts
Linking Objects to Actions: Encoding of Target Object and Grasping Strategy in Primate Ventral Premotor Cortex

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

Journal of Neuroscience, 35(30):10888-10897, July 2015 (article)

Abstract
Neural activity in ventral premotor cortex (PMv) has been associated with the process of matching perceived objects with the motor commands needed to grasp them. It remains unclear how PMv networks can flexibly link percepts of objects affording multiple grasp options into a final desired hand action. Here, we use a relational encoding approach to track the functional state of PMv neuronal ensembles in macaque monkeys through the process of passive viewing, grip planning, and grasping movement execution. We used objects affording multiple possible grip strategies. The task included separate instructed delay periods for object presentation and grip instruction. This approach allowed us to distinguish responses elicited by the visual presentation of the objects from those associated with selecting a given motor plan for grasping. We show that PMv continuously incorporates information related to object shape and grip strategy as it becomes available, revealing a transition from a set of ensemble states initially most closely related to objects, to a new set of ensemble patterns reflecting unique object-grip combinations. These results suggest that PMv dynamically combines percepts, gradually navigating toward activity patterns associated with specific volitional actions, rather than directly mapping perceptual object properties onto categorical grip representations. Our results support the idea that PMv is part of a network that dynamically computes motor plans from perceptual information. Significance Statement: The present work demonstrates that the activity of groups of neurons in primate ventral premotor cortex reflects information related to visually presented objects, as well as the motor strategy used to grasp them, linking individual objects to multiple possible grips. PMv could provide useful control signals for neuroprosthetic assistive devices designed to interact with objects in a flexible way.

publisher link DOI Project Page [BibTex]

publisher link DOI Project Page [BibTex]


Thumb xl screen shot 2015 10 14 at 08.57.57
Multi-view and 3D Deformable Part Models

Pepik, B., Stark, M., Gehler, P., Schiele, B.

Pattern Analysis and Machine Intelligence, 37(11):14, IEEE, March 2015 (article)

Abstract
As objects are inherently 3-dimensional, they have been modeled in 3D in the early days of computer vision. Due to the ambiguities arising from mapping 2D features to 3D models, 3D object representations have been neglected and 2D feature-based models are the predominant paradigm in object detection nowadays. While such models have achieved outstanding bounding box detection performance, they come with limited expressiveness, as they are clearly limited in their capability of reasoning about 3D shape or viewpoints. In this work, we bring the worlds of 3D and 2D object representations closer, by building an object detector which leverages the expressive power of 3D object representations while at the same time can be robustly matched to image evidence. To that end, we gradually extend the successful deformable part model [1] to include viewpoint information and part-level 3D geometry information, resulting in several different models with different level of expressiveness. We end up with a 3D object model, consisting of multiple object parts represented in 3D and a continuous appearance model. We experimentally verify that our models, while providing richer object hypotheses than the 2D object models, provide consistently better joint object localization and viewpoint estimation than the state-of-the-art multi-view and 3D object detectors on various benchmarks (KITTI [2], 3D object classes [3], Pascal3D+ [4], Pascal VOC 2007 [5], EPFL multi-view cars [6]).

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Thumb xl ssimssmall
Spike train SIMilarity Space (SSIMS): A framework for single neuron and ensemble data analysis

Vargas-Irwin, C. E., Brandman, D. M., Zimmermann, J. B., Donoghue, J. P., Black, M. J.

Neural Computation, 27(1):1-31, MIT Press, January 2015 (article)

Abstract
We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.

pdf: publisher site pdf: author's proof DOI Project Page [BibTex]

pdf: publisher site pdf: author's proof DOI Project Page [BibTex]


Thumb xl thumb teaser mrg
Metric Regression Forests for Correspondence Estimation

Pons-Moll, G., Taylor, J., Shotton, J., Hertzmann, A., Fitzgibbon, A.

International Journal of Computer Vision, pages: 1-13, 2015 (article)

springer PDF Project Page [BibTex]

springer PDF Project Page [BibTex]


Thumb xl fotorobos
Formation control driven by cooperative object tracking

Lima, P., Ahmad, A., Dias, A., Conceição, A., Moreira, A., Silva, E., Almeida, L., Oliveira, L., Nascimento, T.

Robotics and Autonomous Systems, 63(1):68-79, 2015 (article)

Abstract
In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles.

DOI [BibTex]

DOI [BibTex]

2012


Thumb xl eigenmaps
An SVD-Based Approach for Ghost Detection and Removal in High Dynamic Range Images

Srikantha, A., Sidibe, D., Meriaudeau, F.

International Conference on Pattern Recognition (ICPR), pages: 380-383, November 2012 (article)

pdf [BibTex]

2012

pdf [BibTex]


Thumb xl coregtr
Coregistration: Supplemental Material

Hirshberg, D., Loper, M., Rachlin, E., Black, M. J.

(No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf [BibTex]

pdf [BibTex]


Thumb xl lietr
Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material

Freifeld, O., Black, M. J.

(No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Thumb xl posear
Coupled Action Recognition and Pose Estimation from Multiple Views

Yao, A., Gall, J., van Gool, L.

International Journal of Computer Vision, 100(1):16-37, October 2012 (article)

publisher's site code pdf Project Page Project Page Project Page [BibTex]

publisher's site code pdf Project Page Project Page Project Page [BibTex]


Thumb xl sinteltr
MPI-Sintel Optical Flow Benchmark: Supplemental Material

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

(No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Thumb xl representativecrop
DRAPE: DRessing Any PErson

Guan, P., Reiss, L., Hirshberg, D., Weiss, A., Black, M. J.

ACM Trans. on Graphics (Proc. SIGGRAPH), 31(4):35:1-35:10, July 2012 (article)

Abstract
We describe a complete system for animating realistic clothing on synthetic bodies of any shape and pose without manual intervention. The key component of the method is a model of clothing called DRAPE (DRessing Any PErson) that is learned from a physics-based simulation of clothing on bodies of different shapes and poses. The DRAPE model has the desirable property of "factoring" clothing deformations due to body shape from those due to pose variation. This factorization provides an approximation to the physical clothing deformation and greatly simplifies clothing synthesis. Given a parameterized model of the human body with known shape and pose parameters, we describe an algorithm that dresses the body with a garment that is customized to fit and possesses realistic wrinkles. DRAPE can be used to dress static bodies or animated sequences with a learned model of the cloth dynamics. Since the method is fully automated, it is appropriate for dressing large numbers of virtual characters of varying shape. The method is significantly more efficient than physical simulation.

YouTube pdf talk Project Page Project Page [BibTex]

YouTube pdf talk Project Page Project Page [BibTex]


Thumb xl ghosthdr
Ghost Detection and Removal for High Dynamic Range Images: Recent Advances

Srikantha, A., Sidib’e, D.

Signal Processing: Image Communication, 27, pages: 650-662, July 2012 (article)

pdf link (url) [BibTex]

pdf link (url) [BibTex]


Thumb xl thumb screen shot 2012 10 06 at 11.48.38 am
Visual Servoing on Unknown Objects

Gratal, X., Romero, J., Bohg, J., Kragic, D.

Mechatronics, 22(4):423-435, Elsevier, June 2012, Visual Servoing \{SI\} (article)

Abstract
We study visual servoing in a framework of detection and grasping of unknown objects. Classically, visual servoing has been used for applications where the object to be servoed on is known to the robot prior to the task execution. In addition, most of the methods concentrate on aligning the robot hand with the object without grasping it. In our work, visual servoing techniques are used as building blocks in a system capable of detecting and grasping unknown objects in natural scenes. We show how different visual servoing techniques facilitate a complete grasping cycle.

Grasping sequence video Offline calibration video Pdf DOI [BibTex]

Grasping sequence video Offline calibration video Pdf DOI [BibTex]


Thumb xl jneuroscicrop
Visual Orientation and Directional Selectivity Through Thalamic Synchrony

Stanley, G., Jin, J., Wang, Y., Desbordes, G., Wang, Q., Black, M., Alonso, J.

Journal of Neuroscience, 32(26):9073-9088, June 2012 (article)

Abstract
Thalamic neurons respond to visual scenes by generating synchronous spike trains on the timescale of 10–20 ms that are very effective at driving cortical targets. Here we demonstrate that this synchronous activity contains unexpectedly rich information about fundamental properties of visual stimuli. We report that the occurrence of synchronous firing of cat thalamic cells with highly overlapping receptive fields is strongly sensitive to the orientation and the direction of motion of the visual stimulus. We show that this stimulus selectivity is robust, remaining relatively unchanged under different contrasts and temporal frequencies (stimulus velocities). A computational analysis based on an integrate-and-fire model of the direct thalamic input to a layer 4 cortical cell reveals a strong correlation between the degree of thalamic synchrony and the nonlinear relationship between cortical membrane potential and the resultant firing rate. Together, these findings suggest a novel population code in the synchronous firing of neurons in the early visual pathway that could serve as the substrate for establishing cortical representations of the visual scene.

preprint publisher's site Project Page [BibTex]

preprint publisher's site Project Page [BibTex]


Thumb xl bilinear
Bilinear Spatiotemporal Basis Models

Akhter, I., Simon, T., Khan, S., Matthews, I., Sheikh, Y.

ACM Transactions on Graphics (TOG), 31(2):17, ACM, April 2012 (article)

Abstract
A variety of dynamic objects, such as faces, bodies, and cloth, are represented in computer graphics as a collection of moving spatial landmarks. Spatiotemporal data is inherent in a number of graphics applications including animation, simulation, and object and camera tracking. The principal modes of variation in the spatial geometry of objects are typically modeled using dimensionality reduction techniques, while concurrently, trajectory representations like splines and autoregressive models are widely used to exploit the temporal regularity of deformation. In this article, we present the bilinear spatiotemporal basis as a model that simultaneously exploits spatial and temporal regularity while maintaining the ability to generalize well to new sequences. This factorization allows the use of analytical, predefined functions to represent temporal variation (e.g., B-Splines or the Discrete Cosine Transform) resulting in efficient model representation and estimation. The model can be interpreted as representing the data as a linear combination of spatiotemporal sequences consisting of shape modes oscillating over time at key frequencies. We apply the bilinear model to natural spatiotemporal phenomena, including face, body, and cloth motion data, and compare it in terms of compaction, generalization ability, predictive precision, and efficiency to existing models. We demonstrate the application of the model to a number of graphics tasks including labeling, gap-filling, denoising, and motion touch-up.

pdf project page link (url) [BibTex]

pdf project page link (url) [BibTex]


Thumb xl humim2012
HUMIM Software for Articulated Tracking

Soren Hauberg, Kim S. Pedersen

(01/2012), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

Code PDF [BibTex]

Code PDF [BibTex]


Thumb xl tr feragen2012
A geometric framework for statistics on trees

Aasa Feragen, Mads Nielsen, Soren Hauberg, Pechin Lo, Marleen de Bruijne, Francois Lauze

(11/02), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

PDF [BibTex]

PDF [BibTex]


Thumb xl thumb latent space2
A metric for comparing the anthropomorphic motion capability of artificial hands

Feix, T., Romero, J., Ek, C. H., Schmiedmayer, H., Kragic, D.

IEEE RAS Transactions on Robotics, TRO, pages: 974-980, 2012 (article)

Publisher site Human Grasping Database Project [BibTex]

Publisher site Human Grasping Database Project [BibTex]


Thumb xl rat4
The Ankyrin 3 (ANK3) Bipolar Disorder Gene Regulates Psychiatric-related Behaviors that are Modulated by Lithium and Stress

Leussis, M., Berry-Scott, E., Saito, M., Jhuang, H., Haan, G., Alkan, O., Luce, C., Madison, J., Sklar, P., Serre, T., Root, D., Petryshen, T.

Biological Psychiatry , 2012 (article)

Prepublication Article Abstract [BibTex]

Prepublication Article Abstract [BibTex]


Thumb xl imavis2012
Natural Metrics and Least-Committed Priors for Articulated Tracking

Soren Hauberg, Stefan Sommer, Kim S. Pedersen

Image and Vision Computing, 30(6-7):453-461, Elsevier, 2012 (article)

Publishers site Code PDF [BibTex]

Publishers site Code PDF [BibTex]

2008


Thumb xl screen shot 2012 06 06 at 11.28.04 am
Infinite Kernel Learning

Gehler, P., Nowozin, S.

(178), Max Planck Institute, octomber 2008 (techreport)

project page pdf [BibTex]

2008

project page pdf [BibTex]


Thumb xl jnm
A non-parametric Bayesian alternative to spike sorting

Wood, F., Black, M. J.

J. Neuroscience Methods, 173(1):1–12, August 2008 (article)

Abstract
The analysis of extra-cellular neural recordings typically begins with careful spike sorting and all analysis of the data then rests on the correctness of the resulting spike trains. In many situations this is unproblematic as experimental and spike sorting procedures often focus on well isolated units. There is evidence in the literature, however, that errors in spike sorting can occur even with carefully collected and selected data. Additionally, chronically implanted electrodes and arrays with fixed electrodes cannot be easily adjusted to provide well isolated units. In these situations, multiple units may be recorded and the assignment of waveforms to units may be ambiguous. At the same time, analysis of such data may be both scientifically important and clinically relevant. In this paper we address this issue using a novel probabilistic model that accounts for several important sources of uncertainty and error in spike sorting. In lieu of sorting neural data to produce a single best spike train, we estimate a probabilistic model of spike trains given the observed data. We show how such a distribution over spike sortings can support standard neuroscientific questions while providing a representation of uncertainty in the analysis. As a representative illustration of the approach, we analyzed primary motor cortical tuning with respect to hand movement in data recorded with a chronic multi-electrode array in non-human primates.We found that the probabilistic analysis generally agrees with human sorters but suggests the presence of tuned units not detected by humans.

pdf preprint pdf from publisher PubMed [BibTex]

pdf preprint pdf from publisher PubMed [BibTex]


Thumb xl pointclickimagesmall2
Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

(J. Neural Engineering Highlights of 2008 Collection)

Kim, S., Simeral, J., Hochberg, L., Donoghue, J. P., Black, M. J.

J. Neural Engineering, 5, pages: 455–476, 2008 (article)

Abstract
Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor’s velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding.

pdf preprint pdf from publisher [BibTex]

pdf preprint pdf from publisher [BibTex]


Thumb xl woodtr
Incremental nonparametric Bayesian regression

Wood, F., Grollman, D. H., Heller, K. A., Jenkins, O. C., Black, M. J.

(CS-08-07), Brown University, Department of Computer Science, 2008 (techreport)

pdf [BibTex]

pdf [BibTex]


Thumb xl jmiv08brownian
Brownian Warps for Non-Rigid Registration

Mads Nielsen, Peter Johansen, Andrew Jackson, Benny Lautrup, Soren Hauberg

Journal of Mathematical Imaging and Vision, 31, pages: 221-231, Springer Netherlands, 2008 (article)

Publishers site PDF [BibTex]

Publishers site PDF [BibTex]


Thumb xl jmiv08theater
An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application

Soren Hauberg, Jakob Sloth

Journal of Mathematical Imaging and Vision, 31, pages: 165-170, Springer Netherlands, 2008 (article)

Publishers site Paper site PDF [BibTex]

Publishers site Paper site PDF [BibTex]

2006


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]

2006


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]

1994


Thumb xl cviu
A computational and evolutionary perspective on the role of representation in computer vision

Tarr, M. J., Black, M. J.

CVGIP: Image Understanding, 60(1):65-73, July 1994 (article)

Abstract
Recently, the assumed goal of computer vision, reconstructing a representation of the scene, has been critcized as unproductive and impractical. Critics have suggested that the reconstructive approach should be supplanted by a new purposive approach that emphasizes functionality and task driven perception at the cost of general vision. In response to these arguments, we claim that the recovery paradigm central to the reconstructive approach is viable, and, moreover, provides a promising framework for understanding and modeling general purpose vision in humans and machines. An examination of the goals of vision from an evolutionary perspective and a case study involving the recovery of optic flow support this hypothesis. In particular, while we acknowledge that there are instances where the purposive approach may be appropriate, these are insufficient for implementing the wide range of visual tasks exhibited by humans (the kind of flexible vision system presumed to be an end-goal of artificial intelligence). Furthermore, there are instances, such as recent work on the estimation of optic flow, where the recovery paradigm may yield useful and robust results. Thus, contrary to certain claims, the purposive approach does not obviate the need for recovery and reconstruction of flexible representations of the world.

pdf [BibTex]

1994

pdf [BibTex]


Thumb xl cviu
Reconstruction and purpose

Tarr, M. J., Black, M. J.

CVGIP: Image Understanding, 60(1):113-118, July 1994 (article)

pdf [BibTex]

pdf [BibTex]