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2018


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Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time

Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., Hilliges, O., Pons-Moll, G.

ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 37, pages: 185:1-185:15, ACM, November 2018, Two first authors contributed equally (article)

Abstract
We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so, we address several difficult challenges. First, the problem is severely under-constrained as multiple pose parameters produce the same IMU orientations. Second, capturing IMU data in conjunction with ground-truth poses is expensive and difficult to do in many target application scenarios (e.g., outdoors). Third, modeling temporal dependencies through non-linear optimization has proven effective in prior work but makes real-time prediction infeasible. To address this important limitation, we learn the temporal pose priors using deep learning. To learn from sufficient data, we synthesize IMU data from motion capture datasets. A bi-directional RNN architecture leverages past and future information that is available at training time. At test time, we deploy the network in a sliding window fashion, retaining real time capabilities. To evaluate our method, we recorded DIP-IMU, a dataset consisting of 10 subjects wearing 17 IMUs for validation in 64 sequences with 330,000 time instants; this constitutes the largest IMU dataset publicly available. We quantitatively evaluate our approach on multiple datasets and show results from a real-time implementation. DIP-IMU and the code are available for research purposes.

data code pdf preprint video DOI Project Page [BibTex]

2018

data code pdf preprint video DOI Project Page [BibTex]


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Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles

Price, E., Lawless, G., Ludwig, R., Martinovic, I., Buelthoff, H. H., Black, M. J., Ahmad, A.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3193-3200, IEEE, October 2018, Also accepted and presented in the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (article)

Abstract
Multi-camera tracking of humans and animals in outdoor environments is a relevant and challenging problem. Our approach to it involves a team of cooperating micro aerial vehicles (MAVs) with on-board cameras only. DNNs often fail at objects with small scale or far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this paper is how to achieve on-board, online, continuous and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs and active selection of most informative regions of image. We demonstrate the efficiency of our approach through simulations with up to 16 robots and real robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. ROS-based source code is provided for the benefit of the community.

Published Version link (url) DOI [BibTex]

Published Version link (url) DOI [BibTex]


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First Impressions of Personality Traits From Body Shapes

Hu, Y., Parde, C. J., Hill, M. Q., Mahmood, N., O’Toole, A. J.

Psychological Science, 29(12):1969-–1983, October 2018 (article)

Abstract
People infer the personalities of others from their facial appearance. Whether they do so from body shapes is less studied. We explored personality inferences made from body shapes. Participants rated personality traits for male and female bodies generated with a three-dimensional body model. Multivariate spaces created from these ratings indicated that people evaluate bodies on valence and agency in ways that directly contrast positive and negative traits from the Big Five domains. Body-trait stereotypes based on the trait ratings revealed a myriad of diverse body shapes that typify individual traits. Personality-trait profiles were predicted reliably from a subset of the body-shape features used to specify the three-dimensional bodies. Body features related to extraversion and conscientiousness were predicted with the highest consensus, followed by openness traits. This study provides the first comprehensive look at the range, diversity, and reliability of personality inferences that people make from body shapes.

publisher site pdf DOI [BibTex]

publisher site pdf DOI [BibTex]


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Visual Perception and Evaluation of Photo-Realistic Self-Avatars From 3D Body Scans in Males and Females

Thaler, A., Piryankova, I., Stefanucci, J. K., Pujades, S., de la Rosa, S., Streuber, S., Romero, J., Black, M. J., Mohler, B. J.

Frontiers in ICT, 5, pages: 1-14, September 2018 (article)

Abstract
The creation or streaming of photo-realistic self-avatars is important for virtual reality applications that aim for perception and action to replicate real world experience. The appearance and recognition of a digital self-avatar may be especially important for applications related to telepresence, embodied virtual reality, or immersive games. We investigated gender differences in the use of visual cues (shape, texture) of a self-avatar for estimating body weight and evaluating avatar appearance. A full-body scanner was used to capture each participant's body geometry and color information and a set of 3D virtual avatars with realistic weight variations was created based on a statistical body model. Additionally, a second set of avatars was created with an average underlying body shape matched to each participant’s height and weight. In four sets of psychophysical experiments, the influence of visual cues on the accuracy of body weight estimation and the sensitivity to weight changes was assessed by manipulating body shape (own, average) and texture (own photo-realistic, checkerboard). The avatars were presented on a large-screen display, and participants responded to whether the avatar's weight corresponded to their own weight. Participants also adjusted the avatar's weight to their desired weight and evaluated the avatar's appearance with regard to similarity to their own body, uncanniness, and their willingness to accept it as a digital representation of the self. The results of the psychophysical experiments revealed no gender difference in the accuracy of estimating body weight in avatars. However, males accepted a larger weight range of the avatars as corresponding to their own. In terms of the ideal body weight, females but not males desired a thinner body. With regard to the evaluation of avatar appearance, the questionnaire responses suggest that own photo-realistic texture was more important to males for higher similarity ratings, while own body shape seemed to be more important to females. These results argue for gender-specific considerations when creating self-avatars.

pdf DOI [BibTex]

pdf DOI [BibTex]


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Robust Physics-based Motion Retargeting with Realistic Body Shapes

Borno, M. A., Righetti, L., Black, M. J., Delp, S. L., Fiume, E., Romero, J.

Computer Graphics Forum, 37, pages: 6:1-12, July 2018 (article)

Abstract
Motion capture is often retargeted to new, and sometimes drastically different, characters. When the characters take on realistic human shapes, however, we become more sensitive to the motion looking right. This means adapting it to be consistent with the physical constraints imposed by different body shapes. We show how to take realistic 3D human shapes, approximate them using a simplified representation, and animate them so that they move realistically using physically-based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The approach automatically adapts the motion of the mocap subject to the body shape of a target subject. This motion respects the physical properties of the new body and every body shape results in a different and appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters. In an interactive environment, successful retargeting requires adapting the motion to unexpected external forces. We achieve robustness to such forces using a novel LQR-tree formulation. We show that the simulated motions look appropriate to each character’s anatomy and their actions are robust to perturbations.

pdf video Project Page Project Page [BibTex]

pdf video Project Page Project Page [BibTex]


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Assessing body image in anorexia nervosa using biometric self-avatars in virtual reality: Attitudinal components rather than visual body size estimation are distorted

Mölbert, S. C., Thaler, A., Mohler, B. J., Streuber, S., Romero, J., Black, M. J., Zipfel, S., Karnath, H., Giel, K. E.

Psychological Medicine, 48(4):642-653, March 2018 (article)

Abstract
Background: Body image disturbance (BID) is a core symptom of anorexia nervosa (AN), but as yet distinctive features of BID are unknown. The present study aimed at disentangling perceptual and attitudinal components of BID in AN. Methods: We investigated n=24 women with AN and n=24 controls. Based on a 3D body scan, we created realistic virtual 3D bodies (avatars) for each participant that were varied through a range of ±20% of the participants' weights. Avatars were presented in a virtual reality mirror scenario. Using different psychophysical tasks, participants identified and adjusted their actual and their desired body weight. To test for general perceptual biases in estimating body weight, a second experiment investigated perception of weight and shape matched avatars with another identity. Results: Women with AN and controls underestimated their weight, with a trend that women with AN underestimated more. The average desired body of controls had normal weight while the average desired weight of women with AN corresponded to extreme AN (DSM-5). Correlation analyses revealed that desired body weight, but not accuracy of weight estimation, was associated with eating disorder symptoms. In the second experiment, both groups estimated accurately while the most attractive body was similar to Experiment 1. Conclusions: Our results contradict the widespread assumption that patients with AN overestimate their body weight due to visual distortions. Rather, they illustrate that BID might be driven by distorted attitudes with regard to the desired body. Clinical interventions should aim at helping patients with AN to change their desired weight.

doi pdf DOI Project Page [BibTex]


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Body size estimation of self and others in females varying in BMI

Thaler, A., Geuss, M. N., Mölbert, S. C., Giel, K. E., Streuber, S., Romero, J., Black, M. J., Mohler, B. J.

PLoS ONE, 13(2), Febuary 2018 (article)

Abstract
Previous literature suggests that a disturbed ability to accurately identify own body size may contribute to overweight. Here, we investigated the influence of personal body size, indexed by body mass index (BMI), on body size estimation in a non-clinical population of females varying in BMI. We attempted to disentangle general biases in body size estimates and attitudinal influences by manipulating whether participants believed the body stimuli (personalized avatars with realistic weight variations) represented their own body or that of another person. Our results show that the accuracy of own body size estimation is predicted by personal BMI, such that participants with lower BMI underestimated their body size and participants with higher BMI overestimated their body size. Further, participants with higher BMI were less likely to notice the same percentage of weight gain than participants with lower BMI. Importantly, these results were only apparent when participants were judging a virtual body that was their own identity (Experiment 1), but not when they estimated the size of a body with another identity and the same underlying body shape (Experiment 2a). The different influences of BMI on accuracy of body size estimation and sensitivity to weight change for self and other identity suggests that effects of BMI on visual body size estimation are self-specific and not generalizable to other bodies.

pdf DOI Project Page [BibTex]


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Temporal Human Action Segmentation via Dynamic Clustering

Zhang, Y., Sun, H., Tang, S., Neumann, H.

arXiv preprint arXiv:1803.05790, 2018 (article)

Abstract
We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring. Our proposed algorithm is unsupervised, fast, generic to process various types of features, and applica- ble in both the online and offline settings. We perform extensive experiments of processing data streams, and show that our algorithm achieves the state-of- the-art results for both online and offline settings.

link (url) [BibTex]

link (url) [BibTex]


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Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering

Keuper, M., Tang, S., Andres, B., Brox, T., Schiele, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 (article)

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]

2011


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High-quality reflection separation using polarized images

Kong, N., Tai, Y., Shin, S. Y.

IEEE Transactions on Image Processing, 20(12):3393-3405, IEEE Signal Processing Society, December 2011 (article)

Abstract
In this paper, we deal with a problem of separating the effect of reflection from images captured behind glass. The input consists of multiple polarized images captured from the same view point but with different polarizer angles. The output is the high quality separation of the reflection layer and the background layer from the images. We formulate this problem as a constrained optimization problem and propose a framework that allows us to fully exploit the mutually exclusive image information in our input data. We test our approach on various images and demonstrate that our approach can generate good reflection separation results.

Publisher site [BibTex]

2011

Publisher site [BibTex]


no image
A human inspired gaze estimation system

Wulff, J., Sinha, P.

Journal of Vision, 11(11):507-507, ARVO, September 2011 (article)

Abstract
Estimating another person's gaze is a crucial skill in human social interactions. The social component is most apparent in dyadic gaze situations, in which the looker seems to look into the eyes of the observer, thereby signaling interest or a turn to speak. In a triadic situation, on the other hand, the looker's gaze is averted from the observer and directed towards another, specific target. This is mostly interpreted as a cue for joint attention, creating awareness of a predator or another point of interest. In keeping with the task's social significance, humans are very proficient at gaze estimation. Our accuracy ranges from less than one degree for dyadic settings to approximately 2.5 degrees for triadic ones. Our goal in this work is to draw inspiration from human gaze estimation mechanisms in order to create an artificial system that can approach the former's accuracy levels. Since human performance is severely impaired by both image-based degradations (Ando, 2004) and a change of facial configurations (Jenkins & Langton, 2003), the underlying principles are believed to be based both on simple image cues such as contrast/brightness distribution and on more complex geometric processing to reconstruct the actual shape of the head. By incorporating both kinds of cues in our system's design, we are able to surpass the accuracy of existing eye-tracking systems, which rely exclusively on either image-based or geometry-based cues (Yamazoe et al., 2008). A side-benefit of this combined approach is that it allows for gaze estimation despite moderate view-point changes. This is important for settings where subjects, say young children or certain kinds of patients, might not be fully cooperative to allow a careful calibration. Our model and implementation of gaze estimation opens up new experimental questions about human mechanisms while also providing a useful tool for general calibration-free, non-intrusive remote eye-tracking.

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Detecting synchrony in degraded audio-visual streams

Dhandhania, K., Wulff, J., Sinha, P.

Journal of Vision, 11(11):800-800, ARVO, September 2011 (article)

Abstract
Even 8–10 week old infants, when presented with two dynamic faces and a speech stream, look significantly longer at the ‘correct’ talking person (Patterson & Werker, 2003). This is true even though their reduced visual acuity prevents them from utilizing high spatial frequencies. Computational analyses in the field of audio/video synchrony and automatic speaker detection (e.g. Hershey & Movellan, 2000), in contrast, usually depend on high-resolution images. Therefore, the correlation mechanisms found in these computational studies are not directly applicable to the processes through which we learn to integrate the modalities of speech and vision. In this work, we investigated the correlation between speech signals and degraded video signals. We found a high correlation persisting even with high image degradation, resembling the low visual acuity of young infants. Additionally (in a fashion similar to Graf et al., 2002) we explored which parts of the face correlate with the audio in the degraded video sequences. Perfect synchrony and small offsets in the audio were used while finding the correlation, thereby detecting visual events preceding and following audio events. In order to achieve a sufficiently high temporal resolution, high-speed video sequences (500 frames per second) of talking people were used. This is a temporal resolution unachieved in previous studies and has allowed us to capture very subtle and short visual events. We believe that the results of this study might be interesting not only to vision researchers, but, by revealing subtle effects on a very fine timescale, also to people working in computer graphics and the generation and animation of artificial faces.

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Trajectory Space: A Dual Representation for Nonrigid Structure from Motion

Akhter, I., Sheikh, Y., Khan, S., Kanade, T.

Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(7):1442-1456, IEEE, July 2011 (article)

Abstract
Existing approaches to nonrigid structure from motion assume that the instantaneous 3D shape of a deforming object is a linear combination of basis shapes. These basis are object dependent and therefore have to be estimated anew for each video sequence. In contrast, we propose a dual approach to describe the evolving 3D structure in trajectory space by a linear combination of basis trajectories. We describe the dual relationship between the two approaches, showing that they both have equal power for representing 3D structure. We further show that the temporal smoothness in 3D trajectories alone can be used for recovering nonrigid structure from a moving camera. The principal advantage of expressing deforming 3D structure in trajectory space is that we can define an object independent basis. This results in a significant reduction in unknowns, and corresponding stability in estimation. We propose the use of the Discrete Cosine Transform (DCT) as the object independent basis and empirically demonstrate that it approaches Principal Component Analysis (PCA) for natural motions. We report the performance of the proposed method, quantitatively using motion capture data, and qualitatively on several video sequences exhibiting nonrigid motions including piecewise rigid motion, partially nonrigid motion (such as a facial expressions), and highly nonrigid motion (such as a person walking or dancing).

pdf project page [BibTex]

pdf project page [BibTex]


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Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation

Sigal, L., Isard, M., Haussecker, H., Black, M. J.

International Journal of Computer Vision, 98(1):15-48, Springer Netherlands, May 2011 (article)

Abstract
We formulate the problem of 3D human pose estimation and tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of loosely-connected body-parts. In particular, we model the body using an undirected graphical model in which nodes correspond to parts and edges to kinematic, penetration, and temporal constraints imposed by the joints and the world. These constraints are encoded using pair-wise statistical distributions, that are learned from motion-capture training data. Human pose and motion estimation is formulated as inference in this graphical model and is solved using Particle Message Passing (PaMPas). PaMPas is a form of non-parametric belief propagation that uses a variation of particle filtering that can be applied over a general graphical model with loops. The loose-limbed model and decentralized graph structure allow us to incorporate information from "bottom-up" visual cues, such as limb and head detectors, into the inference process. These detectors enable automatic initialization and aid recovery from transient tracking failures. We illustrate the method by automatically tracking people in multi-view imagery using a set of calibrated cameras and present quantitative evaluation using the HumanEva dataset.

pdf publisher's site link (url) Project Page Project Page [BibTex]

pdf publisher's site link (url) Project Page Project Page [BibTex]


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Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

Kim, S., Simeral, J. D., Hochberg, L. R., Donoghue, J. P., Friehs, G. M., Black, M. J.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(2):193-203, April 2011 (article)

Abstract
We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2D computer cursor in any desired direction on a computer screen, hold it still and click on the area of interest. This direct brain-computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants were able to control the cursor motion accurately and click on specified targets with a small error rate (< 3% in one participant). This study suggests that signals from a small ensemble of motor cortical neurons (~40) can be used for natural point-and-click 2D cursor control of a personal computer.

pdf publishers's site pub med link (url) Project Page [BibTex]

pdf publishers's site pub med link (url) Project Page [BibTex]


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A Database and Evaluation Methodology for Optical Flow

Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., Szeliski, R.

International Journal of Computer Vision, 92(1):1-31, March 2011 (article)

Abstract
The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex natural scenes, including nonrigid motion, real sensor noise, and motion discontinuities. We propose a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: (1) sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture, (2) realistic synthetic sequences, (3) high frame-rate video used to study interpolation error, and (4) modified stereo sequences of static scenes. In addition to the average angular error used by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data to establish the current state of the art. We also made the data freely available on the web at http://vision.middlebury.edu/flow/ . Subsequently a number of researchers have uploaded their results to our website and published papers using the data. A significant improvement in performance has already been achieved. In this paper we analyze the results obtained to date and draw a large number of conclusions from them.

pdf pdf from publisher Middlebury Flow Evaluation Website [BibTex]

pdf pdf from publisher Middlebury Flow Evaluation Website [BibTex]


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Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array

(J. Neural Engineering Highlights of 2011 Collection. JNE top 10 cited papers of 2010-2011.)

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

J. of Neural Engineering, 8(2):025027, 2011 (article)

Abstract
The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.

pdf pdf from publisher link (url) Project Page [BibTex]


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Modelling pipeline for subject-specific arterial blood flow—A review

Igor Sazonov, Si Yong Yeo, Rhodri Bevan, Xianghua Xie, Raoul van Loon, Perumal Nithiarasu

International Journal for Numerical Methods in Biomedical Engineering, 27(12):1868–1910, 2011 (article)

Abstract
In this paper, a robust and semi-automatic modelling pipeline for blood flow through subject-specific arterial geometries is presented. The framework developed consists of image segmentation, domain discretization (meshing) and fluid dynamics. All the three subtopics of the pipeline are explained using an example of flow through a severely stenosed human carotid artery. In the Introduction, the state-of-the-art of both image segmentation and meshing is presented in some detail, and wherever possible the advantages and disadvantages of the existing methods are analysed. Followed by this, the deformable model used for image segmentation is presented. This model is based upon a geometrical potential force (GPF), which is a function of the image. Both the GPF calculation and level set determination are explained. Following the image segmentation method, a semi-automatic meshing method used in the present study is explained in full detail. All the relevant techniques required to generate a valid domain discretization are presented. These techniques include generating a valid surface mesh, skeletonization, mesh cropping, boundary layer mesh construction and various mesh cosmetic methods that are essential for generating a high-quality domain discretization. After presenting the mesh generation procedure, how to generate flow boundary conditions for both the inlets and outlets of a geometry is explained in detail. This is followed by a brief note on the flow solver, before studying the blood flow through the carotid artery with a severe stenosis.

[BibTex]

[BibTex]


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Geometrically Induced Force Interaction for Three-Dimensional Deformable Models

Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu

IEEE Transactions on Image Processing, 20(5):1373 - 1387, 2011 (article)

Abstract
In this paper, we propose a novel 3-D deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images, and show that the proposed method achieves significant improvements against existing image gradient techniques.

[BibTex]

[BibTex]


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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]

[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]

2005


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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]

1997


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Recognizing facial expressions in image sequences using local parameterized models of image motion

Black, M. J., Yacoob, Y.

Int. Journal of Computer Vision, 25(1):23-48, 1997 (article)

Abstract
This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust, and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performed with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.

pdf pdf from publisher abstract video [BibTex]