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"abstract": "Abstract—Since the first shape-from-shading (SFS) technique was developed by Horn in the early 1970s, many different approaches have emerged. In this paper, six well-known SFS algorithms are implemented and compared. The performance of the algorithms was analyzed on synthetic images using mean and standard deviation of depth (Z_$Z$_Z) error, mean of surface gradient (Z_$p$_Z, Z_$q$_Z) error, and CPU timing. Each algorithm works well for certain images, but performs poorly for others. In general, minimization approaches are more robust, while the other approaches are faster. The implementation of these algorithms in C and images used in this paper are available by anonymous ftp under the pub/tech_paper/survey directory at eustis.cs.ucf.edu (132.170.108.42). These are also part of the electronic version of paper.",
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"content": "Abstract—Since the first shape-from-shading (SFS) technique was developed by Horn in the early 1970s, many different approaches have emerged. In this paper, six well-known SFS algorithms are implemented and compared. The performance of the algorithms was analyzed on synthetic images using mean and standard deviation of depth ($Z$) error, mean of surface gradient ($p$, $q$) error, and CPU timing. Each algorithm works well for certain images, but performs poorly for others. In general, minimization approaches are more robust, while the other approaches are faster. The implementation of these algorithms in C and images used in this paper are available by anonymous ftp under the pub/tech_paper/survey directory at eustis.cs.ucf.edu (132.170.108.42). These are also part of the electronic version of paper.",
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"content": "A robust approach to the recovery of shape from shading information is presented. Assuming uniform albedo and Lambertian surface for the imaging model, two methods for estimating the azimuth of the illuminant are presented. One is based on local estimates on smooth patches, and the other method uses shading information along image contours. The elevation of the illuminant and surface albedo are estimated from image statistics, taking into consideration the effect of self-shadowing. With the estimated reflectance map parameters, the authors then compute the surface shape using a procedure that implements the smoothness constraint by requiring the gradients of reconstructed density to be close to the gradients of the input image. The algorithm is data driven, stable, updates the surface slope and height maps simultaneously, and significantly reduces the residual errors in irradiance and integrability terms. A hierarchical implementation of the algorithm is presented. Typical results on synthetic and images are given to illustrate the usefulness of the approach.",
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"content": "We comment on a paper that describes a closed-form formulation to Tensor Voting, a technique to perceptually group clouds of points, usually applied to infer features in images. The authors proved an analytic solution to the technique, a highly relevant contribution considering that the original formulation required numerical integration, a time-consuming task. Their work constitutes the first closed-form expression for the Tensor Voting framework. In this work we first observe that the proposed formulation leads to unexpected results which do not satisfy the constraints for a Tensor Voting output, hence they cannot be interpreted. Given that the closed-form expression is said to be an analytic equivalent solution, unexpected outputs should not be encountered unless there are flaws in the proof. We analyzed the underlying math to find which were the causes of these unexpected results. In this commentary we show that their proposal does not in fact provide a proper analytic solution to Tensor Voting and we indicate the flaws in the proof.",
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"title": "Comments on “A Closed-Form Solution to Tensor Voting: Theory and Applications”",
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"content": "Fitting regression models with many multivariate responses and covariates can be challenging, but such responses and covariates sometimes have tensor-variate structure. We extend the classical multivariate regression model to exploit such structure in two ways: first, we impose four types of low-rank tensor formats on the regression coefficients. Second, we model the errors using the tensor-variate normal distribution that imposes a Kronecker separable format on the covariance matrix. We obtain maximum likelihood estimators via block-relaxation algorithms and derive their computational complexity and asymptotic distributions. Our regression framework enables us to formulate tensor-variate analysis of variance (TANOVA) methodology. This methodology, when applied in a one-way TANOVA layout, enables us to identify cerebral regions significantly associated with the interaction of suicide attempters or non-attemptor ideators and positive-, negative- or death-connoting words in a functional Magnetic Resonance Imaging study. Another application uses three-way TANOVA on the Labeled Faces in the Wild image dataset to distinguish facial characteristics related to ethnic origin, age group and gender. A R package <monospace>totr</monospace> implements the methodology.",
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"abstract": "This article aims to propose a novel Analytical Tensor Voting (ATV) mechanism, which enables robust perceptual grouping and salient information extraction for noisy <inline-formula><tex-math notation=\"LaTeX\">Z_$N-$_Z</tex-math></inline-formula>dimensional (ND) data. Firstly, the approximation of the decaying function is investigated and adopted based on the idea of penalizing the <inline-formula><tex-math notation=\"LaTeX\">Z_$1-$_Z</tex-math></inline-formula>tensor votes by distance and curvature, respectively, followed by the derivation of analytical solution to the <inline-formula><tex-math notation=\"LaTeX\">Z_$1-$_Z</tex-math></inline-formula>tensor voting in ND space from the geometric view. Secondly, a novel spherical representation mechanism is proposed to facilitate the representation of the elementary tensors in various dimensional spaces, where the high dimensional spherical coordinate system is utilized to construct the controllable unit vectors and corresponding <inline-formula><tex-math notation=\"LaTeX\">Z_$1-$_Z</tex-math></inline-formula>tensors. Accordingly, any elementary <inline-formula><tex-math notation=\"LaTeX\">Z_$K-$_Z</tex-math></inline-formula>tensor is represented by the surface integration of the constructed <inline-formula><tex-math notation=\"LaTeX\">Z_$1-$_Z</tex-math></inline-formula>tensors over the unit <inline-formula><tex-math notation=\"LaTeX\">Z_$K-$_Z</tex-math></inline-formula>sphere. Thirdly, the ATV mechanism is constructed using the adopted decaying function and proposed spherical representation mechanism, where the analytical solution to tensor voting in ND space is derived, which enables the robust and accurate salient information extraction from noisy ND data. Finally, several interesting properties of the proposed ATV mechanism are investigated. Experimental results on synthetic and real data validate the effectiveness, efficiency and robustness of the proposed method in perceptual grouping tasks in 3D,10D or higher dimensional spaces.",
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"abstract": "Biofeedback games are an attractive alternative to standard techniques for learning short-term relaxation skills. In this paper, we present the design, implementation, and evaluation of three respiratory biofeedback games. To validate these games, we compared breathing rate across 100 male participants (23 years ± 3.2 years) playing biofeedback and audio pacing versions of these games as well as a paced breathing app. The games were placed between repeat runs of a cognitively stressful Stroop-based task and the impact of the games on breathing and cognitive performance in the task also assessed. Our results showed that 1) differences in gameplay did not impact player performance; 2) biofeedback not only led to better breath control during play but also during the subsequent cognitively stressful task; and 3) biofeedback led to better attentional-cognitive performance in the subsequent task. Our multi-game experiments show that using respiratory biofeedback in video games is an effective strategy to learn paced breathing—on par with the standalone technique of paced breathing—and to self-regulate stress levels in later stressful scenarios. Furthermore, owing to its entertainment value, our relaxation solution has the potential to be more engaging and accessible than standalone paced breathing, for use over longer durations.",
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"content": "Biofeedback games are an attractive alternative to standard techniques for learning short-term relaxation skills. In this paper, we present the design, implementation, and evaluation of three respiratory biofeedback games. To validate these games, we compared breathing rate across 100 male participants (23 years ± 3.2 years) playing biofeedback and audio pacing versions of these games as well as a paced breathing app. The games were placed between repeat runs of a cognitively stressful Stroop-based task and the impact of the games on breathing and cognitive performance in the task also assessed. Our results showed that 1) differences in gameplay did not impact player performance; 2) biofeedback not only led to better breath control during play but also during the subsequent cognitively stressful task; and 3) biofeedback led to better attentional-cognitive performance in the subsequent task. Our multi-game experiments show that using respiratory biofeedback in video games is an effective strategy to learn paced breathing—on par with the standalone technique of paced breathing—and to self-regulate stress levels in later stressful scenarios. Furthermore, owing to its entertainment value, our relaxation solution has the potential to be more engaging and accessible than standalone paced breathing, for use over longer durations.",
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"content": "We envision a future time when wearable cameras are worn by the masses and recording first-person point-of-view videos of everyday life. While these cameras can enable new assistive technologies and novel research challenges, they also raise serious privacy concerns. For example, first-person videos passively recorded by wearable cameras will necessarily include anyone who comes into the view of a camera-with or without consent. Motivated by these benefits and risks, we developed a self-search technique tailored to first-person videos. The key observation of our work is that the egocentric head motion of a target person (i.e., the self) is observed both in the point-of-view video of the target and observer. The motion correlation between the target person's video and the observer's video can then be used to identify instances of the self uniquely. We incorporate this feature into the proposed approach that computes the motion correlation over densely-sampled trajectories to search for a target individual in observer videos. Our approach significantly improves self-search performance over several well-known face detectors and recognizers. Furthermore, we show how our approach can enable several practical applications such as privacy filtering, target video retrieval, and social group clustering.",
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