A Dynamic Programming Solution to Bounded Dejittering Problems

We propose a dynamic programming solution to image dejittering problems with bounded displacements and obtain efficient algorithms for the removal of line jitter, line pixel jitter, and pixel jitter.Comment: The final publication is available at link.springer.co

LAN-HDR: Luminance-based Alignment Network for High Dynamic Range Video Reconstruction

As demands for high-quality videos continue to rise, high-resolution and high-dynamic range (HDR) imaging techniques are drawing attention. To generate an HDR video from low dynamic range (LDR) images, one of the critical steps is the motion compensation between LDR frames, for which most existing works employed the optical flow algorithm. However, these methods suffer from flow estimation errors when saturation or complicated motions exist. In this paper, we propose an end-to-end HDR video composition framework, which aligns LDR frames in the feature space and then merges aligned features into an HDR frame, without relying on pixel-domain optical flow. Specifically, we propose a luminance-based alignment network for HDR (LAN-HDR) consisting of an alignment module and a hallucination module. The alignment module aligns a frame to the adjacent reference by evaluating luminance-based attention, excluding color information. The hallucination module generates sharp details, especially for washed-out areas due to saturation. The aligned and hallucinated features are then blended adaptively to complement each other. Finally, we merge the features to generate a final HDR frame. In training, we adopt a temporal loss, in addition to frame reconstruction losses, to enhance temporal consistency and thus reduce flickering. Extensive experiments demonstrate that our method performs better or comparable to state-of-the-art methods on several benchmarks.Comment: ICCV 202

Action Intentions, Predictive Processing, and Mind Reading: Turning Goalkeepers Into Penalty Killers

The key to action control is one’s ability to adequately predict the consequences of one’s actions. Predictive processing theories assume that forward models enable rapid “preplay” to assess the match between predicted and intended action effects. Here we propose the novel hypothesis that “reading” another’s action intentions requires a rich forward model of that agent’s action. Such a forward model can be obtained and enriched through learning by either practice or simulation. Based on this notion, we ran a series of studies on soccer goalkeepers and novices, who predicted the intended direction of penalties being kicked at them in a computerized penalty-reading task. In line with hypotheses, extensive practice in penalty kicking improved performance in penalty reading among goalkeepers who had extensive prior experience in penalty blocking but not in penalty kicking. A robust benefit in penalty reading did not result from practice in kinesthetic motor imagery of penalty kicking in novice participants. To test whether goalkeepers actually use such penalty-kicking imagery in penalty reading, we trained a machine-learning classifier on multivariate fMRI activity patterns to distinguish motor-imagery-related from attention-related strategies during a penalty-imagery training task. We then applied that classifier to fMRI data related to a separate penalty-reading task and showed that 2/3 of all correctly read penalty kicks were classified as engaging the motor-imagery circuit rather than merely the attention circuit. This study provides initial evidence that, in order to read our opponent’s action intention, it helps to observe their action kinematics, and use our own forward model to predict the sensory consequences of “our” penalty kick if we were to produce these action kinematics ourselves. In sum, it takes practice as a penalty kicker to become a penalty killer

A variational method for dejittering large fluorescence line scanner images

International audienceWe propose a variational method dedicated to jitter correction of large fluorescence scanner images. Our method consists in minimizing a global energy functional to estimate a dense displacement field representing the spatially-varying jitter. The computational approach is based on a half-quadratic splitting of the energy functional, which decouples the realignment data term and the dedicated differential-based regularizer. The resulting problem amounts to alternatively solving two convex and nonconvex optimization subproblems with appropriate algorithms. Experimental results on artificial and large real fluorescence images demonstrate that our method is not only capable to handle large displacements but is also efficient in terms of subpixel precision without inducing additional intensity artifacts

Resting state correlates of subdimensions of anxious affect

Resting state fMRI may help identify markers of risk for affective disorder. Given the comorbidity of anxiety and depressive disorders and the heterogeneity of these disorders as defined by DSM, an important challenge is to identify alterations in resting state brain connectivity uniquely associated with distinct profiles of negative affect. The current study aimed to address this by identifying differences in brain connectivity specifically linked to cognitive and physiological profiles of anxiety, controlling for depressed affect. We adopted a two-stage multivariate approach. Hierarchical clustering was used to independently identify dimensions of negative affective style and resting state brain networks. Combining the clustering results, we examined individual differences in resting state connectivity uniquely associated with subdimensions of anxious affect, controlling for depressed affect. Physiological and cognitive subdimensions of anxious affect were identified. Physiological anxiety was associated with widespread alterations in insula connectivity, including decreased connectivity between insula subregions and between the insula and other medial frontal and subcortical networks. This is consistent with the insula facilitating communication between medial frontal and subcortical regions to enable control of physiological affective states. Meanwhile, increased connectivity within a frontoparietal-posterior cingulate cortex-precunous network was specifically associated with cognitive anxiety, potentially reflecting increased spontaneous negative cognition (e.g., worry). These findings suggest that physiological and cognitive anxiety comprise subdimensions of anxiety-related affect and reveal associated alterations in brain connectivity

Use of synchronisation pattern to estimate geometric distortions

Publication in the conference proceedings of EUSIPCO, Toulouse, France, 200

Strategic incentives undermine gaze as a signal of prosocial motives

People often have to judge the social motives of others, for example, to distinguish truly prosocial people from those merely trying to appear prosocial. Gaze can reveal the motives underlying social decisions, as decision-makers dedicate more attention to motive-relevant information. We extend the use of eye-tracking and apply it as a communication device by providing (real-time) eye-tracking information of one participant to another. We find that untrained observers can judge the prosociality of decision-makers from their eye-tracked gaze alone, but only if there are no strategic incentives to be chosen for a future interaction. When there are such strategic incentives, the cues of prosociality are invalidated, as both individualistic and prosocial decision-makers put effort into appearing more prosocial. Overall, we find that gaze carries information about a person's prosociality, but also that gaze is malleable and affected by strategic considerations

Local Geometric Consensus: A General Purpose Point Pattern-Based Tracking Algorithm

Proceedings of ACM ISMAR 2015, Fukuoka, JapanInternational audienceWe present a method which can quickly and robustly match 2D and 3D point patterns based on their sole spatial distribution , but it can also handle other cues if available. This method can be easily adapted to many transformations such as similarity transformations in 2D/3D, and affine and perspective transformations in 2D. It is based on local geometric consensus among several local matchings and a refinement scheme. We provide two implementations of this general scheme, one for the 2D homography case (which can be used for marker or image tracking) and one for the 3D similarity case. We demonstrate the robustness and speed performance of our proposal on both synthetic and real images and show that our method can be used to augment any (textured/textureless) planar objects but also 3D objects

高速ビジョンを用いたリアルタイムビデオモザイキングと安定化に関する研究

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora