Time perception in film viewing: A modulation of scene's duration estimates as a function of film editing.

Filmmakers and editors have empirically developed techniques to ensure the spatiotemporal continuity of a film's narration. In terms of time, editing techniques (e.g.

Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds.

Agricultural robotics is an up and coming field which deals with the development of robotic systems able to tackle a multitude of agricultural tasks efficiently. The case of interest, in this work, is mushroom collection in industrial mushroom farms. Developing such a robot, able to select and out-root a mushroom, requires delicate actions that can only be conducted if a well-performing perception module exists.

Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders.

Introduction: Monitoring biometric data using smartwatches (digital phenotypes) provides a novel approach for quantifying behavior in patients with psychiatric disorders. We tested whether such digital phenotypes predict changes in psychopathology of patients with psychotic disorders.

Methods: We continuously monitored digital phenotypes from 35 patients (20 with schizophrenia and 15 with bipolar spectrum disorders) using a commercial smartwatch for a period of up to 14 months.

E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures.

Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice.

The i-Walk Lightweight Assistive Rollator: First Evaluation Study.

Robots can play a significant role as assistive devices for people with movement impairment and mild cognitive deficit. In this paper we present an overview of the lightweight i-Walk intelligent robotic rollator, which offers cognitive and mobility assistance to the elderly and to people with light to moderate mobility impairment. The utility, usability, safety and technical performance of the device is investigated through a clinical study, which took place at a rehabilitation center in Greece involving real patients with mild to moderate cognitive and mobility impairment.

Evaluating the task effectiveness and user satisfaction with different operation modes of an assistive bathing robot in older adults.

Bathing robots have the potential to foster the independence of older adults who require assistance with bathing. Making human-robot interaction (HRI) for older persons as easy, effective, and user-satisfying as possible is, however, a major challenge in the development of such robots. The study aimed to evaluate the effectiveness (coverage, step effectiveness) and user satisfaction (After-Scenario Questionnaire, ASQ) with three operation modes (autonomous operation, shared control, tele-manipulation) for the HRI with a bathing robot in potential users.

Improving gesture-based interaction between an assistive bathing robot and older adults via user training on the gestural commands.

Background: Gesture-based human-robot interaction (HRI) depends on the technical performance of the robot-integrated gesture recognition system (GRS) and on the gestural performance of the robot user, which has been shown to be rather low in older adults. Training of gestural commands (GCs) might improve the quality of older users' input for gesture-based HRI, which in turn may lead to an overall improved HRI.

Objective: To evaluate the effects of a user training on gesture-based HRI between an assistive bathing robot and potential elderly robot users.

Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation.

We propose graph-driven approaches to image segmentation by developing diffusion processes defined on arbitrary graphs. We formulate a solution to the image segmentation problem modeled as the result of infectious wavefronts propagating on an image-driven graph where pixels correspond to nodes of an arbitrary graph. By relating the popular Susceptible - Infected - Recovered epidemic propagation model to the Random Walker algorithm, we develop the Normalized Random Walker and a lazy random walker variant.

Assessing the effect of physical differences in the articulation of consonants and vowels on audiovisual temporal perception.

We investigated how the physical differences associated with the articulation of speech affect the temporal aspects of audiovisual speech perception. Video clips of consonants and vowels uttered by three different speakers were presented. The video clips were analyzed using an auditory-visual signal saliency model in order to compare signal saliency and behavioral data.

Synergy between object recognition and image segmentation using the expectation-maximization algorithm.

In this work, we formulate the interaction between image segmentation and object recognition in the framework of the Expectation-Maximization (EM) algorithm. We consider segmentation as the assignment of image observations to object hypotheses and phrase it as the E-step, while the M-step amounts to fitting the object models to the observations. These two tasks are performed iteratively, thereby simultaneously segmenting an image and reconstructing it in terms of objects.

Bayesian inference on multiscale models for poisson intensity estimation: applications to photon-limited image denoising.

We present an improved statistical model for analyzing Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main contributions include: 1) a rigorous and robust regularized expectation-maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the noisy observed Poisson data (counts); 2) extension of the method to work under a multiscale hidden Markov tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling interscale coefficient dependencies in the vicinity of image edges; 3) exploration of a 2-D recursive quad-tree image representation, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary-tree image representation involving beta-mixture rate-ratio densities; and 4) a novel multiscale image representation, which we term Poisson-Haar decomposition, that better models the image edge structure, thus yielding improved performance.

Texture analysis and segmentation using modulation features, generative models, and weighted curve evolution.

In this work we approach the analysis and segmentation of natural textured images by combining ideas from image analysis and probabilistic modeling. We rely on AM-FM texture models and specifically on the Dominant Component Analysis (DCA) paradigm for feature extraction. This method provides a low-dimensional, dense and smooth descriptor, capturing essential aspects of texture, namely scale, orientation, and contrast.

Generalized flooding and Multicue PDE-based image segmentation.

Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. Moreover, the increasing demands of image analysis tasks in terms of segmentation results' quality introduce the necessity of employing multiple cues for improving image segmentation results. In this paper, we attempt to incorporate cues such as intensity contrast, region size, and texture in the segmentation procedure and derive improved results compared to using individual cues separately.

Multigrid geometric active contour models.

Geometric active contour models are very popular partial differential equation-based tools in image analysis and computer vision. We present a new multigrid algorithm for the fast evolution of level-set-based geometric active contours and compare it with other established numerical schemes. We overcome the main bottleneck associated with most numerical implementations of geometric active contours, namely the need for very small time steps to avoid instability, by employing a very stable fully 2-D implicit-explicit time integration numerical scheme.