Prototype-based budget maintenance for tracking in depth videos

© 2016, Springer Science+Business Media New York. The use of conventional video tracking based on color or gray-level videos often raises concerns about the privacy of the tracked targets. To alleviate this issue, this paper presents a novel tracker that operates solely from depth data. The proposed tracker is designed as an extension of the popular Struck algorithm which leverages the effective framework of structural SVM. The main contributions of our paper are: i) a dedicated depth feature based on local depth patterns, ii) a heuristic for handling view occlusions in depth frames, and iii) a technique for keeping the number of the support vectors within a given “budget” so as to limit computational costs. Experimental results over the challenging Princeton Tracking Benchmark (PTB) dataset report a remarkable accuracy compared to the original Struck tracker and other state-of-the-art trackers using depth and RGB data

Local depth patterns for fine-grained activity recognition in depth videos

© 2016 IEEE. Fine-grained activities are human activities involving small objects and small movements. Automatic recognition of such activities can prove useful for many applications, including detailed diarization of meetings and training sessions, assistive human-computer interaction and robotics interfaces. Existing approaches to fine-grained activity recognition typically leverage the combined use of multiple sensors including cameras, RFID tags, gyroscopes and accelerometers borne by the monitored people and target objects. Although effective, the downside of these solutions is that they require minute instrumentation of the environment that is intrusive and hard to scale. To this end, this paper investigates fine-grained activity recognition in a kitchen setting by solely using a depth camera. The primary contribution of this work is an aggregated depth descriptor that effectively captures the shape of the objects and the actors. Experimental results over the challenging '50 Salads' dataset of kitchen activities show an accuracy comparable to that of a state-of-the-art approach based on multiple sensors, thereby validating a less intrusive and more practical way of monitoring fine-grained activities

On the Invertibility of Storage Systems

The invertibility of single-input single output storage systems (network of reservoirs) is considered in this paper. The analysis shows that cascade and feedback connections of invertible subsystems give rise to invertible systems, and that parallel connections are invertible provided that the network is not too diversified topologically and that the reservoirs have comparable dynamics. These results often allow one to ascertain the invertibility of a complex storage system by direct inspection of a graph

Dyslexia and Comorbid Dyscalculia: rate of comorbidity and underlying cognitive and learning profile

PURPOSE OF THE STUDY. Children diagnosed with a specific learning disorder (SLD) have four to five times higher chances of developing a comorbid condition. In particular, the high prevalence of comorbid dyscalculia (MD) in children with dyslexia (RD) has been documented. Nevertheless, the exact rate of MD comorbidity and the causes underlying the overlap remain unclear since most research has focused on studying them in isolation. Given the relevance of early identification and evidence-based interventions for further compensation of SLD, there is a need for studies on this matter. The study intended to fill this gap. METHOD. The study was a secondary data analysis of the standardised test scores of 215 neuropsychological assessments administered to grade 1 to 3 schoolchildren in Argentina who had a prior diagnosis of RD. For the purposes of the study, they were classified into 2 groups (RD only and comorbid RDMD). Scores were analyzed using SPSS Statistics to (i) explore the rate of MD comorbidity in children with RD; (ii) contrast the cognitive and learning profiles of the RD and the RDMD group; and (iii) assess the predictive value of each cognitive factor to the development of the RDMD comorbidity. RESULTS AND CONCLUSION. The study found that children with RD developed RDMD at a frequency of 33.5%. There was a significant difference in the two groups' learning and cognitive factors scores, with the comorbid group worst affected in all domains. Among these, verbal working memory, spatial skills, semantic long-term memory and phonological awareness were the most sensitive predictors; together they could account for 35% of the MD comorbidity. These findings are evidence of the high incidence of MD comorbidity in the population with RD and highlight the predictive value of specific cognitive markers

The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene

© 2018 The Author(s). Objectives: (i) To demonstrate the feasibility of automated, direct observation and collection of hand hygiene data, (ii) to develop computer visual methods capable of reporting compliance with moment 1 (the performance of hand hygiene before touching a patient) and (iii) to report the diagnostic accuracy of automated, direct observation of moment 1. Design: Observation of simulated hand hygiene encounters between a healthcare worker and a patient. Setting: Computer laboratory in a university. Participants: Healthy volunteers. Main outcome measures: Sensitivity and specificity of automatic detection of the first moment of hand hygiene. Methods: We captured video and depth images using a Kinect camera and developed computer visual methods to automatically detect the use of alcohol-based hand rub (ABHR), rubbing together of hands and subsequent contact of the patient by the healthcare worker using depth imagery. Results: We acquired images from 18 different simulated hand hygiene encounters where the healthcare worker complied with the first moment of hand hygiene, and 8 encounters where they did not. The diagnostic accuracy of determining that ABHR was dispensed and that the patient was touched was excellent (sensitivity 100%, specificity 100%). The diagnostic accuracy of determining that the hands were rubbed together after dispensing ABHR was good (sensitivity 83%, specificity 88%). Conclusions: We have demonstrated that it is possible to automate the direct observation of hand hygiene performance in a simulated clinical setting. We used cheap, widely available consumer technology and depth imagery which potentially increases clinical application and decreases privacy concerns

Where does brain neural activation in aesthetic responses to visual art occur? Meta-analytic evidence from neuroimaging studies

Here we aimed at finding the neural correlates of the general aspect of visual aesthetic experience (VAE) and those more strictly correlated with the content of the artworks. We applied a general activation likelihood estimation (ALE) meta-analysis to 47 fMRI experiments described in 14 published studies. We also performed four separate ALE analyses in order to identify the neural substrates of reactions to specific categories of artworks, namely portraits, representation of real-world-visual-scenes, abstract paintings, and body sculptures. The general ALE revealed that VAE relies on a bilateral network of areas, and the individual ALE analyses revealed different maximal activation for the artworks' categories as function of their content. Specifically, different content-dependent areas of the ventral visual stream are involved in VAE, but a few additional brain areas are involved as well. Thus, aesthetic-related neural responses to art recruit widely distributed networks in both hemispheres including content-dependent brain areas of the ventral visual stream. Together, the results suggest that aesthetic responses are not independent of sensory, perceptual, and cognitive processe

The neural correlates of orienting to walking direction in 6-month-old infants: an ERP study

The ability to detect social signals represents a first step to enter our social world. Behavioral evidence has demonstrated that 6‐month‐old infants are able to orient their attention toward the position indicated by walking direction, showing faster orienting responses toward stimuli cued by the direction of motion than toward uncued stimuli. The present study investigated the neural mechanisms underpinning this attentional priming effect by using a spatial cueing paradigm and recording EEG (Geodesic System 128 channels) from 6‐month‐old infants. Infants were presented with a central point‐light walker followed by a single peripheral target. The target appeared randomly at a position either congruent or incongruent with the walking direction of the cue. We examined infants' target‐locked event‐related potential (ERP) responses and we used cortical source analysis to explore which brain regions gave rise to the ERP responses. The P1 component and saccade latencies toward the peripheral target were modulated by the congruency between the walking direction of the cue and the position of the target. Infants' saccade latencies were faster in response to targets appearing at congruent spatial locations. The P1 component was larger in response to congruent than to incongruent targets and a similar congruency effect was found with cortical source analysis in the parahippocampal gyrus and the anterior fusiform gyrus. Overall, these findings suggest that a type of biological motion like the one of a vertebrate walking on the legs can trigger covert orienting of attention in 6‐month‐old infants, enabling enhancement of neural activity related to visual processing of potentially relevant information as well as a facilitation of oculomotor responses to stimuli appearing at the attended location

The Relationships between Cognitive Styles and Creativity: The Role of Field Dependence-Independence on Visual Creative Production

Previous studies explored the relationships between field dependent-independent cognitive style (FDI) and creativity, providing misleading and unclear results. The present research explored this problematic interplay through the lens of the Geneplore model, employing a product oriented task: the Visual Creative Synthesis Task (VCST). The latter requires creating objects belonging to pre-established categories, starting from triads of visual components and consists of two steps: the preinventive phase and the inventive phase. Following the Amabile’s consensual assessment technique, three independent judges evaluated preinventive structures in terms of originality and synthesis whereas inventions were evaluated in terms of originality and appropriateness. The Embedded Figure Test (EFT) was employed in order to measure the individual’s predisposition toward the field dependence or the field independence. Sixty undergraduate college students (31 females) took part in the experiment. Results revealed that field independent individuals outperformed field dependent ones in each of the four VCST scores, showing higher levels of creativity. Results were discussed in light of the better predisposition of field independent individuals in mental imagery, mental manipulation of abstract objects, as well as in using their knowledge during complex tasks that require creativity. Future research directions were also discussed

Well-M³N: A Maximum-Margin Approach to Unsupervised Structured Prediction

Unsupervised structured prediction is of fundamental importance for the clustering and classification of unannotated structured data. To date, its most common approach still relies on the use of structural probabilistic models and the expectation-maximization (EM) algorithm. Conversely, structural maximum-margin approaches, despite their extensive success in supervised and semi-supervised classification, have not raised equivalent attention in the unsupervised case. For this reason, in this paper we propose a novel approach that extends the maximum-margin Markov networks (M3N) to an unsupervised training framework. The main contributions of our extension are new formulations for the feature map and loss function of M3N that decouple the labels from the measurements and support multiple ground-truth training. Experiments on two challenging segmentation datasets have achieved competitive accuracy and generalization compared to other unsupervised algorithms such as k-means, EM and unsupervised structural SVM, and comparable performance to a contemporary deep learning-based approach