Reduced foveal vision enhances peripheral monitoring and peripheral event detection

Introduction: Although it seems plausible that sports performance relies on high-acuity foveal vision, it could be empirically shown that myoptic blur (up to +2 diopters) does not harm performance in sport tasks that require foveal information pick-up like golf putting (Bulson, Ciuffreda, & Hung, 2008). How myoptic blur affects peripheral performance is yet unknown. Attention might be less needed for processing visual cues foveally and lead to better performance because peripheral cues are better processed as a function of reduced foveal vision, which will be tested in the current experiment. Methods: 18 sport science students with self-reported myopia volunteered as participants, all of them regularly wearing contact lenses. Exclusion criteria comprised visual correction other than myopic, correction of astigmatism and use of contact lenses out of Swiss delivery area. For each of the participants, three pairs of additional contact lenses (besides their regular lenses; used in the “plano” condition) were manufactured with an individual overcorrection to a retinal defocus of +1 to +3 diopters (referred to as “+1.00 D”, “+2.00 D”, and “+3.00 D” condition, respectively). Gaze data were acquired while participants had to perform a multiple object tracking (MOT) task that required to track 4 out of 10 moving stimuli. In addition, in 66.7 % of all trials, one of the 4 targets suddenly stopped during the motion phase for a period of 0.5 s. Stimuli moved in front of a picture of a sports hall to allow for foveal processing. Due to the directional hypotheses, the level of significance for one-tailed tests on differences was set at α = .05 and posteriori effect sizes were computed as partial eta squares (ηρ2). Results: Due to problems with the gaze-data collection, 3 participants had to be excluded from further analyses. The expectation of a centroid strategy was confirmed because gaze was closer to the centroid than the target (all p < .01). In comparison to the plano baseline, participants more often recalled all 4 targets under defocus conditions, F(1,14) = 26.13, p < .01, ηρ2 = .65. The three defocus conditions differed significantly, F(2,28) = 2.56, p = .05, ηρ2 = .16, with a higher accuracy as a function of a defocus increase and significant contrasts between conditions +1.00 D and +2.00 D (p = .03) and +1.00 D and +3.00 D (p = .03). For stop trials, significant differences could neither be found between plano baseline and defocus conditions, F(1,14) = .19, p = .67, ηρ2 = .01, nor between the three defocus conditions, F(2,28) = 1.09, p = .18, ηρ2 = .07. Participants reacted faster in “4 correct+button” trials under defocus than under plano-baseline conditions, F(1,14) = 10.77, p < .01, ηρ2 = .44. The defocus conditions differed significantly, F(2,28) = 6.16, p < .01, ηρ2 = .31, with shorter response times as a function of a defocus increase and significant contrasts between +1.00 D and +2.00 D (p = .01) and +1.00 D and +3.00 D (p < .01). Discussion: The results show that gaze behaviour in MOT is not affected to a relevant degree by a visual overcorrection up to +3 diopters. Hence, it can be taken for granted that peripheral event detection was investigated in the present study. This overcorrection, however, does not harm the capability to peripherally track objects. Moreover, if an event has to be detected peripherally, neither response accuracy nor response time is negatively affected. Findings could claim considerable relevance for all sport situations in which peripheral vision is required which now needs applied studies on this topic. References: Bulson, R. C., Ciuffreda, K. J., & Hung, G. K. (2008). The effect of retinal defocus on golf putting. Ophthalmic and Physiological Optics, 28, 334-344.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Gene expression profile in newborn rat lungs after two days of recovery of mechanical ventilation.

BACKGROUND Preterm infants having immature lungs often require respiratory support, potentially leading to bronchopulmonary dysplasia (BPD). Conventional BPD rodent models based on mechanical ventilation (MV) present outcome measured at the end of the ventilation period. A reversible intubation and ventilation model in newborn rats recently allowed discovering that different sets of genes modified their expression related to time after MV. In a newborn rat model, the expression profile 48 h after MV was analyzed with gene arrays to detect potentially interesting candidates with an impact on BPD development. METHODS Rat pups were injected P4-5 with 2 mg/kg lipopolysaccharide (LPS). One day later, MV with 21 or 60% oxygen was applied during 6 h. Animals were sacrified 48 h after end of ventilation. Affymetrix gene arrays assessed the total gene expression profile in lung tissue. RESULTS In fully treated animals (LPS + MV + 60% O(2)) vs. controls, 271 genes changed expression significantly. All modified genes could be classified in six pathways: tissue remodeling/wound repair, immune system and inflammatory response, hematopoiesis, vasodilatation, and oxidative stress. Major alterations were found in the MMP and complement system. CONCLUSION MMPs and complement factors play a central role in several of the pathways identified and may represent interesting targets for BPD treatment/prevention.Bronchopulmonary dysplasia (BPD) is a chronic lung disease occurring in ~30% of preterm infants born less than 30 wk of gestation (1). Its main risk factors include lung immaturity due to preterm delivery, mechanical ventilation (MV), oxygen toxicity, chorioamnionitis, and sepsis. The main feature is an arrest of alveolar and capillary formation (2). Models trying to decipher genes involved in the pathophysiology of BPD are mainly based on MV and oxygen application to young mammals with immature lungs of different species (3). In newborn rodent models, analyses of lung structure and gene and protein expression are performed for practical reasons directly at the end of MV (4,5,6). However, later appearing changes of gene expression might also have an impact on lung development and the evolution towards BPD and cannot be discovered by such models. Recently, we developed a newborn rat model of MV using an atraumatic (orotracheal) intubation technique that allows the weaning of the newborn animal off anesthesia and MV, the extubation to spontaneous breathing, and therefore allows the evaluation of effects of MV after a ventilation-free period of recovery (7). Indeed, applying this concept of atraumatic intubation by direct laryngoscopy, we recently were able to show significant differences between gene expression changes appearing directly after MV compared to those measured after a ventilation-free interval of 48 h. Immediately after MV, inflammation-related genes showed a transitory modified expression, while another set of more structurally related genes changed their expression only after a delay of 2 d (7). Lung structure, analyzed by conventional 2D histology and also by 3D reconstruction using synchrotron x-ray tomographic microscopy revealed, 48 h after end of MV, a reduced complexity of lung architecture compared to the nonventilated rat lungs, similar to the typical findings in BPD. To extend these observations about late gene expression modifications, we performed with a similar model a full gene expression profile of lung tissue 48 h after the end of MV with either room air or 60% oxygen. Essentially, we measured changes in the expression of genes related to the MMPs and complement system which played a role in many of the six identified mostly affected pathways.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

Effects of early and late lesion of orbital frontal cortex on visual processing of faces in rhesus macaques (Macaca mulatta)

Many mental disorders disrupt social skills, yet few studies have examined how the brain processes social information. Functional neuroimaging, neuroconnectivity and electrophysiological studies suggest that orbital frontal cortex plays important roles in social cognition, including the analysis of information from faces, which are important cues in social interactions. Studies in humans and non-human primates show that damage to orbital frontal cortex produces social behavior impairments, including abnormal aggression, but these studies have failed to determine whether damage to this area impairs face processing. In addition, it is not known whether damage early in life is more detrimental than damage in adulthood. This study examined whether orbital frontal cortex is necessary for the discrimination of face identity and facial expressions, and for appropriate behavioral responses to aggressive (threatening) facial expressions. Rhesus monkeys (Macaca mulatta) received selective lesions of orbital frontal cortex as newborns or adults. As adults, these animals were compared with sham-operated controls on their ability to discriminate between faces of individual monkeys and between different facial expressions of emotion. A passive visual paired-comparison task with standardized rhesus monkey face stimuli was designed and used to assess discrimination. In addition, looking behavior toward aggressive expressions was assessed and compared with that of normal control animals. The results showed that lesion of orbital frontal cortex (1) may impair discrimination between faces of individual monkeys, (2) does not impair facial expression discrimination, and (3) changes the amount of time spent looking at aggressive (threatening) facial expressions depending on the context. The effects of early and late lesions did not differ. Thus, orbital frontal cortex appears to be part of the neural circuitry for recognizing individuals and for modulating the response to aggression in faces, and the plasticity of the immature brain does not allow for recovery of these functions when the damage occurs early in life. This study opens new avenues for the assessment of rhesus monkey face processing and the neural basis of social cognition, and allows a better understanding of the nature of the neuropathology in patients with mental disorders that disrupt social behavior, such as autism. ^

The cost of publicly financed dental care in relation to community water fluoridation in Texas

Dental caries lead to children being less ready to learn and results in diminished productivity in the classroom. Tooth decay causes pain and infection, leading to impaired chewing, speech, and facial expression, in addition to a loss in self-esteem. There have been many studies supporting the safety and efficacy of community water fluoridation in reducing dental caries. Water fluoridation has been identified by the Centers for Disease Control and Prevention as one of 10 great public health achievements of the 20th century. The decline in the prevalence and severity of tooth decay in the United States during the past 60 years has been attributed largely to the increased use of fluoride; in particular, the widespread utilization of community water fluoridation. However, in the decades since fluoridation was first introduced, reductions in dental caries have declined, most likely due to the presence of other sources of fluoride. Questions have been raised regarding the need to continue to fluoridate community water supplies in the face of possible excessive exposure to fluoride. Nevertheless, dental caries continue to be a significant public health burden throughout the world, including the United States, especially among low-income and disadvantaged populations. Although many poor children receive their dental care through Medicaid, the percentage of Texas children with untreated dental caries continues to exceed the U.S. average and is well above Healthy People 2010 goals, even as state Medicaid expenditures continue to rise. The objective of this study is to determine the relationship between Medicaid dental expenditures and community water fluoridation levels in Texas counties. By examining this relationship, the cost-effectiveness of community water fluoridation in the Texas pediatric Medicaid beneficiary population, as measured by publicly financed dental care expenditures, may be ascertained.^

Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.

Nutrient concentrations along a North-South transect in the North Atlantic measured during Maria S. Merain cruise MSM03/1 (VISION) cruise

Samples were taken along a transect in the North Atlantic Ocean from 66°139.27'N; 29°136.65'W to 34°124.87'N; 28°128.90'W during the VISION cruise (diVersIty, Structure and functION) MSM03/01 on board the research vessel Maria S. Merian from September 21 to September 30, 2006. Along this transect, each station was sampled at 12 depths, from 10m down to 250m or 500m. Samples were collected with a rosette of 20-l Niskin bottles mounted on a conductivity-temperature-density profiler. Water samples for nutrients analysis were filtered directly after sampling through 0.45-µm in-line filters attached to a 60-ml pre-cleaned syringe into two 12-ml polystyrole tubes. Samples were stored at 4°C (dissolved silicate) or 80°C (ammonium, phosphate, nitrate and nitrite) The samples were spectrophotometrically measured with a continuous-flow analyzer using standard AA3 methods (Seal Analytical, Norderstedt, Germany) using a variant of the method of Grasshoff et al. (1983).

A Logarithmic Image Prior for Blind Deconvolution

Blind Deconvolution consists in the estimation of a sharp image and a blur kernel from an observed blurry image. Because the blur model admits several solutions it is necessary to devise an image prior that favors the true blur kernel and sharp image. Many successful image priors enforce the sparsity of the sharp image gradients. Ideally the L0 “norm” is the best choice for promoting sparsity, but because it is computationally intractable, some methods have used a logarithmic approximation. In this work we also study a logarithmic image prior. We show empirically how well the prior suits the blind deconvolution problem. Our analysis confirms experimentally the hypothesis that a prior should not necessarily model natural image statistics to correctly estimate the blur kernel. Furthermore, we show that a simple Maximum a Posteriori formulation is enough to achieve state of the art results. To minimize such formulation we devise two iterative minimization algorithms that cope with the non-convexity of the logarithmic prior: one obtained via the primal-dual approach and one via majorization-minimization.

Building the View Graph of a Category by Exploiting Image Realism

We propose a weakly supervised method to arrange images of a given category based on the relative pose between the camera and the object in the scene. Relative poses are points on a sphere centered at the object in a given canonical pose, which we call object viewpoints. Our method builds a graph on this sphere by assigning images with similar viewpoint to the same node and by connecting nodes if they are related by a small rotation. The key idea is to exploit a large unlabeled dataset to validate the likelihood of dominant 3D planes of the object geometry. A number of 3D plane hypotheses are evaluated by applying small 3D rotations to each hypothesis and by measuring how well the deformed images match other images in the dataset. Correct hypotheses will result in deformed images that correspond to plausible views of the object, and thus will likely match well other images in the same category. The identified 3D planes are then used to compute affinities between images related by a change of viewpoint. We then use the affinities to build a view graph via a greedy method and the maximum spanning tree.

Bilayer Blind Deconvolution with the Light Field Camera

In this paper we propose a solution to blind deconvolution of a scene with two layers (foreground/background). We show that the reconstruction of the support of these two layers from a single image of a conventional camera is not possible. As a solution we propose to use a light field camera. We demonstrate that a single light field image captured with a Lytro camera can be successfully deblurred. More specifically, we consider the case of space-varying motion blur, where the blur magnitude depends on the depth changes in the scene. Our method employs a layered model that handles occlusions and partial transparencies due to both motion blur and out of focus blur of the plenoptic camera. We reconstruct each layer support, the corresponding sharp textures, and motion blurs via an optimization scheme. The performance of our algorithm is demonstrated on synthetic as well as real light field images.

Segmentación de Secuencias de Vídeo: análisis y desarrollo de estrategias para la detección de cambios de toma y para la detección de objetos móviles

The important technological advances experienced along the last years have resulted in an important demand for new and efficient computer vision applications. On the one hand, the increasing use of video editing software has given rise to a necessity for faster and more efficient editing tools that, in a first step, perform a temporal segmentation in shots. On the other hand, the number of electronic devices with integrated cameras has grown enormously. These devices require new, fast, and efficient computer vision applications that include moving object detection strategies. In this dissertation, we propose a temporal segmentation strategy and several moving object detection strategies, which are suitable for the last generation of computer vision applications requiring both low computational cost and high quality results. First, a novel real-time high-quality shot detection strategy is proposed. While abrupt transitions are detected through a very fast pixel-based analysis, gradual transitions are obtained from an efficient edge-based analysis. Both analyses are reinforced with a motion analysis that allows to detect and discard false detections. This analysis is carried out exclusively over a reduced amount of candidate transitions, thus maintaining the computational requirements. On the other hand, a moving object detection strategy, which is based on the popular Mixture of Gaussians method, is proposed. This strategy, taking into account the recent history of each image pixel, adapts dynamically the amount of Gaussians that are required to model its variations. As a result, we improve significantly the computational efficiency with respect to other similar methods and, additionally, we reduce the influence of the used parameters in the results. Alternatively, in order to improve the quality of the results in complex scenarios containing dynamic backgrounds, we propose different non-parametric based moving object detection strategies that model both background and foreground. To obtain high quality results regardless of the characteristics of the analyzed sequence we dynamically estimate the most adequate bandwidth matrices for the kernels that are used in the background and foreground modeling. Moreover, the application of a particle filter allows to update the spatial information and provides a priori knowledge about the areas to analyze in the following images, enabling an important reduction in the computational requirements and improving the segmentation results. Additionally, we propose the use of an innovative combination of chromaticity and gradients that allows to reduce the influence of shadows and reflects in the detections.

Image Segmentation Using Ant System-based Clustering Algorithm

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.

Catadioptric panoramic stereovision for humanoid robots

This paper proposes a novel design of a reconfigurable humanoid robot head, based on biological likeness of human being so that the humanoid robot could agreeably interact with people in various everyday tasks. The proposed humanoid head has a modular and adaptive structural design and is equipped with three main components: frame, neck motion system and omnidirectional stereovision system modules. The omnidirectional stereovision system module being the last module, a motivating contribution with regard to other computer vision systems implemented in former humanoids, it opens new research possibilities for achieving human-like behaviour. A proposal for a real-time catadioptric stereovision system is presented, including stereo geometry for rectifying the system configuration and depth estimation. The methodology for an initial approach for visual servoing tasks is divided into two phases, first related to the robust detection of moving objects, their depth estimation and position calculation, and second the development of attention-based control strategies. Perception capabilities provided allow the extraction of 3D information from a wide range of visions from uncontrolled dynamic environments, and work results are illustrated through a number of experiments.

Real-time image processing for crop/weed discrimination in maize fields

This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem's mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is moving