A neuromorphic dataset for tabletop object segmentation in indoor cluttered environment.

Event-based cameras are commonly leveraged to mitigate issues such as motion blur, low dynamic range, and limited time sampling, which plague conventional cameras. However, a lack of dedicated event-based datasets for benchmarking segmentation algorithms, especially those offering critical depth information for occluded scenes, has been observed. In response, this paper introduces a novel Event-based Segmentation Dataset (ESD), a high-quality event 3D spatial-temporal dataset designed for indoor object segmentation within cluttered environments. ESD encompasses 145 sequences featuring 14,166 manually annotated RGB frames, along with a substantial event count of 21.88 million and 20.80 million events from two stereo-configured event-based cameras. Notably, this densely annotated 3D spatial-temporal event-based segmentation benchmark for tabletop objects represents a pioneering initiative, providing event-wise depth, and annotated instance labels, in addition to corresponding RGBD frames. By releasing ESD, our aim is to offer the research community a challenging segmentation benchmark of exceptional quality.

Within-Modality Synthesis and Novel Radiomic Evaluation of Brain MRI Scans.

One of the most common challenges in brain MRI scans is to perform different MRI sequences depending on the type and properties of tissues. In this paper, we propose a generative method to translate T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) volume from T2-weight-Fluid-attenuated-Inversion-Recovery (FLAIR) and vice versa using Generative Adversarial Networks (GAN). To evaluate the proposed method, we propose a novel evaluation schema for generative and synthetic approaches based on radiomic features. For the evaluation purpose, we consider 510 pair-slices from 102 patients to train two different GAN-based architectures Cycle GAN and Dual Cycle-Consistent Adversarial network (DC2Anet). The results indicate that generative methods can produce similar results to the original sequence without significant change in the radiometric feature. Therefore, such a method can assist clinics to make decisions based on the generated image when different sequences are not available or there is not enough time to re-perform the MRI scans.

Dynamic-Vision-Based Force Measurements Using Convolutional Recurrent Neural Networks.

In this paper, a novel dynamic Vision-Based Measurement method is proposed to measure contact force independent of the object sizes. A neuromorphic camera (Dynamic Vision Sensor) is utilizused to observe intensity changes within the silicone membrane where the object is in contact. Three deep Long Short-Term Memory neural networks combined with convolutional layers are developed and implemented to estimate the contact force from intensity changes over time. Thirty-five experiments are conducted using three objects with different sizes to validate the proposed approach. We demonstrate that the networks with memory gates are robust against variable contact sizes as the networks learn object sizes in the early stage of a grasp. Moreover, spatial and temporal features enable the sensor to estimate the contact force every 10 ms accurately. The results are promising with Mean Squared Error of less than 0.1 N for grasping and holding contact force using leave-one-out cross-validation method.

Schwannoma of the Lateral Femoral Cutaneous Nerve, an Unusual Cause of Meralgia Paresthetica: A Case Report.

INTRODUCTION: Meralgia paresthetica (MP) is a clinical syndrome usually resulting usually from compression of the lateral femoral cutaneous nerve (LFCN). Tumors arising from this nerve could also be the cause of this syndrome. CASE REPORT: We present an unusual cause of MP in a 67-year-old Caucasian male. The cause of the syndrome appeared to be a schwannoma tumor of the LFCN. Such a cause of MP has not been reported previously in the literature. CONCLUSION: Medical practitioners should also consider other causes of MP syndrome, such as peripheral nerve tumors. Although diagnosis is considered to be clinical, ultrasound or magnetic resonance imaging (MRI) could be helpful to establish the diagnosis.

Evaluation of Indirect Methods for Motion Compensation in 2-D Focal Liver Lesion Contrast-Enhanced Ultrasound (CEUS) Imaging.

This study investigates the application and evaluation of existing indirect methods, namely point-based registration techniques, for the estimation and compensation of observed motion included in the 2-D image plane of contrast-enhanced ultrasound (CEUS) cine-loops recorded for the characterization and diagnosis of focal liver lesions (FLLs). The value of applying motion compensation in the challenging modality of CEUS is to assist in the quantification of the perfusion dynamics of an FLL in relation to its parenchyma, allowing for a potentially accurate diagnostic suggestion. Towards this end, this study also proposes a novel quantitative multi-level framework for evaluating the quantification of FLLs, which to the best of our knowledge remains undefined, notwithstanding many relevant studies. Following quantitative evaluation of 19 indirect algorithms and configurations, while also considering the requirement for computational efficiency, our results suggest that the "compact and real-time descriptor" (CARD) is the optimal indirect motion compensation method in CEUS.

Rationale and Design of an Online Educational Program Using Game-Based Learning to Improve Nutrition and Physical Activity Outcomes Among University Students in the United Kingdom.

OBJECTIVE: To assess the impact of an online game-based educational program on nutrition knowledge and dietary and physical activity habits among university students in the United Kingdom. DESIGN: Randomized controlled trial with pre- and postintervention comparisons. SETTING: Two higher education settings in London, UK. SUBJECTS: Current undergraduate and postgraduate students of two universities (n = 88) aged 18-34 years are randomly allocated to an intervention (n = 44) or a control group (n = 44). INTERVENTION: The intervention group will receive access to an educational website and online quizzes with gamification elements, including information about healthy eating and physical activity. The control group will receive no information. Duration of the intervention will be 10 weeks. MEASURES OF OUTCOME: Primary outcome is nutrition knowledge. Secondary outcomes include dietary and activity habits. Nutrition knowledge and dietary and activity habits will be assessed using questionnaires. Weekly steps will be counted using pedometers. Assessment of anthropometric and metabolic risk factors will take place. ANALYSIS: Quantitative analysis will investigate changes in nutrition knowledge between the two groups of the study population. Linear regression analysis will be used, if the data follow the normal distribution (otherwise binomial regression analysis), to examine whether field of study, residence status, body mass index (BMI), and demographic factors affect nutrition knowledge. Associations between changes in knowledge and dietary and physical activity behavior will be assessed by correlations. CONCLUSIONS/IMPLICATIONS: The study will provide insights with regard to the design and use of online game-playing as a cost-effective approach to improve nutritional knowledge among university students.

A Novel Event-Based Incipient Slip Detection Using Dynamic Active-Pixel Vision Sensor (DAVIS).

In this paper, a novel approach to detect incipient slip based on the contact area between a transparent silicone medium and different objects using a neuromorphic event-based vision sensor (DAVIS) is proposed. Event-based algorithms are developed to detect incipient slip, slip, stress distribution and object vibration. Thirty-seven experiments were performed on five objects with different sizes, shapes, materials and weights to compare precision and response time of the proposed approach. The proposed approach is validated by using a high speed constitutional camera (1000 FPS). The results indicate that the sensor can detect incipient slippage with an average of 44.1 ms latency in unstructured environment for various objects. It is worth mentioning that the experiments were conducted in an uncontrolled experimental environment, therefore adding high noise levels that affected results significantly. However, eleven of the experiments had a detection latency below 10 ms which shows the capability of this method. The results are very promising and show a high potential of the sensor being used for manipulation applications especially in dynamic environments.

Automatic Identification of the Optimal Reference Frame for Segmentation and Quantification of Focal Liver Lesions in Contrast-Enhanced Ultrasound.

Post-examination interpretation of contrast-enhanced ultrasound (CEUS) cineloops of focal liver lesions (FLLs) requires offline manual assessment by experienced radiologists, which is time-consuming and generates subjective results. Such assessment usually starts by manually identifying a reference frame, where FLL and healthy parenchyma are well-distinguished. This study proposes an automatic computational method to objectively identify the optimal reference frame for distinguishing and hence delineating an FLL, by statistically analyzing the temporal intensity variation across the spatially discretized ultrasonographic image. Level of confidence and clinical value of the proposed method were quantitatively evaluated on retrospective multi-institutional data (n = 64) and compared with expert interpretations. Results support the proposed method for facilitating easier, quicker and reproducible assessment of FLLs, further increasing the radiologists' confidence in diagnostic decisions. Finally, our method yields a useful training tool for radiologists, widening CEUS use in non-specialist centers, potentially leading to reduced turnaround times and lower patient anxiety and healthcare costs.

One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.

This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.

Generalized Laplacian eigenmaps for modeling and tracking human motions.

This paper presents generalized Laplacian eigenmaps, a novel dimensionality reduction approach designed to address stylistic variations in time series. It generates compact and coherent continuous spaces whose geometry is data-driven. This paper also introduces graph-based particle filter, a novel methodology conceived for efficient tracking in low dimensional space derived from a spectral dimensionality reduction method. Its strengths are a propagation scheme, which facilitates the prediction in time and style, and a noise model coherent with the manifold, which prevents divergence, and increases robustness. Experiments show that a combination of both techniques achieves state-of-the-art performance for human pose tracking in underconstrained scenarios.

Structural Laplacian Eigenmaps for modeling sets of multivariate sequences.

A novel embedding-based dimensionality reduction approach, called structural Laplacian Eigenmaps, is proposed to learn models representing any concept that can be defined by a set of multivariate sequences. This approach relies on the expression of the intrinsic structure of the multivariate sequences in the form of structural constraints, which are imposed on dimensionality reduction process to generate a compact and data-driven manifold in a low dimensional space. This manifold is a mathematical representation of the intrinsic nature of the concept of interest regardless of the stylistic variability found in its instances. In addition, this approach is extended to model jointly several related concepts within a unified representation creating a continuous space between concept manifolds. Since a generated manifold encodes the unique characteristic of the concept of interest, it can be employed for classification of unknown instances of concepts. Exhaustive experimental evaluation on different datasets confirms the superiority of the proposed methodology to other state-of-the-art dimensionality reduction methods. Finally, the practical value of this novel dimensionality reduction method is demonstrated in three challenging computer vision applications, i.e., view-dependent and view-independent action recognition as well as human-human interaction classification.

Tracking human position and lower body parts using Kalman and particle filters constrained by human biomechanics.

In this paper, a novel framework for visual tracking of human body parts is introduced. The approach presented demonstrates the feasibility of recovering human poses with data from a single uncalibrated camera by using a limb-tracking system based on a 2-D articulated model and a double-tracking strategy. Its key contribution is that the 2-D model is only constrained by biomechanical knowledge about human bipedal motion, instead of relying on constraints that are linked to a specific activity or camera view. These characteristics make our approach suitable for real visual surveillance applications. Experiments on a set of indoor and outdoor sequences demonstrate the effectiveness of our method on tracking human lower body parts. Moreover, a detail comparison with current tracking methods is presented.