Learning Temporal Distribution and Spatial Correlation Toward Universal Moving Object Segmentation.

The goal of moving object segmentation is separating moving objects from stationary backgrounds in videos. One major challenge in this problem is how to develop a universal model for videos from various natural scenes since previous methods are often effective only in specific scenes. In this paper, we propose a method called Learning Temporal Distribution and Spatial Correlation (LTS) that has the potential to be a general solution for universal moving object segmentation.

Saliency-Driven Hand Gesture Recognition Incorporating Histogram of Oriented Gradients (HOG) and Deep Learning.

Hand gesture recognition is a vital means of communication to convey information between humans and machines. We propose a novel model for hand gesture recognition based on computer vision methods and compare results based on images with complex scenes. While extracting skin color information is an efficient method to determine hand regions, complicated image backgrounds adversely affect recognizing the exact area of the hand shape.

Cognition and Financial Decision-Making in Older Adult Spouses.

Introduction: Age-related decline in executive functioning has been found to negatively impact one's capacity to make prudent financial decisions. The broader literature also speaks to the importance of considering interrelatedness in older spouses' functioning, as these individuals typically represent one's longest and closest relationship that involves an extended history of shared experiences. Accordingly, the aim of the present study was to provide the first examination of whether older adults' financial decision-making capacity is impacted not only by their own but also by their partner's, level of cognitive functioning.

Dual-Graph Global and Local Concept Factorization for Data Clustering.

Considering a wide range of applications of nonnegative matrix factorization (NMF), many NMF and their variants have been developed. Since previous NMF methods cannot fully describe complex inner global and local manifold structures of the data space and extract complex structural information, we propose a novel NMF method called dual-graph global and local concept factorization (DGLCF). To properly describe the inner manifold structure, DGLCF introduces the global and local structures of the data manifold and the geometric structure of the feature manifold into CF.

Universal Background Subtraction Based on Arithmetic Distribution Neural Network.

We propose a universal background subtraction framework based on the Arithmetic Distribution Neural Network (ADNN) for learning the distributions of temporal pixels. In our ADNN model, the arithmetic distribution operations are utilized to introduce the arithmetic distribution layers, including the product distribution layer and the sum distribution layer. Furthermore, in order to improve the accuracy of the proposed approach, an improved Bayesian refinement model based on neighboring information, with a GPU implementation, is incorporated.

3D Reconstruction of Carotid Artery from Ultrasound Images.

3D reconstruction is an important area in computer vision, which can be applied to assist in medical diagnosis. Compared to observing 2D ultrasound images, 3D models are more suitable for diagnostic interpretation. In this paper, we describe an approach for 3D reconstruction of the carotid artery utilizing ultrasound images from the transverse and longitudinal views.

XAI Feature Detector for Ultrasound Feature Matching.

Feature matching is a crucial component of computer vision that has various applications. With the emergence of Computer-Aided Diagnosis (CAD), the need for feature matching has also emerged in the medical imaging field. In this paper, we proposed a novel algorithm using the Explainable Artificial Intelligence (XAI) [1] approach to achieve feature detection for ultrasound images based on the Deep Unfolding Super-resolution Network (USRNET).

Pixel Distribution Learning for Vessel Segmentation under Multiple Scales.

In this work we try to address if there is a better way to classify two distributions, rather than using histograms; and answer if we can make a deep learning network learn and classify distributions automatically. These improvements can have wide ranging applications in computer vision and medical image processing. More specifically, we propose a new vessel segmentation method based on pixel distribution learning under multiple scales.

RAVA: Region-Based Average Video Quality Assessment.

Video has become the most popular medium of communication over the past decade, with nearly 90 percent of the bandwidth on the Internet being used for video transmission. Thus, evaluating the quality of an acquired or compressed video has become increasingly important. The goal of video quality assessment (VQA) is to measure the quality of a video clip as perceived by a human observer.

A hybrid level set model for image segmentation.

Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information.

Alternative Techniques for Breast Tumour Detection using Ultrasound.

Ultrasound images are potentially invaluable for imaging internal organs and diseases. However, due to noise, they are still difficult to interpret. We apply and compare supervised machine learning approaches to train a model of lesions using features with unsupervised machine learning approaches to segment and detect tumours in breasts.

Automatic Classification and Monitoring of Denovo Parkinson's Disease by Learning Demographic and Clinical Features.

Parkinson's Disease (PD) is the second most prevalent progressive neurological disorder around the world with high incidence rates for seniors. Since most symptoms are exposed in the later stages of the disease, early diagnosis of PD is essential for more effective treatment. The motivation of this research is early automatic assessment of PD using clinical information, not only for disease diagnosis but also for monitoring progression.

Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson's Disease.

Parkinson's disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients' quality of life, and even death.

Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net.

A Fully Convolutional Network (FCN) based deep architecture called Dual Path U-Net (DPU-Net) is proposed for automatic segmentation of the lumen and media-adventitia in IntraVascular UltraSound (IVUS) frames, which is crucial for diagnosis of many cardiovascular diseases and also for facilitating 3D reconstructions of human arteries. One of the most prevalent problems in medical image analysis is the lack of training data. To overcome this limitation, we propose a twofold solution.

Eye gaze of endoscopists during simulated colonoscopy.

Regaining orientation during an endoscopic procedure is critical. We investigated how endoscopists maintain orientation based on video and eye gaze analysis. Novices and experts performed a simulated colonoscopy procedure.

A fast segmentation-free fully automated approach to white matter injury detection in preterm infants.

White matter injury (WMI) is the most prevalent brain injury in the preterm neonate leading to developmental deficits. However, detecting WMI in magnetic resonance (MR) images of preterm neonate brains using traditional WM segmentation-based methods is difficult mainly due to lack of reliable preterm neonate brain atlases to guide segmentation. Hence, we propose a segmentation-free, fast, unsupervised, atlas-free WMI detection method.

Segmentation of arterial walls in intravascular ultrasound cross-sectional images using extremal region selection.

Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. Segmentation of arterial wall boundaries from the IVUS images is not only crucial for quantitative analysis of the vessel walls and plaque characteristics, but is also necessary for generating 3D reconstructed models of the artery. The aim of this study is twofold.

Investigation of gaze patterns in multi view laparoscopic surgery.

Laparoscopic Surgery (LS) is a modern surgical technique whereby the surgery is performed through an incision with tools and camera as opposed to conventional open surgery. This promises minimal recovery times and less hemorrhaging. Multi view LS is the latest development in the field, where the system uses multiple cameras to give the surgeon more information about the surgical site, potentially making the surgery easier.

Low resolution tool tracking for microsurgical training in a simulated environment.

In this work, we propose a method that detects and tracks the tip of tools used in microsurgical training. This method can be used to provide valuable metrics regarding the surgeon's hand movement. It can benefit the training of surgeons, given the steep learning curve in microsurgery.

Determining team cognition from delay analysis using cross recurrence plot.

Team cognition is an important factor in evaluating and determining team performance. Forming a team with good shared cognition is even more crucial for laparoscopic surgery applications. In this study, we analyzed the eye tracking data of two surgeons during a laparoscopic simulation operation, then performed Cross Recurrence Analysis (CRA) on the recorded data to study the delay behaviour for good performer and poor performer teams.

Smart Sensor-Based Motion Detection System for Hand Movement Training in Open Surgery.

We introduce a smart sensor-based motion detection technique for objective measurement and assessment of surgical dexterity among users at different experience levels. The goal is to allow trainees to evaluate their performance based on a reference model shared through communication technology, e.g.

Ground truth delineation for medical image segmentation based on Local Consistency and Distribution Map analysis.

Computer-aided detection (CAD) systems are being increasingly deployed for medical applications in recent years with the goal to speed up tedious tasks and improve precision. Among others, segmentation is an important component in CAD systems as a preprocessing step to help recognize patterns in medical images. In order to assess the accuracy of a CAD segmentation algorithm, comparison with ground truth data is necessary.

Graph Matching Based on Stochastic Perturbation.

This paper presents a novel perspective on characterizing the spectral correspondence between the nodes of weighted graphs for image matching applications. The algorithm is based on the principal feature components obtained by stochastic perturbation of a graph. There are three areas of contributions in this paper.

Stochastic process for white matter injury detection in preterm neonates.

Preterm births are rising in Canada and worldwide. As clinicians strive to identify preterm neonates at greatest risk of significant developmental or motor problems, accurate predictive tools are required. Infants at highest risk will be able to receive early developmental interventions, and will also enable clinicians to implement and evaluate new methods to improve outcomes.

Touchfree medical interfaces.

Real-time control of visual display systems via mid-air hand gestures offers many advantages over traditional interaction modalities. In medicine, for example, it allows a practitioner to adjust display values, e.g.