Denis Laurendeau

A one-dimensional model in Python was developed to analyze the acoustic behavior of the Kurdish wind instrument, the şimşal. This model was developed using the A one-dimensional model in Python was developed to analyze the acoustic behavior of the Kurdish wind instrument, the şimşal. This model was developed using the

The accuracy of 3D reconstruction for metrology applications using active stereo pairs depends on the quality of the calibration of the system. Active stereo pairs are generally composed of cameras mounted on tilt/pan mechanisms separated by a constant or variable baseline. This paper presents a calibration approach based on speckle metrology that allows the separation of translation and rotation in the estimation of extrinsic parameters. To achieve speckle-based calibration, a device called an Almost Punctual Speckle Source (APSS) is introduced. Using the APSS, a thorough method for the calibration of extrinsic parameters of stereo pairs is described. Experimental results obtained with a stereo system called the Agile Stereo Pair (ASP) demonstrate that speckle-based calibration achieves better reconstruction performance than methods using standard calibration procedures. Although the experiments were performed with a specific stereo pair, such as the ASP, which is described in the paper, the speckle-based calibration approach using the APSS can be transposed to other stereo setups.

Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.

The aim of this paper is to document and compare the 2D qualitative and semi-quantitative methods currently used to describe the shape of gold grains in fluvial environments with the 3D quantitative methods using X-ray microtomography and SEM photogrammetry. These 3D methods are used to compute flatness, roundness, convexity, sphericity and ellipticity shape descriptors of 13 gold grains from the Rivière du Moulin (Québec, Canada) in order to quantify the morphological change along 9 km of fluvial transport. Gold grains have moderate to high values of flatness, compactness, sphericity and ellipticity indices that do not change significantly with distance of transport, whereas the roundness increases during transport. Gold grains are used to compare 2D and 3D methods, and the results show small differences (<8%) when shape descriptors are computed using image analysis software, whereas the difference (up to 70%) is more important for 2D measurements performed by a human operator. For application and characterization on a large set of gold grains, the 2D methods offer the advantage of speed, whereas, for a more detailed study on a limited number of gold grains, 3D methods enable estimation of the volume and yield more detailed shape descriptor changes during fluvial transport.

Physical interactions within virtual environments are often limited to visual information within a restricted workspace. A new system exploiting a cable-driven parallel robot to combine visual and haptic information related to environmental physical constraints (e.g. shelving, object weight) was developed. The aim of this study was to evaluate the impact on user movement patterns of adding haptic feedback in a virtual environment with this robot. Twelve healthy participants executed a manual handling task under three conditions: 1) in a virtual environment with haptic feedback; 2) in a virtual environment without haptic feedback; 3) in a real physical environment. Temporal parameters (movement time, peak velocity, movement smoothness, time to maximum flexion, time to peak wrist velocity) and spatial parameters of movement (maximum trunk flexion, range of motion of the trunk, length of the trajectory, index of curvature and maximum clearance from the shelf) were analysed during the reaching, lowering and lifting phases. Our results suggest that adding haptic feedback improves spatial parameters of movement to better respect the environmental constraints. However, the visual information presented in the virtual environment through the head mounted display appears to have an impact on temporal parameters of movement leading to greater movement time. Taken together, our results suggest that a cable-driven robot can be a promising device to provide a more ecological context during complex tasks in virtual reality.

Thermal imagery for monitoring of body temperature provides a powerful tool to decrease health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging). The presented approach discusses an experiment to simulate radiology conditions with infrared imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system uses an incremental low-rank noise reduction applying incremental singular value decomposition (SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary. Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera. This dataset is created to verify the robustness of our method with respect to motion-artifacts and in presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the infrared images in the dataset and was able to successfully measure and track the ROI continuously (100% detecting and tracking the temperature of participants), and provided considerable robustness against noise (unchanged accuracy even in 20% additive noise), which shows promising performance.

This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB-D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB-D sensors require either an offline step for supervised learning or 3D head model construction, which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject’s face in 3D rather than the complete head, which, in turn, relaxes all of the constraints in the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluated the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.

This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB--D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB--D sensors require either an offline step for supervised learning or 3D head model construction which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3--D rather than the complete head, which in turn, relaxes all of the constraints with the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluate the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.