Measuring the distance is one of the main objectives for sensor development. In this article, we present a new sensor system design for depth recovery. It is based on stereopsis with the integrated rotation of an image sensor. Different from conventional stereo vision systems with multiple cameras, the proposed rotational stereo configuration is built upon a single imaging device. By directing its lateral motion with a circular constraint, the visual parallax is generated from multiple viewpoints. The image pairs captured with the integrated sensor rotation are used for the correspondence matching and disparity computation. Moreover, a variation of multiple-baseline stereo with multiple base-angle implementation is proposed. It provides a flexible baseline setting to improve the depth measurement accuracy. The geometric formulation and computational algorithm are derived and developed. In the experiments, a prototype range sensing device is constructed. The results obtained from the real scene environment have demonstrated the feasibility of the proposed depth measurement approach.

Multitask learning (MTL) has achieved notable progress in many medical applications. In this article, we propose a multitask neural network, MRNet, for segmentation and spinal parameter inspection. It is developed based on the multipath convolutional neural network for the robust detection of obscure regions on X-ray images. The proposed MRNet has two branches. One is for the segmentation of lumbar vertebrae, sacrum, and femoral heads. It shares the main features with the second branch for detection and classification by supervised learning. The output of the second branch is used to estimate the parameters for lumbosacral spine inspection. We conduct this research on our dataset collected and annotated by doctors for model training and performance evaluation. The datasets are used to train our MRNet as well as other networks for performance evaluation. Compared to the state-of-the-art techniques, the proposed MRNet is capable of X-ray image segmentation and parameter estimation with very limited training data. The results have demonstrated the feasibility of our MRNet for the segmentation of lumbar vertebrae, as well as the automated parameter prediction for lumbosacral spine inspection. Code is available at https://github.com/LuanTran07/BiLUnet-Lumbar-Spine .

The ability to reliably measure the depth of the object surface is very important in a range of high-value industries. With the development of 3D vision techniques, RGB-D cameras have been widely used to perform the 6D pose estimation of target objects for a robotic manipulator. Many applications require accurate shape measurements of the objects for 3D template matching. In this work, we develop an RGB-D camera based on the structured light technique with gray-code coding. The intrinsic and extrinsic parameters of the camera system are determined by a calibration process. 3D reconstruction of the object surface is based on the ray triangulation principle. We construct an RGB-D sensing system with an industrial camera and a digital light projector. In the experiments, real-world objects are used to test the feasibility of the proposed technique. The evaluation carried out using planar objects has demonstrated the accuracy of our RGB-D depth measurement system.

The ability of understanding a scene and predicting the pose of objects has attracted significant interests in recent years. Specifically, it is used with visual sensors to provide the information for a robotic manipulator to interact with the target. Thus, 6D pose estimation and object recognition from point clouds or RGB-D images are important tasks for visual servoing. In this paper, we propose a learning based approach to perform 6D pose estimation for robotic manipulation using a BiLuNetICP pipeline. It consists of a multi-path convolutional neural network (CNN) for semantic segmentation on RGB images. The network extracts the object mask and uses it to merge with the depth information to perform 6D pose estimation by the Iterative Closest Point (ICP) algorithm. We collect our own dataset for training and evaluate with Intersection over Union (IoU). The proposed method is able to provide better results compared with Unet++ using a small amount of training data. For the robotic grasping application, we test and evaluate our approach using a HIWIN 6-axis robot with Asus Xtion Live 3D camera and our structured-light depth camera. The experimental results demonstrate its efficiency in computation and the high success rate in grasping.

This paper investigates the application of the kernel PCA to select the features that produced by an extraction feature method, i.e. complete local binary pattern from three orthogonal planes. The proposed approach is used to detect Alzheimer's disease using 3D Magnetic Resonance Images (MRI) of brain. In this study, the feature extraction method is done by using the different radius and the number of different neighbors. A support vector machine classifier is adapted to discriminant normal from Alzheimer's, normal from mild cognitive impairment (MCI) and MCI from Alzheimer's. The experimental results show our proposed method achieves an accuracy of 100% for classification of Alzheimer's and normal. This accuracy result is also achieved by MCI and normal classification, whereas the accuracy of Alzheimer's and MCI classification is only 84%.