Accurate motion capture plays an important role in sports analysis, the medical field and virtual reality. Current methods for motion capture often suffer from occlusions, which limits the accuracy of their pose estimation. In this paper, we propose a complete system to measure the pose parameters of the human body accurately. Different from previous monocular depth camera systems, we leverage two Kinect sensors to acquire more information about human movements, which ensures that we can still get an accurate estimation even when significant occlusion occurs. Because human motion is temporally constant, we adopt a learning analysis to mine the temporal information across the posture variations. Using this information, we estimate human pose parameters accurately, regardless of rapid movement. Our experimental results show that our system can perform an accurate pose estimation of the human body with the constraint of information from the temporal domain.

Non-contact human body measurement plays an important role in surveillance, physical healthcare, on-line business and virtual fitting. Current methods for measuring the human body without physical contact usually cannot handle humans wearing clothes, which limits their applicability in public environments. In this paper, we propose an effective solution that can measure accurate parameters of the human body with large-scale motion from a Kinect sensor, assuming that the people are wearing clothes. Because motion can drive clothes attached to the human body loosely or tightly, we adopt a space-time analysis to mine the information across the posture variations. Using this information, we recover the human body, regardless of the effect of clothes, and measure the human body parameters accurately. Experimental results show that our system can perform more accurate parameter estimation on the human body than state-of-the-art methods.

We address the problem of classifying 3D point clouds: given 3D urban street scenes gathered by a lidar sensor, we wish to assign a class label to every point. This work is a key step toward realizing applications in robots and cars, for example. In this paper, we present a novel approach to the classification of 3D urban scenes based on super-segments, which are generated from point clouds by two stages of segmentation: a clustering stage and a grouping stage. Then, six effective normal and dimension features that vary with object class are extracted at the super-segment level for training some general classifiers. We evaluate our method both quantitatively and qualitatively using the challenging Velodyne lidar data set. The results show that by only using normal and dimension features we can achieve better recognition than can be achieved with high-dimensional shape descriptors. We also evaluate the adopting of the MRF framework in our approach, but the experimental results indicate that thisbarely improved the accuracy of the classified results due to the sparse property of the super-segments.

In this paper, we propose a general framework to estimate the spectrum of the illumination from global specular information in a single hyperspectral image. By utilizing the specular independent subspace, we iteratively separate the reflectance components and shape a weight scheme in order to find specular-contaminated pixels. After that, the illumination can be directly estimated by factorizing the weighted specular-contaminated pixels. The proposed method enables a direct and effective decomposition of the illumination and reflectance components from a single hyperspectral image. We demonstrate the robustness and accuracy of our method on simulation and real experiments. Moreover, we capture a hyperspectral image dataset with ground-truth illumination to quantitative compare the performance.

Most 3D spine reconstruction methods require X-ray images as input, which usually leads to high cost and radiation damage. Therefore, these methods are hardly ever applied to large scale scoliosis screening or spine pose monitoring during treatment. We propose a novel, low-cost, easy-to-operate and none-radioactive 3D spine model reconstruction method, which is based on human back surface information without requiring X-ray images as input. Our method fits a pre-built Spine Priors Model (SPrM) to human back surface information and reconstructs the main part of spine with 17 vertebrae: lumbar vertebrae L1–L5 and thoracic vertebrae T1–T12. The Spine Priors Model is constructed according to human spine priors, including Statistical Spine Shape Model (SSSM), Spine Pose Model (SPM) and Spine Biomechanical Simplified Model (SBSM). The spine-related information on back surface, including back surface spinous curve and local symmetry nearby spinous curve is extracted from the RGBD images of human back surface. We formulate the spine optimization constraints from spine-related feature on back surface and spine priors, then optimize the spine model by gradient descent to get the optimal personalized shape parameters and pose parameters of the Spine Priors Model (SPrM). We assess our reconstruction by scoliosis Cobb angle error, and the result is comparable to current X-ray based methods.