Microsoft Kinect sensor has been widely used in many applications since the launch of its first version. Recently, Microsoft released a new version of Kinect sensor with improved hardware. However, the accuracy assessment of the sensor remains to be answered. In this paper, we measure the depth accuracy of the newly released Kinect v2 depth sensor, and obtain a cone model to illustrate its accuracy distribution. We then evaluate the variance of the captured depth values by depth entropy. In addition, we propose a trilateration method to improve the depth accuracy with multiple Kinects simultaneously. The experimental results are provided to ascertain the proposed model and method.

With the increase in health consciousness, noninvasive body monitoring has aroused interest among researchers. As one of the most important pieces of physiological information, researchers have remotely estimated the heart rate from facial videos in recent years. Although progress has been made over the past few years, there are still some limitations, like the processing time increasing with accuracy and the lack of comprehensive and challenging datasets for use and comparison. Recently, it was shown that heart rate information can be extracted from facial videos by spatial decomposition and temporal filtering. Inspired by this, a new framework is introduced in this paper for remotely estimating the heart rate under realistic conditions by combining spatial and temporal filtering and a convolutional neural network. Our proposed approach shows better performance compared with the benchmark on the MMSE-HR dataset in terms of both the average heart rate estimation and short-time heart rate estimation. High consistency in short-time heart rate estimation is observed between our method and the ground truth.

Augmented-reality (AR) technology has been developing rapidly for decades. A recently released cutting-edge AR device, Microsoft HoloLens, has attracted considerable attention with its advanced capabilities. In this paper, we report the design and execution of a series of experiments to quantitatively evaluate HoloLens' performance in head localization, real environment reconstruction, spatial mapping, hologram visualization, and speech recognition. The results show that HoloLens is able to estimate head posture more correctly at low movement speeds, reconstruct the environment most precisely for a flat surface under bright conditions, anchor augmented contents at desired locations most accurately at distances of 1.5 m and 2.5 m, display objects with an average size error of 6.64%, and recognize speech commands with correctness rates of 74.47% and 66.87% for userdefined and system-defined commands, respectively. Discussions are also provided to further explain our work and the limitations of the experiments.

As an unsupervised representation problem in deep learning, next-frame prediction is a new, promising direction of research in computer vision, predicting possible future images by presenting historical image information. It provides extensive application value in robot decision making and autonomous driving. In this paper, we introduce recent state-of-the-art next-frame prediction networks and categorize them into two architectures: sequence-to-one architecture and sequence-to-sequence architecture. After comparing these approaches by analyzing the loss function design and network architecture, the pros and cons are analyzed. Based on the off-the-shelf data-sets and the corresponding evaluation metrics, the performance of the aforementioned approaches is quantitatively compared. The future promising research directions are pointed out at last.

Date fruits are small fruits that are abundant and popular in the Middle East, and have growing international presence. There are many different types of dates, each with different features. Sorting of dates is a key process in the date industry, and can be a tedious job. In this paper, we present a method for automatic classification of date fruits based on computer vision and pattern recognition. The method was implemented, and empirically tested on an image data spanning seven different categories of dates. In our method, an appropriately crafted mixture of fifteen different visual features was extracted, and then, multiple methods of classification were tried out, until satisfactory performance was achieved. Top accuracies ranged between 89% and 99%.

3D pose estimation is a challenging problem in computer vision. Most of the existing neural-network-based approaches address color or depth images through convolution networks (CNNs). In this paper, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimation method, we propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. We first cast the 3D human pose estimation from 2D depth images to 3D point clouds and directly predict the 3D joint position. Our experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods. The reported results on both ITOP and EVAL datasets demonstrate the effectiveness of our method on the targeted tasks.