Geometrical recovery of missing data for shape from motion

This paper studies the geometrical recovery of an incomplete observation matrix for converting existing 2D video sequences to 3D content. In situations when converting previously recorded monoscopic video to 3D, several entries of the observation matrix have not been observed and other entries have been perturbed by the influence of noise. In such cases, there is no simple solution for SVD factorization for shape from motion. In this paper, a new recovery algorithm is proposed for recovering missing feature points, by minimizing the influence of noise, using iteratively geometrical correlations between a 2D observation matrix and 3D shape. Results in practical situations demonstrated with synthetic and real video sequences verify the efficiency and flexibility of the proposed method.     

A robust dynamic programming algorithm for motion detection and estimation

A new robust algorithm for motion detection and precise evaluation of the motion vectors of moving objects in a sequence of images is presented. It is well known that the accuracy of estimating motion vectors estimation is limited by smoothness constraints and mutual occlusions of motion segments. The proposed method is a fusion of block-matching motion estimation and global optimization technique. It is robust to motion discontinuity and moving objects occlusions. To avoid some contradictions between global optimization techniques and piece-wise smooth values of sought motion vectors, a hidden segmentation model is utilized. Computer simulation and experimental results demonstrate an excellent performance of the method in terms of dynamic motion analysis. This article was translated by the authors. Mikhail Mozerov received his MS degree in Physics from the Moscow State University in 1982 and his PhD degree in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. He works at the Laboratory of Digital Optics of the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include signal and image processing, pattern recognition, digital holography. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at the Centro de Investigación Científica y de Educación Superior de Ensenada (Cicese), México. His research interests include signal and image processing, pattern recognition. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received candidate’s degree in 1953 and doctoral degree in information theory in 1972. At present, he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

基于AVS的快速亚像素运动估计算法

数字音视频编解码标准(AVS)采用了可变大小块以及1/4像素精度的运动估计技术以提高编码效率.针对视频编码中运动估计计算量大的问题,提出一种改进的亚像素快速搜索算法,该算法采用分象限预测策略,并通过阈值判断提前结束搜索.该算法可以有效地减少亚像素搜索点数,与AVS参考软件中的亚像素全搜索算法相比较,该算法在保证图像质量和编码效率的同时,亚像素搜索点数减少50％～81.25％,编码时间节省了25.36％～34.51％.     

Strength and range of motion of females with carpal tunnel syndrome

An experimental study was conducted to evaluate the grip and fingertip pinch strength, and range of motion of females with carpal tunnel syndrome (CTS). The range of motion of the wrist was measured in the sagittal, frontal, and transverse planes. Strength and range of motion measurements were also conducted on a control group. Results indicate that some strength and range of motion measures of the females with CTS were significantly different from that of the control group. Also, measurements in different planes caused significant differences in some range of motions of the wrist for both the CTS and the control groups.     

Manifold surface reconstruction of an environment from sparse Structure-from-Motion data

The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by Structure-from-Motion. The previous attempts to solve this problem are currently very limited: the surface is non-manifold or has zero genus, the experiments are done on small scenes or objects using a few dozens of images. Our solution does not have these limitations. Furthermore, we experiment with hand-held or helmet-held catadioptric cameras moving in a city and generate 3D models such that the camera trajectory can be longer than one kilometer.     

全插值分像素运动估计VLSI结构

提出一种高效的分像素运动估计VLSI结构。通过采用对整个搜索窗口进行并行插值,并设计数据路由结构对数据流进行分配存储的方法,在节省存储空间的同时,有效降低了存储器的访问次数,提高了数据利用率,解决了分像素运动估计数据存储量大、搜索窗口访存次数多以及搜索时间长的问题。在SMIC 0.13μm工艺下,用Synopsys DC进行逻辑综合。在时钟频率300 MHz下,处理1080P的视频图像,速度可以达到65 frame/s。     

Multiresolution image motion detection and displacement estimation

A motion vision system is developed in which a moving object can be detected and image displacement can be estimated based on human visual characteristics and use of a multiresolution image. The system consists of four parts: (1) Temporal gradient, logic AND, and dynamic thresholding operations are used to obtain the primary mask. (2) A region growing algorithm is applied. (3) A hierarchical object detection algorithm is used to identify image patterns. (4) Displacement of the image is estimated by breaking each frame of the motion sequence into local regions (edges). A search is undertaken to discover how the image pattern within a given region appears displaced. This search takes the form of motion channels, the output of which are used to obtain the estimation of displacement. A correlative measure is proposed to match the patterns.     

Direct surface reconstruction from a moving sensor

A technique is described for surface reconstruction from a sequence of images when the sensor motion is known. The reconstruction takes the form of an orthographic depth map in a viewer-independent frame. Following a mathematical justification, the technique is illustrated with examples using noisy synthetic images.     

Shape classification and normal estimation for non-uniformly sampled, noisy point data

We present an algorithm for robustly analyzing point data arising from sampling a 2D surface embedded in 3D, even in the presence of noise and non-uniform sampling. The algorithm outputs, for each data point, a surface normal, a local surface approximation in the form of a one-ring, the local shape (flat, ridge, bowl, saddle, sharp edge, corner, boundary), the feature size, and a confidence value that can be used to determine areas where the sampling is poor or not surface-like.We show that the normal estimation out-performs traditional fitting approaches, especially when the data points are non-uniformly sampled and in areas of high curvature. We demonstrate surface reconstruction, parameterization, and smoothing using the one-ring neighborhood at each point as an approximation of the full mesh structure.     

Foot surface area database and estimation formula

The purpose of this study is to establish a foot surface area (FSA) database and estimation formula based on 3-D foot scan data. For each gender, 135 subjects stratified in five statures and three body weights were drawn. The foot was measured using a high-resolution 3-D foot scanner, of which the precision and accuracy is within 1%. The FSA was computed by the triangular mesh summation method and five 1-D foot measurements were extracted automatically to be used as candidate estimators for FSA estimation formula. The results of the FSA measurements are tabulated on fifteen strata for the Male, the Female and the Total (the two genders combined). The comparison of these FSA measurements with previous studies shows that previous studies underestimated the FSA approximately 4.06% for the Total (for the Male, 6.93%; for the Female, 0.82%). Regression analyses using these five 1-D foot measurements were performed. The results show that foot-length and ball-girth are effective estimators of FSA for the total (FSA = 1.043 × foot-length × ball-girth, R² = 95.4%). A test on the necessity of gender-specific formula indicated that no gender-specific formula is needed, and the formula for the total is good for both genders.     

Robust real-time 3D head pose estimation from range data

In this paper a real-time 3D pose estimation algorithm using range data is described. The system relies on a novel 3D sensor that generates a dense range image of the scene. By not relying on brightness information, the proposed system guarantees robustness under a variety of illumination conditions, and scene contents. Efficient face detection using global features and exploitation of prior knowledge along with novel feature localization and tracking techniques are described. Experimental results demonstrate accurate estimation of the six degrees of freedom of the head and robustness under occlusions, facial expressions, and head shape variability.     

Pose estimation for objects with planar surfaces using eigenimage and range data analysis

In this paper we present a novel method for estimating the object pose for 3D objects with well-defined planar surfaces. Specifically, we investigate the feasibility of estimating the object pose using an approach that combines the standard eigenspace analysis technique with range data analysis. In this sense, eigenspace analysis was employed to constrain one object rotation and reject surfaces that are not compatible with a model object. The remaining two object rotations are estimated by computing the normal to the surface from the range data. The proposed pose estimation scheme has been successfully applied to scenes defined by polyhedral objects and experimental results are reported.     

Binocular matching constraints from motion

Structure from motion and structure from stereo are two vision cues for achieving 3D reconstruction. The two cues have complementary strengths; while 3D reconstruction is accurate but correspondence establishment is difficult in the stereo cue, the reverse is true in the motion cue. This paper addresses how to combine the two cues when a stereo pair of cameras are available to capture image data for 3D reconstruction. The work is distinct in that, in contrast with the previous ones, it is not to exploit the redundancy in the image data for boosting the reconstruction accuracy, but to make the two vision cues complementary, preserving their strengths and avoiding their weaknesses. A mechanism is introduced that allows dense motion correspondences in the two separate image streams be transferred to dense binocular correspondences across the image streams, so that 3D can be reconstructed from the latter and accurate reconstruction is possible even with short motions of the stereo rig. Both the stereo correspondences and the motion of the stereo rig are assumed to be unknown in this work. Experiments involving real image data are presented to indicate the feasibility and robustness of the approach.     

Preemptive RANSAC for live structure and motion estimation

A system capable of performing robust live ego-motion estimation for perspective cameras is presented. The system is powered by random sample consensus with preemptive scoring of the motion hypotheses. A general statement of the problem of efficient preemptive scoring is given. Then a theoretical investigation of preemptive scoring under a simple inlier–outlier model is performed. A practical preemption scheme is proposed and it is shown that the preemption is powerful enough to enable robust live structure and motion estimation.Prepared through collaborative participation in the Robotics Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0012. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. David Nistér received PhD degree in computer vision, numerical analysis and computing science from the Royal Institute of Technology (KTH), Stockholm, Sweden, with the thesis ‘Automatic Dense Reconstruction from Uncalibrated Video Sequences’. He is currently an assistant professor at the Computer Science Department and the Center for Visualization and Virtual Environments, University of Kentucky, Lexington. Before joining UK, he was a researcher in the Vision Technologies Laboratory, Sarnoff Corporation, Princeton, and Visual Technology, Ericsson Research, Stockholm, Sweden. His research interests include computer vision, computer graphics, structure from motion, multiple view geometry, Bayesian formulations, tracking, recognition, image and video compression. He is a member of the IEEE and American Mensa.     

Three-dimensional motion and reconstruction of coronary arteries from biplane cineangiography

A new approach is described for reconstructing coronary arteries from two sequences of projection images. The estimation of motion is performed on three-dimensional line segments (or centrelines), and is based on a ‘predictionprojection-optimization’ loop. The method copes with time varying properties, deformations and superpositions of vessels. Experiments using simulated and real data have been carried out. and the results found to be robust over a full cycle of a human heart. Local and global kinetic features can then be derived to obtain a greater insight on the cardiac functional state     

Invariant image reconstruction from irregular samples and hexagonal grid splines

We present an algorithm for the reconstruction of images from irregularly placed samples, using linear splines with control points on a hexagonal grid. Several spline approximations are computed for different transformations of the control point grid (e.g. translations and rotations). These approximations are then merged together after compensation of the transformations, yielding a high-quality invariant image reconstruction. Evaluations show that the use of hexagonal grids of the “invariance by integration” principle improves reconstruction quality. An application to image coding is also presented.     

Fair, G- and C-continuous circle splines for the interpolation of sparse data points

This paper presents a carefully chosen curve blending scheme between circles, which is based on angles, rather than point positions. The result is a class of circle splines that robustly produce fair-looking G²-continuous curves without any cusps or kinks, even through rather challenging, sparse sets of interpolation points. With a simple reparameterization the curves can also be made C²-continuous. The same method is usable in the plane, on the sphere, and in 3D space.