Outlier correction from uncalibrated image sequence using the Triangulation method

We propose a robust algorithm for estimating the projective reconstruction from image features using the RANSAC-based Triangulation method. In this method, we select input points randomly, separate the input points into inliers and outliers by computing their reprojection error, and correct the outliers so that they can become inliers. The reprojection error and correcting outliers are computed using the Triangulation method. After correcting the outliers, we can reliably recover projective motion and structure using the projective factorization method. Experimental results showed that errors can be reduced significantly compared to the previous research as a result of robustly estimated projective reconstruction.     

图像序列中运动人体的分割方法的研究

在对现有的运动分割算法的研究基础上结合运动分析方法,对基于图像帧差法构造背景图像进行了深入的分析和研究,提出了一种利用阈值来判定人体信息和背景信息以达到背景减除的新算法来实时地提取出运动的人体,在利用帧差法提取出背景图像后,通过当前帧和背景图像的像素值的对比减除可快速精确地完成对运动人体的分割.在NLPR数据库中进行了实验,结果表明本算法能快速有效地一次提取出多个人体目标,并且失真度比较小.     

Closed-form solutions to image flow equations for 3D structure and motion

A major source of three-dimensional (3D) information about objects in the world is available to the observer in the form of time-varying imagery. Relative motion between textured objects and the observer generates a time-varying optic array at the image, from which image motion of contours, edge fragments, and feature points can be extracted. These dynamic features serve to sample the underlying image flow field. New, closed-form solutions are given for the structure and motion of planar and curved surface patches from monocular image flow and its derivatives through second order. Both planar and curved surface solutions require at most, the solution of a cubic equation. The analytic solution for curved surface patches combines the transformation of Longuet-Higgins and Prazdny [25] with the planar surface solution of Subbarao and Waxman [43]. New insights regarding uniqueness of solutions also emerge. Thus, the structure-motion coincidence of Waxman and Ullman [54] is interpreted as the duality of tangent plane solutions. The multiplicity of transformation angles (up to three) is related to the sign of the Gaussian curvature of the surface patch. Ovoid patches (i.e., bowls) are shown to possess a unique transform angle, though they are subject to the local structure-motion coincidence. Thus, ovoid patches almost always yield a unique 3D interpretation. In general, ambiguous solutions can be resolved by requiring continuity of the solution over time.The support of the Defense Advanced Research Projects Agency and the U.S. Army Night Vision Laboratory under Contract DAAK70-83-K-0018 (DARPA Order 3206) is gratefully acknowledged.     

From an image sequence to a recognized polyhedral object

The paper describes the combination of several novel algorithms into a system that obtains visual motion from a sequence of images and uses it to recover the three-dimensional (3D) geometry and 3D motion of polyhedral objects relative to the sensor. The system goes on to use the recovered geometry to recognize the object from a database, a stage which also resolves the depth/speed scaling ambiguity, resulting in absolute depth and motion recovery. The performance of the system is demonstrated on imagery from a well carpentered constructive solid geometry (CSG) model and on real imagery from a simple wooden model.     

Estimation of 3D structure and motion from image corners

This paper deals with motion estimation from image corner correspondences in two cases: the orthogonal corner and the general corner with known space angles. The contribution of the paper is in three folds: First, the three-dimensional structure of a corner is recovered easily from its image by introducing a new coordinate system; second, it is shown that the one corner and two points correspondences over two views are sufficient to uniquely determine the motion, i.e., the rotation and translation; third, experiments using both simulated data and real images are conducted, which present good results.     

3-D translational motion and structure from binocular image flows

Image flow fields from parallel stereo cameras are analyzed to determine the relative 3-D translational motion of the camera platform with respect to objects in view and to establish stereo correspondence of features in the left and right images. A two-step procedure is suggested. In the first step, translational motion parameters are determined from linear equations the coefficients of which consist of the sums of measured quantities in the two images. Separate equations are developed for cases when measurements of either the full optical flow or the normal flow are available. This computation does not require feature-to-feature correspondence. In addition, no assumption is made about the surfaces being viewed. In the second step of the calculation, with the knowledge of the estimated translational motion parameters, the binocular flow information is used to find features in one image that correspond to given features in the other image. Experimental results with synthetic and laboratory images indicate that the method provides accurate results even in the presence of noise     

Color correction for an image sequence

The color correction technique presented constructs a mapping from the registered overlapping regions of two adjacent images in an image sequence. The method involves two main steps. In the first step we use multiple regression to find the relationship between the registered overlapping regions of two adjacent images in the sequence. We then use the derived mapping to correct the color in one image so that it matches the adjacent image. After describing the basic method and discussing strategies far dealing with specific problems that arise, we present and analyze relevant test results     

Using amplification to compute majority with small majority gates

In this paper, we consider the formula complexity of the majority function over a basis consisting only of small (bounded-size) majority gates. Using Valiant's amplification technique, we show that there is a formula of sizeO(n ^4.29 ) when only the gateM ₃ (the majority gate on three inputs) is used. Then, based on a result of Boppana, we show that not only is our result optimal with respect to the amplification technique, there is no smaller formula over the basis of all monotone 3-input functions (again with respect to amplification). Finally, we show that no better bounds are possible even with respect to more general input distributions. In particular, we show that it is not possible to use amplification to bootstrap, that is, use smaller majority functions in the initial distribution to find an optimal formula for a larger majority function.     

Inferring 3D structure from image motion: The constraint of Poinsot motion

Monocular observers perceive as three-dimensional (3D) many displays that depict three points rotating rigidly in space but rotating about an axis that is itself tumbling. No theory of structure from motion currently available can account for this ability. We propose a formal theory for this ability based on the constraint of Poinsot motion, i.e., rigid motion with constant angular momentum. In particular, we prove that three (or more) views of three (or more) points are sufficient to decide if the motion of the points conserves angular momentum and, if it does, to compute a unique 3D interpretation. Our proof relies on an upper semicontinuity theorem for finite morphisms of algebraic varieties. We discuss some psychophysical implications of the theory.This work was supported by National Science Foundation grants IRI-8700924 and DIR-9014278 and by Office of Naval Research contract N00014-88-K-0354.     

Image sequence enhancements based on the normal component of image motion

Huang and Hsu (1981) describe an image sequence enhancement algorithm based on computing motion vectors between successive frames and using these vectors to determine the correspondence between pixels for frame averaging. In this note, we demonstrate that it may be sufficient to use only the components of the motion vectors in the gradient direction (called the normal components) to perform the enhancement.     

Real-time velocity measurement to linear motion of a rigid object with monocular image sequence analyses

This paper presents a methodology and all procedures used to validate it, which were executed in a physics laboratory under controlled and known conditions. The validation was based on the analyses of registered data in an image sequence and the measurements acquired by high precision sensors. This methodology intended to measure the velocity of a rigid object in linear motion with the use of an image sequence acquired by commercial digital video camera. The proposed methodology does not need a stereo pair of images to calculate the object position in the 3D space: it needs only images sequence acquired for one, only one, angle view (monocular vision). To do so, these objects need to be detected while in movement, which is conducted by the application of a segmentation technique based on the temporal average values of each pixel registered in N consecutive image frames. After detecting and framing these objects, specific points belonging to the object (pixels), on the plane image (2D coordinates or space image), are automatically chosen, which are then transformed into corresponding points in the space object (3D coordinates) by the application of collinearity equations or rational functions (proposed in this work). After obtaining the coordinates of these points in the space object that are registered in the sequence of images, the distance, in meters, covered by the object in a particular time interval may be measured and, consequently, its velocity can be calculated. The system is low cost, use only a computer (architecture Intel I3), and a webcam used to acquire the images (640 × 480, 30 fps). The complexity of the algorithm is linear, fact that allows the system to operate in real time. The results of the analyses are discussed and the advantages and disadvantages of the method are presented.     

Using algebraic software to compute the moments of order statistics

Here we examine using the algebraic software Mathematica for computing the moments of the order statistics of several frequently used distribution functions, specifically those for the extreme-value, Weibull, double Weibull, logistic and Cauchy distributions. These are illustrated in obtaining the generalised least-squares estimates of the location and scale parameters of the extreme-value distribution.     

Transitory image sequences, asymptotic properties, and estimationof motion and structure

A transitory image sequence is one in which no scene element is visible through the entire sequence. This article deals with some major theoretical and algorithmic issues associated with the task of estimating structure and motion from transitory image sequences. It is shown that integration with a transitory sequence has properties that are very different from those with a nontransitory one. Two representations, world-centered (WC) and camera-centered (CC), behave very differently with a transitory sequence. The asymptotic error rates derived in this article indicate that one representation is significantly superior to the other, depending on whether one needs camera-centered or world-centered estimates. We introduce an efficient “cross-frame” estimation technique for the CC representation. For the WC representation, our analysis indicates that a good technique should be based on camera global pose instead of interframe motions. Rigorous experiments were conducted with real-image sequences taken by a fully calibrated camera system     

The feasibility of motion and structure from noisy time-varying image velocity information

This research addresses the problem of noise sensitivity inherent in motion and structure algorithms. The motion and structure paradigm is a two-step process. First, we measure image velocities and, perhaps, their spatial and temporal derivatives, are obtained from time-varying image intensity data and second, we use these data to compute the motion of a moving monocular observer in a stationary environment under perspective projection, relative to a single 3-D planar surface. The first contribution of this article is an algorithm that uses time-varying image velocity information to compute the observer's translation and rotation and the normalized surface gradient of the 3-D planar surface. The use of time-varying image velocity information is an important tool in obtaining a more robust motion and structure calculation. The second contribution of this article is an extensive error analysis of the motion and structure problem. Any motion and structure algorithm that uses image velocity information as its input should exhibit error sensitivity behavior compatible with the results reported here. We perform an average and worst case error analysis for four types of image velocity information: full and normal image velocities and full and normal sets of image velocity and its derivatives. (These derivatives are simply the coefficients of a truncated Taylor series expansion about some point in space and time.) The main issues we address here are: just how sensitive is a motion and structure computation in the presence of noisy input, or alternately, how accurate must our image velocity information be, how much and what type of input data is needed, and under what circumstances is motion and structure feasible? That is, when can we be sure that a motion and structure computation will produce usable results? We base our answers on a numerical error analysis we conduct for a large number of motions.     

Using hidden Markov models to predict DNA-binding proteins with sequence and structure information

In the post-genome period, the protein domain structures are published rapidly, but they have not been studied comprehensively. To figure out the cell function, the protein–DNA interactions decrypt the protein domain structures in recent research. Several machine-learning based methods are applied to the issue; however, they are not efficient to translate the tertiary structure characteristics of proteins into appropriate features for predicting the DNA-binding proteins. In this work, a novel machine-learning approach based on hidden Markov models identifies the characteristics of DNA-binding proteins with their amino acid sequences and tertiary structures. After we distill the features from DNA-binding proteins, a support vector machine based classifier predicts general DNA-binding proteins with the accuracy of 88.45 % through fivefolds cross-validation. Furthermore, we construct a response element specific classifier for predicting response element specific DNA-binding proteins, and the performance achieves the precision of 96.57 % with recall rate as 88.83 % in average. To verify the prediction of DNA-binding proteins, we used the DNA-binding proteins from MCF-7 that are likely to bind with estrogen response elements (ERE), and the results show that our methods can apply to practice.     

Using automatic differentiation to compute derivatives for a quantum-chemical computer program

The ADIFOR 2.0 tool for Automatic Differentiation of Fortran programs has been used to generate analytic gradient code for all semiempirical SCF methods available in the MNDO97 program. The correctness and accuracy of the new code have been verified. Its performance has been compared with that of hand-coded analytic derivative routines and with numerical differentiation. From a quantum-chemical point of view, the major advance of this work is the development of previously unavailable analytic gradient code for the recently proposed OM1 and OM2 methods.     

Driving saccade to pursuit using image motion

Within the context of active vision, scant attention has been paid to the execution of motion saccades—rapid re-adjustments of the direction of gaze to attend to moving objects. In this paper we first develop a methodology for, and give real-time demonstrations of, the use of motion detection and segmentation processes to initiate capture saccades towards a moving object. The saccade is driven by both position and velocity of the moving target under the assumption of constant target velocity, using prediction to overcome the delay introduced by visual processing. We next demonstrate the use of a first order approximation to the segmented motion field to compute bounds on the time-to-contact in the presence of looming motion. If the bound falls below a safe limit, a panic saccade is fired, moving the camera away from the approaching object. We then describe the use of image motion to realize smooth pursuit, tracking using velocity information alone, where the camera is moved so as to null a single constant image motion fitted within a central image region. Finally, we glue together capture saccades with smooth pursuit, thus effecting changes in both what is being attended to and how it is being attended to. To couple the different visual activities of waiting, saccading, pursuing and panicking, we use a finite state machine which provides inherent robustness outside of visual processing and provides a means of making repeated exploration. We demonstrate in repeated trials that the transition from saccadic motion to tracking is more likely to succeed using position and velocity control, than when using position alone.