Bayesian estimation of motion vector fields

A stochastic approach to the estimation of 2D motion vector fields from time-varying images is presented. The formulation involves the specification of a deterministic structural model along with stochastic observation and motion field models. Two motion models are proposed: a globally smooth model based on vector Markov random fields and a piecewise smooth model derived from coupled vector-binary Markov random fields. Two estimation criteria are studied. In the maximum a posteriori probability (MAP) estimation, the a posteriori probability of motion given data is maximized, whereas in the minimum expected cost (MEC) estimation, the expectation of a certain cost function is minimized. Both algorithms generate sample fields by means of stochastic relaxation implemented via the Gibbs sampler. Two versions are developed: one for a discrete state space and the other for a continuous state space. The MAP estimation is incorporated into a hierarchical environment to deal efficiently with large displacements     

Detecting singular patterns in 2D vector fields using weighted Laurent polynomial

In this paper, we propose a method for detecting patterns of interest in vector fields. Our method detects patterns in a scale- and rotation-invariant manner. It works by approximating the vector-field data locally using a Laurent polynomial weighted by radial basis functions. The proposed representation is able to model both analytic and non-analytic flow fields. Invariance to scale and rotation is achieved by combining the linearity properties of the model coefficients and a scale-space parameter of the radial basis functions. Promising detection results are obtained on a variety of fluid-flow sequences.     

Fast curvilinear structure extraction and delineation using density estimation

Detection and delineation of lines is important for many applications. However, most of the existing algorithms have the shortcoming of high computational cost and can not meet the on-board real-time processing requirement. This paper presents a novel method for curvilinear structure extraction and delineation by using kernel-based density estimation. The method is based on efficient calculation of pixel-wise density estimation for an input feature image, which is termed as local weighted features (LWF). For gray and binary images, the LWF can be efficiently calculated by integral image and accumulated image, respectively. Detectors for small objects and centerlines based on LWF are developed and the selection of density estimation kernels is also illustrated. The algorithm is very fast and achieves 50 fps on a PIV2.4G processor. Evaluation results on a number of images and videos are given to demonstrate the satisfactory performances of the proposed method with its high stability and adaptability.     

基于NURBS曲面拟合的三角网格曲率计算

针对三角网格提出了一种基于NURBS曲面拟合的计算Gauss曲率和平均曲率的算法。首先选取边界检测后的二阶邻点作为局部拟合数据,采用直接投影法实现参数化,由二次NURBS曲面进行最小平方拟合反算控制点矩阵,最后由拟合曲面计算曲率。并从三角网格分辨率和噪声两方面进行了比较,实验结果表明本文算法精度高、较其他算法稳定,因而更具通用性。     

Collision-free object movement using vector fields

Presents a technique for automatically providing animation and collision avoidance in a general-purpose computer graphics system. The technique, which relies on an expanded notion of vector fields, allows users to set up and animate objects easily, then prevents objects from colliding as the animation proceeds. This technique automatically generates volume octree vector fields around objects in a scene. These vector fields affect object motion and animation, and also provide for automatic collision avoidance for arbitrary objects. Applications of collision avoidance in an animation system encompass any scene containing object movement above or around other objects     

The estimation of velocity vector fields from time-varying image sequences

Changes in successive images from a time-varying image sequence of a scene can be characterized by velocity vector fields. The estimate of the velocity vector field is determined as a compromise between optical flow and directional smoothness constraints. The optical flow constraints relate the values of the time-varying image function at the corresponding points of the successive images of the sequence. The directional smoothness constraints relate the values of neighboring velocity vectors. To achieve the compromise, we introduce a system of nonlinear equations of the unknown estimate of the velocity vector field using a novel variational principle applied to the weighted average of the optical flow and the directional smoothness constraints. A stable iterative method for solving this system is developed. The optical flow and the directional smoothness constraints are selectively suppressed in the neighborhoods of the occluding boundaries by implicitly adjusting their weights. These adjustments are based on the spatial variations of the estimates of the velocity vectors and the spatial variations of the time-varying image function. The system of nonlinear equations is defined in terms of the time-varying image function and its derivatives. The initial image functions are in general discontinuous and cannot be directly differentiated. These difficulties are overcome by treating the initial image functions as generalized functions and their derivatives as generalized derivatives. These generalized functions are evaluated (observed) on the parametric family of testing (smoothing) functions to obtain parametric families of secondary images, which are used in the system of nonlinear equations. The parameter specifies the degree of smoothness of each secondary image. The secondary images with progressively higher degrees of smoothness are sampled with progressively lower resolutions. Then coarse-to-fine control strategies are used to obtain the estimate.     

Constrained motion control using vector potential fields

Discusses the generation of a control signal that would instruct the actuators of a robotics manipulator to drive motion along a safe and well-behaved path to a desired target. The proposed concept of navigation control along with the tools necessary for its construction achieve this goal. The most significant tool is the artificial vector potential field which shows a better ability to steer motion than does a scalar potential field. The synthesis procedure emphasizes flexibility so that the effort needed to modify the control is commensurate with the change in the geometry of the workspace. Theoretical development along with simulation results are provided     

Facial feature detection using distance vector fields

A novel method for eye and mouth detection and eye center and mouth corner localization, based on geometrical information is presented in this paper. First, a face detector is applied to detect the facial region, and the edge map of this region is calculated. The distance vector field of the face is extracted by assigning to every facial image pixel a vector pointing to the closest edge pixel. The x and y components of these vectors are used to detect the eyes and mouth regions. Luminance information is used for eye center localization, after removing unwanted effects, such as specular highlights, whereas the hue channel of the lip area is used for the detection of the mouth corners. The proposed method has been tested on the XM2VTS and BioID databases, with very good results.     

Visualising two-dimensional vector fields using directed halftone cells

Digital halftoning is a technique for converting an image with multiple levels of grey into a bi-level (bitmap) image, typically in preparation for printing on paper. It is standard practice to optimize the halftoning process to reduce the visibility of artifacts that appear as textures within what should be a region of uniform or slowly varying intensity. This paper describes a method of manipulating the halftoning process to cause the texture to give an indication of field direction, while the field magnitude is displayed using the intensity. The method is very fast, and gives an unambiguous indication of direction everywhere in the field. It is suitable for displaying up to about 100×100 samples on a normal size page.     

Applications of vector fields to image processing

We use rotational and curvature properties of vector fields to identify critical features of an image. Using vector analysis and dif-ferential geometry, we establish the properties needed, and then use these properties in three ways. First, our results make it theoretically possible to identify extremal edges of an intensity function f(x, y) of two variables by considering the gradient vector field V = ?f. There is also enough information in ?f to find regions of high curvature (i.e., high curvature of the level paths of f). For color images, we use the vector field V = (I, Q). In application, the image is partitioned into a grid of squares. On the boundary of each square, V/|V| is sampled, and these unit vectors are used as the tangents of a curve ?. The rotation number (or topological degree) ?(?) and the average curvature f|??| are computed for each square. Analysis of these numbers yields infor-mation on edges and curvature. Experimental results from both simu-lated and real data are described.     

Multimodal estimation of discontinuous optical flow using Markovrandom fields

The estimation of dense velocity fields from image sequences is basically an ill-posed problem, primarily because the data only partially constrain the solution. It is rendered especially difficult by the presence of motion boundaries and occlusion regions which are not taken into account by standard regularization approaches. In this paper, the authors present a multimodal approach to the problem of motion estimation in which the computation of visual motion is based on several complementary constraints. It is shown that multiple constraints can provide more accurate flow estimation in a wide range of circumstances. The theoretical framework relies on Bayesian estimation associated with global statistical models, namely, Markov random fields. The constraints introduced here aim to address the following issues: optical flow estimation while preserving motion boundaries, processing of occlusion regions, fusion between gradient and feature-based motion constraint equations. Deterministic relaxation algorithms are used to merge information and to provide a solution to the maximum a posteriori estimation of the unknown dense motion field. The algorithm is well suited to a multiresolution implementation which brings an appreciable speed-up as well as a significant improvement of estimation when large displacements are present in the scene. Experiments on synthetic and real world image sequences are reported     

Visualization of vector fields using seed LIC and volume rendering

Line integral convolution (LIC) is a powerful texture-based technique for visualizing vector fields. Due to the high computational expense of generating 3D textures and the difficulties of effectively displaying the result, LIC has most commonly been used to depict vector fields in 2D or over a surface in 3D. We propose new methods for more effective volume visualization of three-dimensional vector fields using LIC: 1) we present a fast method for computing volume LIC textures that exploits the sparsity of the input texture. 2) We propose the use of a shading technique, called limb darkening, to reveal the depth relations among the field lines. The shading effect is obtained simply by using appropriate transfer functions and, therefore, avoids using expensive shading techniques. 3) We demonstrate how two-field visualization techniques can be used to enhance the visual information describing a vector field. The volume LIC textures are rendered using texture-based rendering techniques, which allows interactive exploration of a vector field.     

Curvature estimation scheme for triangle meshes using biquadratic Bézier patches

When dealing with triangle meshes, it is often important to compute curvature information for the purposes of feature recognition, segmentation, or shape analysis. Since a triangle mesh is a piecewise linear surface, curvature has to be estimated. Several different schemes have been proposed, both discrete and continuous, i.e. based on fitting surfaces locally. This paper compares commonly used discrete and continuous curvature estimation schemes. We also present a novel method which uses biquadratic Bézier patches as a local surface fitting technique.     

Robust face detection using local gradient patterns and evidence accumulation

This paper proposes a novel face detection method using local gradient patterns (LGP), in which each bit of the LGP is assigned the value one if the neighboring gradient of a given pixel is greater than the average of eight neighboring gradients, and 0 otherwise. LGP representation is insensitive to global intensity variations like the other representations such as local binary patterns (LBP) and modified census transform (MCT), and to local intensity variations along the edge components. We show that LGP has a higher discriminant power than LBP in both the difference between face histogram and non-face histogram and the detection error based on the face/face distance and face/non-face distance. We also reduce the false positive detection error greatly by accumulating evidences from multi-scale detection results with negligible extra computation time. In experiments using the MIT+CMU and FDDB databases, the proposed LGP-based face detection followed by evidence accumulation method provides a face detection rate that is 5–27% better than those of existing methods, and reduces the number of false positives greatly.     

Message estimation for universal steganalysis using multi-classification support vector machine

To prevent misusing of the steganography from the terrorists, effective steganalysis schemes which discriminate the stego-images from suspicious images are necessary. Some steganalysis methods can accurately estimate the length of embedded messages but they are only useful in the pre-defined condition. Active steganalysis methods are powerful in length estimation such as regular singular (RS) and sample pairs analysis (SPA) steganalysis schemes, but they would become invalid in frequency domain. Passive steganalysis methods may discriminate stego-images from suspicious images in spatial and frequency domains such as Lyu and Fraid's steganalysis scheme, but they could not estimate the length of hidden messages. Although length estimation has been discussed in the active steganalysis methods for a while, it is a novel study in passive steganalysis method. We improve the Lyu and Fraid's universal steganalysis scheme and design an efficient length estimation policy in passive steganalysis methods. Experimental results demonstrate the efficiency and practicability of the proposed universal steganalysis scheme.     

Drill flank wear estimation using supervised vector quantization neural networks

Drill wear detection and prognosis is one of the most important considerations in reducing the cost of rework and scrap and to optimize tool utilization in hole making industry. This study presents the development and implementation of two supervised vector quantization neural networks for estimating the flank-land wear size of a twist drill. The two algorithms are; the learning vector quantization (LVQ) and the fuzzy learning vector quantization (FLVQ). The input features to the neural networks were extracted from the vibration signals using power spectral analysis and continuous wavelet transform techniques. Training and testing were performed under a variety of speeds and feeds in the dry drilling of steel plates. It was found that the FLVQ is more efficient in assessing the flank wear size than the LVQ. The experimental procedure for acquiring vibration data and extracting features in the time-frequency domain using the wavelet transform is detailed. Experimental results demonstrated that the proposed neural network algorithms were effective in estimating the size of the drill flank wear.     

Image variants generated by using vector models of multidimensional random fields

The author proposes vector models of multidimensional random fields to obtain the image variants for an object under observation, which differ in color and geometric distortions simulating the reflection of the interaction between the observed object and the environment.