Orthogonal Gaussian-Hermite Moments Research Articles

Rotation invariants of vector fields from orthogonal moments

Vector field images are a type of new multidimensional data that appear in many engineering areas. Although the vector fields can be visualized as images, they differ from graylevel and color images in several aspects. To analyze them, special methods and algorithms must be originally developed or substantially adapted from the traditional image processing area. In this paper, we propose a method for the description and matching of vector field patterns under an unknown rotation of the field. Rotation of a vector field is so-called total rotation, where the action is applied not only on the spatial coordinates but also on the field values. Invariants of vector fields with respect to total rotation constructed from orthogonal Gaussian–Hermite moments and Zernike moments are introduced. Their numerical stability is shown to be better than that of the invariants published so far. We demonstrate their usefulness in a real world template matching application of rotated vector fields.

Differential components of discriminative 2D Gaussian–Hermite moments for recognition of facial expressions

This paper deals with a new expression recognition method by representing facial images in terms of higher-order two-dimensional orthogonal Gaussian–Hermite moments (GHMs) and their geometric invariants. Only the moments having high discrimination power are selected as a set of features for expressions. To obtain the differentially expressive components of the moments, the discriminative GHMs are projected on to a new expression-invariant subspace using the correlations among the neutral faces. Features obtained from the discriminative moments and differentially expressive components of the moments are used to recognize an expression using the well-known support vector machine classifier. Experimental results presented are obtained from commonly-referred databases such as the CK-AUC, FRGC, and MMI that have posed or spontaneous expressions as well as the GENKI database that has expressions in-the-wild. Experiments on mutually exclusive subjects reveal that the performance of expression recognition of the proposed method is significantly better than that of the existing or similar methods, which use the local or patch-based high dimensional binary patterns, directional number patterns generated from derivatives of Gaussian, Gabor- or other moment-based features.

3D rotation invariants of Gaussian–Hermite moments

3D rotation invariants based on orthogonal Gaussian–Hermite moments are proposed in this paper. We present an elegant and easy theoretical derivation of them. At the same time we prove by experiments that the Gaussian–Hermite invariants have better numerical stability than the traditional invariants composed of geometric moments.

Non-interactive automatic video segmentation of moving targets

Extracting moving targets from video accurately is of great significance in the field of intelligent transport. To some extent, it is related to video segmentation or matting. In this paper, we propose a non-interactive automatic segmentation method for extracting moving targets. First, the motion knowledge in video is detected with orthogonal Gaussian-Hermite moments and the Otsu algorithm, and the knowledge is treated as foreground seeds. Second, the background seeds are generated with distance transformation based on foreground seeds. Third, the foreground and background seeds are treated as extra constraints, and then a mask is generated using graph cuts methods or closed-form solutions. Comparison showed that the closed-form solution based on soft segmentation has a better performance and that the extra constraint has a larger impact on the result than other parameters. Experiments demonstrated that the proposed method can effectively extract moving targets from video in real time.

Automatic non-interactive matting of dynamic targets in complex video using Graph Cuts

Extracting moving targets from video automatically and accurately is of great significance in the field of intelligent transport. This paper is devoted to the video object matting problem which is based on solving an optimization problem. The matting problem is severely ill-posed and generally cannot be solved without any extra constraints. In contrast to previous efforts, we develop a new method of automatic non-interactive matting of dynamic objects in complex video. Combining the Orthogonal Gaussian–Hermite Moments algorithm and Graph Cuts, highly stabile scribbles become automatic generation without interaction in the method and are used as the extra constraints in calculating the minimum energy function. The real-time of moving object matting may be improved obviously. Experiments demonstrate that the proposed method can effectively extract moving targets from video in a real-time way. And the method can be widely applied and suited to the extraction of complex video images due to the robustness of these scribbles.

DETECTING MOVING OBJECTS USING FUZZY INTEGRATION MOMENT

Moments are widely used in pattern recognition, image processing, computer vision and multi-resolution analysis. It can also be use to detect the moving objects. In this paper, we address the problems about detecting moving objects in a video stream obtained by a fixed camera. To detect the moving objects, a method is proposed using the fuzzy integrating moment that combines the 1st, 3rd and 5th order temporal orthogonal Gaussian-Hermite moments and it takes into account the non-symmetric membership function π. To evaluate the performances of moving detection, the performance comparisons of different methods are carried out. The experiment results show good performance of our method for detecting moving objects.

Traffic object detections and its action analysis

In this paper, we propose a new method to detect the moving objects using the orthogonal Gaussian–Hermite moments (OGHM). For the segmentation of moving objects in the moment images, we propose a fuzzy relaxation method (FRM) and 3D morphological relaxation method (3DMRM). Finally, the trajectories, speeds and accelerations of moving objects are analyzed to determine their moving direction. The experimental results show good performance of our methods in the noise scene for detecting the motion objects.

Properties of Orthogonal Gaussian-Hermite Moments and Their Applications

Moments are widely used in pattern recognition, image processing, and computer vision and multiresolution analysis. In this paper, we first point out some properties of the orthogonal Gaussian-Hermite moments, and propose a new method to detect the moving objects by using the orthogonal Gaussian-Hermite moments. The experiment results are reported, which show the good performance of our method.

ORTHOGONAL MOMENTS AND THEIR APPLICATION TO MOTION DETECTION IN IMAGE SEQUENCES

Motion detection is one of the most important subjects in modern information acquisition systems for dynamic scenes. In this paper, we address motion detection in image sequences by the use of smoothed orthogonal Gaussian-Hermite moments. After introducing the orthogonal moments, with their fast calculations and the analysis on their behavior, we present a new method of motion detection in image sequences by the use of temporal Gaussian-Hermite moments. Experimental results are presented and are compared with the other methods, which shows the good performance of smoothed orthogonal Gaussian-Hermite moments for motion detection.