Xinting Gao

Image enhancement based on a nonlinear multiscale method using dual-tree complex wavelet transform

This paper proposes a new nonlinear multiscale reconstruction method for image enhancement using dual-tree complex wavelet transform. The enhanced image is obtained by successively combining each coarser scale image with the corresponding nonlinearly modified interscale (detail) images. This image enhancement method reduces additive noise while preserves the sharpness of the image. Simulation results show the improved performance of our image enhancement method in comparison with that of the existing methods.

Scale-space Based Corner Detection of Gray Level Images Using Plessey Operator

This paper proposes a multiscale corner detection method for gray level images based on scale-space theory and Plessey operator. The proposed method solves three problems existing in the original Plessey detector. First, it works in the scale-space domain, so it detects corners belonging to different scales instead of a certain scale. Second, only one parameter needs to be set instead of three parameters needed in the original Plessey method. Third, delocalization is a well-known inherent drawback of the Plessey corner operator and it will increase with the scale at which it operates. The proposed algorithm solves the problem by detecting the corners from small scale to large scale, then track back from large scale to small scale. As the delocalization in the smallest scale can be ignored, the proposed method obtain the accurate localization. This proposed multiscale scheme can also be applied to other spatial corner detectors to improve their performances. The simulation results and the application in stereo matching show the improved performance of the proposed method compared with the original Plessey detector and the SUSAN detector

A feature-based matching scheme: MPCD and robust matching strategy

This paper presents a scheme that matches interest point features detected on two images taken from different points of view. To accomplish this objective, we jointly consider the corner detection and matching problems. Firstly, a new multi-scale Plessey corner detector (MPCD) is used to detect the interest points. Secondly, the geometric constraint between two images is exploited, based on which we propose a new energy function that can approximate 2D affine transformation in a more efficient way. Only a small set of corners with the highest accuracy and robustness are considered in the first stage, consequently, a small data space is provided to the robust algorithm in the second stage reducing the computation time. Therefore, more information can be incorporated into our scheme based on corner detection and matching phases. We compare our method using the proposed MPCD with two standard corner detectors based on image matching. We also evaluate our proposed matching strategy against Zhengyous method [Deriche, R., Zhang, Z., Luong, Q.-T., Faugeras, O., 1994. Robust recovery of the epipolar geometry for an uncalibrated stereo rig. In: European Conference on Computer Vision, Stockholm, Sweden, pp. 567-576]. Our scheme provides a new viewpoint and better results for the traditional feature matching problem.

Corner detection of contour images using continuous wavelet transform

This paper presents a multiscale corner detection method based on continuous wavelet transform (CWT) of contour images. The corner points are detected from the local wavelet transform modulus maxima (WTMM) of the contour orientation. To reduce the side effects of the discretization and smoothing that are introduced by the preprocessing steps, we adopt a simple but efficient post processing algorithm: nonmaximum suppression. The proposed method detects sharp corners as well as subtle corners. Simulation results illustrate the better performance of the proposed corner detector compared to the other methods.

Local Natural Scale Based Contour Corner Detection Using Wavelet Transform

A new corner detection method for contour images is proposed based on dyadic wavelet transform (WT). The wavelet transform modulus maxima (WTMM) at different scales are taken as corner candidates. For each candidate, the scale at which the maximum value of the WTMM exists is defined as its local natural scale, and the corresponding modulus is taken as the significance measurement. This approach achieves more accurate estimations of the natural scale than the existing global natural scale methods. The simulation results show that the proposed method is effective for both long contours and short contours. The objective evaluation reveals the better performance of the proposed algorithm compared to the existing methods. The technique is inherently fast due to the fast implementations of the dyadic WT computations

A robust feature-based matching of two uncalibrated images

This paper presents a method that matches interest point features detected on two images taken from different view points. A new multi-scale Plessey corner detector (MPCD) is used to detect the interest points. The geometric constraint between two images is exploited as in [R. Deriche, et al., 1994]: the fundamental matrix is derived using least median of squares (LMedS) from an initial set of matches, and then it is used to guide new matches. However, we propose a new energy function that can approximate affine transformation in a more effective way. Then, the initial set of matches is derived by minimizing the energy function. As a result, our method can perform well even when the pose variation is large between the two images. We compare our method using the proposed MPCD against two standard corner detectors on image matching. Also we evaluate our proposed matching criterion against Zhangs. Our method gave better results in both experiments on real face images.

Multiscale corner detection for gray level images using Plessey method

This paper proposes an improved Plessey corner detection method for gray level images using multiscale analysis. Plessey corner detector is well known for its good performance. But, as we understand, Plessey corner detection method has three drawbacks. Firstly, it works only in the spatial domain, so it can only detect corners belonging to a specific scale. On the other hand, the proposed detector works in the scale-space domain, thus, it can detect the corner points belonging to different scales. Secondly, three parameters are needed to be set manually in the original Plessey method, while the proposed method requires to set only one parameter. Thirdly, the delocalization is a well-known inherent problem for the Plessey corner operator. And it can be increased with the scale at which it operates. The proposed algorithm partially solves this problem by detecting the corners from smaller scale to larger scales. Our proposed multiscale scheme can also be applied to other spatial corner detectors to improve their performances. Better simulation results are shown and compared with the original Plessey and SUSAN corner detectors.