Hongbin Jin

Localizing edges for estimating point spread function by removing outlier points

This paper presents an approach to detect sharp edges for estimating point spread function (PSF) of a lens. A category of PSF estimation methods detect sharp edges from low-resolution (LR) images and estimate PSF with the detected edges. Existing techniques usually rely on accurate detection of ending points of the profile normal to an edge. In practice, however, it is often very difficult to localize profiles accurately. Inaccurately localized profiles generate a poor PSF estimation. We employ the Random Sample Consensus (RANSAC) algorithm to rule out outlier points. In RANSAC, prior knowledge about a pattern shape is incorporated, and the edge points lying far away from the pattern shape will be removed. The proposed method is tested on images of saddle patterns. Experimental results show that the proposed method can robustly localize sharp edges from LR saddle pattern images and yield accurate PSF estimation.

Speed-up keypoint mapping technique by multi-resolution and global information

This work deals with the problem of high computation complexity in image registration. A hierarchical multiresolution strategy is utilized to speed up the processing of SIFT by starting on a low resolution octave. The initial affine transformation model will be achieved. In subsequent multiresolution octaves, we apply the transformation affine model getting from upper octave to current octave, then, combined with geometrical distribution of matched keypoints to further remove incorrect mappings and update affine transformation model. The strategy ends with the best affine transformation model on the bottom octave(full-size image). Experimental results show that the proposed method can achieve comparative accuracy with less computational than original SIFT

Robustly building keypoint mappings with global information on multispectral images

This paper proposes an approach to robustly build keypoint mappings on multispectral images. The distinctiveness and repeatability of descriptors often decrease significantly on multispectral images and thus give unreliable keypoint mappings. To complement this decrease, global information over entire images is induced in this work to evaluate keypoint mappings. Initial keypoint mappings are established by utilizing descriptors. A pair of keypoint mappings determines a similarity transformation T, and then it is evaluated with the induced global information that is defined to be the similarity metric between the reference image and the transformed image by T. A process is utilized that iteratively considers the pairs of keypoint mappings and searches the best reference matched keypoint for every test keypoint. Experimental results show that the proposed approach can provide more reliable keypoint mappings than SIFT, ORB, FREAK, and ISS on multispectral images.

Building Keypoint Mappings on Multispectral Images by a Cascade of Classifiers with a Resurrection Mechanism

Inspired by the boosting technique for detecting objects, this paper proposes a cascade structure with a resurrection mechanism to establish keypoint mappings on multispectral images. The cascade structure is composed of four steps by utilizing best bin first (BBF), color and intensity distribution of segment (CIDS), global information and the RANSAC process to remove outlier keypoint matchings. Initial keypoint mappings are built with the descriptors associated with keypoints; then, at each step, only a small number of keypoint mappings of a high confidence are classified to be incorrect. The unclassified keypoint mappings will be passed on to subsequent steps for determining whether they are correct. Due to the drawback of a classification rule, some correct keypoint mappings may be misclassified as incorrect at a step. Observing this, we design a resurrection mechanism, so that they will be reconsidered and evaluated by the rules utilized in subsequent steps. Experimental results show that the proposed cascade structure combined with the resurrection mechanism can effectively build more reliable keypoint mappings on multispectral images than existing methods.

Improving matching ability of descriptors on multi-sensor images with complementary information

This paper proposes an approach to reliably matching keypoints on multi-sensor images. Keypoint matching techniques have been successfully applied to a wide range of fields, but in most cases their success is limited to single-mode images that are acquired by the same type of sensor, e.g., both images are Landsat TIRS. The common information between multi-sensor images is typically much less than that between single-sensor images, which compromises the matching ability of descriptors and thus causes keypoint mismatchings. Observing this, we propose utilizing the edge information outside of the window for computing descriptors to improve the matching performance of descriptors. The edge information is obtained over entire images and hence encodes a global descriptor that is complementary to the local descriptor. Experimental results show that the correct rate of keypoint matchings increase on multi-sensor images.

Building reliable keypoint matches by a cascade of classifiers with resurrection mechanism

This paper presents a cascade of classifiers with “resurrection” mechanism for building reliable keypoint matches. It is likely to cause that correct keypoint mappings are removed because of too strict regulation in many existing solutions of image registration. To avoid this situation and get accuracy result, a cascade framework with multi-steps is proposed to remove the incorrect keypoint mappings. To further reduce the rate of misjudgment to correct mappings in each step, we introduce “resurrection” in a cascade structure. Keypoint mappings are initially built with their associated descriptors, and then in each step part of keypoint mappings are determined to be incorrect and deleted completely. Meanwhile, some mappings which perform relatively poor are undetermined and their fate will be decided in next step under their performance. By this means, we use multi-steps efficiently and reduce misjudgment to correct mappings. Experimental results show that the presented cascade structure can robustly remove the outlier keypoint mappings and achieve accurate image registration.