An Efficient Globally Optimal Algorithm for Asymmetric Point Matching

Point Clouds Matching Based on Discrete Optimal Transport

Matching is an important prerequisite for point clouds registration, which is to establish a reliable correspondence between two point clouds. This paper aims to improve recent theoretical and algorithmic results on discrete optimal transport (DOT), since it lacks robustness for the point clouds matching problems with large-scale affine or even nonlinear transformation. We first consider the importance of the used prior probability for accurate matching and give some theoretical analysis. Then, to solve the point clouds matching problems with complex deformation and noise, we propose an improved DOT model, which introduces an orthogonal matrix and a diagonal matrix into the classical DOT model. To enhance its capability of dealing with cases with outliers, we further bring forward a relaxed and regularized DOT model. Meantime, we propose two algorithms to solve the brought forward two models. Finally, extensive experiments on some real datasets are designed in the presence of reflection, large-scale rotation, stretch, noise, and outliers. Some state-of-the-art methods, including CPD, APM, RANSAC, TPS-ICP, TPS-RPM, RPMNet, and classical DOT methods, are to be discussed and compared. For different levels of degradation, the numerical results demonstrate that the proposed methods perform more favorably and robustly than the other methods.

Image Matching from Handcrafted to Deep Features: A Survey

As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions about which method would be a suitable choice for specific applications with respect to different scenarios and task requirements and how to design better image matching methods with superior performance in accuracy, robustness and efficiency. This encourages us to conduct a comprehensive and systematic review and analysis for those classical and latest techniques. Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the development of these methods in theory and practice. Secondly, we briefly introduce several typical image matching-based applications for a comprehensive understanding of the significance of image matching. In addition, we also provide a comprehensive and objective comparison of these classical and latest techniques through extensive experiments on representative datasets. Finally, we conclude with the current status of image matching technologies and deliver insightful discussions and prospects for future works. This survey can serve as a reference for (but not limited to) researchers and engineers in image matching and related fields.

Efficient Similarity Point Set Registration by Transformation Decomposition

Point set registration is one of the basic problems in computer vision. When the overlap ratio between point sets is small or the relative transformation is large, local methods cannot guarantee the accuracy. However, the time complexity of the branch and bound (BnB) optimization used in most existing global methods is exponential in the dimensionality of parameter space. Therefore, seven-Degrees of Freedom (7-DoF) similarity transformation is a big challenge for BnB. In this paper, a novel rotation and scale invariant feature is introduced to decouple the optimization of translation, rotation, and scale in similarity point set registration, so that BnB optimization can be done in two lower dimensional spaces. With the transformation decomposition, the translation is first estimated and then the rotation is optimized by maximizing a robust objective function defined on consensus set. Finally, the scale is estimated according to the potential correspondences in the obtained consensus set. Experiments on synthetic data and clinical data show that our method is approximately two orders of magnitude faster than the state-of-the-art global method and more accurate than a typical local method. When the outlier ratio with respect to the inliers is up to 1.0, our method still achieves accurate registration.

Learning Sparse Representation for Objective Image Retargeting Quality Assessment

The goal of image retargeting is to adapt source images to target displays with different sizes and aspect ratios. Different retargeting operators create different retargeted images, and a key problem is to evaluate the performance of each retargeting operator. Subjective evaluation is most reliable, but it is cumbersome and labor-consuming, and more importantly, it is hard to be embedded into online optimization systems. This paper focuses on exploring the effectiveness of sparse representation for objective image retargeting quality assessment. The principle idea is to extract distortion sensitive features from one image (e.g., retargeted image) and further investigate how many of these features are preserved or changed in another one (e.g., source image) to measure the perceptual similarity between them. To create a compact and robust feature representation, we learn two overcomplete dictionaries to represent the distortion sensitive features of an image. Features including local geometric structure and global context information are both addressed in the proposed framework. The intrinsic discriminative power of sparse representation is then exploited to measure the similarity between the source and retargeted images. Finally, individual quality scores are fused into an overall quality by a typical regression method. Experimental results on several databases have demonstrated the superiority of the proposed method.