Bio: Yufei Chen is an academic researcher from Tongji University. The author has contributed to research in topics: Image segmentation & Point set registration. The author has an hindex of 14, co-authored 69 publications receiving 665 citations.
TL;DR: A registration framework based on speed up robust feature (SURF) detector, PIIFD and robust point matching, called SURF–PIIFD–RPM, which outperforms existing algorithms, and it is quite robust to outliers.
TL;DR: This work extends traditional neighborhood coverings to fuzzy ones and proposes a Three-Way Classification method with Fuzzy Neighborhood Covering (3WC-FNC), which is effective to handle the uncertain data and reduces the classification risk.
TL;DR: Abundant experiments demonstrate that the proposed three-way decision support method is effective to handle the uncertain medical cases, and in the meantime achieves precise classification of FLLs to support liver cancer diagnosis.
Abstract: Malignant Focal Liver Lesion (FLL) is a main cause of primary liver cancer. In most existing Computer-Aided Diagnosis (CAD) systems of FLLs, machine learning and data mining methods have been widely applied to classify liver CT images for diagnostic decision making. However, these strategies of automatic decision support depend on data-driven classification methods and may lead to risky diagnosis on uncertain medical cases. To tackle the drawback, we expect to integrate the objective judgments from classification algorithms and the subjective judgments from human expert experiences, and propose a data-human-driven Three-way Decision Support for FLL diagnosis. The methodology of three-way decision support is motivated by Three-way Decision (3WD) theory. It tri-partitions the FLL medical records into certain benign, certain malignant and uncertain cases. The certain cases are automatically classified by decision rules and the challenging uncertain cases will be carefully diagnosed by human experts. Therefore, the method of three-way decision support can balance well the risk and efficiency of decision making. The workflow of three-way decision support for FLL diagnosis includes the stages of semantic feature extraction, three-way rule mining and decision cost optimization. Abundant experiments demonstrate that the proposed three-way decision support method is effective to handle the uncertain medical cases, and in the meantime achieves precise classification of FLLs to support liver cancer diagnosis.
TL;DR: Experimental results demonstrate that the novel robust method for non-rigid point set registration is robust to a large degree of degradations, and it outperforms several state-of-the-art methods in most tested scenarios.
TL;DR: The reduction process preserves lower and upper approximations of neighborhood covering and thereby provides a flexible way to handle uncertain samples and noise and achieves precise and stable results on noisy data.
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
01 Jan 2002
TL;DR: This survey introduces feature detection, description, and matching techniques from handcrafted methods to trainable ones and provides an analysis of the development of these methods in theory and practice, and briefly introduces several typical image matching-based applications.
Abstract: 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.
TL;DR: The authors' method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds, and achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.
Abstract: Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative image feature correspondences. To achieve the goal, an efficient approach, termed as locality preserving matching (LPM), is designed, the principle of which is to maintain the local neighborhood structures of those potential true matches. We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities. Our method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. To demonstrate the generality of our strategy for handling image matching problems, extensive experiments on various real image pairs for general feature matching, as well as for point set registration, visual homing and near-duplicate image retrieval are conducted. Compared with other state-of-the-art alternatives, our LPM achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.