Bio: Weidong Zhao is an academic researcher from Tongji University. The author has contributed to research in topics: Point set registration & Image segmentation. The author has an hindex of 11, co-authored 45 publications receiving 531 citations.
Papers published on a yearly basis
TL;DR: A novel supervised learning method, called Sparsity Preserving Discriminant Projections (SPDP), which attempts to preserve the sparse representation structure of the data and maximize the between-class separability simultaneously, can be regarded as a combiner of manifold learning and sparse representation.
Abstract: Dimensionality reduction is extremely important for understanding the intrinsic structure hidden in high-dimensional data. In recent years, sparse representation models have been widely used in dimensionality reduction. In this paper, a novel supervised learning method, called Sparsity Preserving Discriminant Projections (SPDP), is proposed. SPDP, which attempts to preserve the sparse representation structure of the data and maximize the between-class separability simultaneously, can be regarded as a combiner of manifold learning and sparse representation. Specifically, SPDP first creates a concatenated dictionary by classwise PCA decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least square method. Secondly, a local between-class separability function is defined to characterize the scatter of the samples in the different submanifolds. Then, SPDP integrates the learned sparse representation information with the local between-class relationship to construct a discriminant function. Finally, the proposed method is transformed into a generalized eigenvalue problem. Extensive experimental results on several popular face databases demonstrate the feasibility and effectiveness of the proposed approach.
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: 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: A semi-supervised approach for liver segmentation from computed tomography (CT) scans, which is based on the graph cut model integrated with domain knowledge, which shows effectiveness and efficiency.
••23 May 2009
TL;DR: The security theory of text watermarking is proposed in this paper, and the following security topics are discussed: (i) the classification and application of textWatermarking; (ii) the classified and analysis of attacks; (iii) the water marking model and security countermeasures.
Abstract: Security issues of text watermarking are greatly different from those of other multimedia watermarking, in terms of its specific requirements and characteristics of text watermarking. The security theory of text watermarking is proposed in this paper, and the following security topics are discussed: (i) the classification and application of text watermarking; (ii) the classification and analysis of attacks; (iii) the watermarking model and security countermeasures. Other open issues and further challenges related to text watermarking are also addressed.
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
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.
01 Jan 1999
TL;DR: Several mechanisms for marking documents and several other mechanisms for decoding the marks after documents have been subjected to common types of distortion are described and compared.
Abstract: Each copy of a text document can be made different in a nearly invisible way by repositioning or modifying the appearance of different elements of text, i.e., lines, words, or characters. A unique copy can be registered with its recipient, so that subsequent unauthorized copies that are retrieved can be traced back to the original owner. In this paper we describe and compare several mechanisms for marking documents and several other mechanisms for decoding the marks after documents have been subjected to common types of distortion. The marks are intended to protect documents of limited value that are owned by individuals who would rather possess a legal than an illegal copy if they can be distinguished. We will describe attacks that remove the marks and countermeasures to those attacks. An architecture is described for distributing a large number of copies without burdening the publisher with creating and transmitting the unique documents. The architecture also allows the publisher to determine the identity of a recipient who has illegally redistributed the document, without compromising the privacy of individuals who are not operating illegally. Two experimental systems are described. One was used to distribute an issue of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, and the second was used to mark copies of company private memoranda.
TL;DR: A literature review of 190 application papers, published between 2004 and 2016, by classifying them on the basis of the area of application, the identified theme, the year of publication, and so forth, shows that FAHP is used primarily in the Manufacturing, Industry and Government sectors.
Abstract: A state-of the-art survey of FAHP applications is carried out: 190 papers are reviewedPapers are classified based on their: Application area, Theme, Year, Country, etc.Review is summarized in tabular formats/charts to help readers extract quick info.Results and Findings are made available through an online (free) testbedThe testbed makes fuzzy pairwise comparison matrices (from all papers) available As a practical popular methodology for dealing with fuzziness and uncertainty in Multiple Criteria Decision-Making (MCDM), Fuzzy AHP (FAHP) has been applied to a wide range of applications. As of the time of writing there is no state of the art survey of FAHP, we carry out a literature review of 190 application papers (i.e., applied research papers), published between 2004 and 2016, by classifying them on the basis of the area of application, the identified theme, the year of publication, and so forth. The identified themes and application areas have been chosen based upon the latest state-of-the-art survey of AHP conducted by Vaidya, O., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research, 169(1), 1-29.. To help readers extract quick and meaningful information, the reviewed papers are summarized in various tabular formats and charts. Unlike previous literature surveys, results and findings are made available through an online (and free) testbed, which can serve as a ready reference for those who wish to apply, modify or extend FAHP in various applications areas. This online testbed makes also available one or more fuzzy pairwise comparison matrices (FPCMs) from all the reviewed papers (255źmatrices in total).In terms of results and findings, this survey shows that: (i) FAHP is used primarily in the Manufacturing, Industry and Government sectors; (ii) Asia is the torchbearer in this field, where FAHP is mostly applied in the theme areas of Selection and Evaluation; (iii) a significant amount of research papers (43% of the reviewed literature) combine FAHP with other tools, particularly with TOPSIS, QFD and ANP (AHP's variant); (iv) Chang's extent analysis method, which is used for FPCMs' weight derivation in FAHP, is still the most popular method in spite of a number of criticisms in recent years (considered in 57% of the reviewed literature).