查看更多>>摘要：Studying on the microstructural evolution of cement paste during hydration is of considerable significance for understanding its mechanism and designing such material in cement industry. With the use of microtomography and image registration, the four-dimensional (4D) microstructure of cement paste can be captured, thereby assisting material scientists in studying the hydration process in situ . However, as a challenging task, the construction of high-quality 4D microstructural image is remarkably impeded by image size, isotropy, and homogeneity. This paper proposes an image processing framework to construct 4D high quality microstructural image rapidly for cement hydration. This framework improves and accelerates microstructural image registration and enhancement by using bias field correction, temporal intensity calibration and fast image registration. Additionally, a partial information registration method adopting partial information on the spatial and phased scales, is proposed to improve the registration speed and accuracy. Furthermore, a multi-factor multi-layer particle swarm optimization is proposed to improve the optimization in registration. Experimental results indicate that the 4D high quality microstructural image can be constructed rapidly with promising precision. (c) 2021 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Given the descriptions of classes, Zero-Shot Learning (ZSL) aims to recognize unseen samples by learning a projection between the visual features of samples and the semantic descriptions (prototypes) of classes from seen data. However, due to the inherent distribution gap between seen and unseen domains, the learned projection is generally biased to seen classes and may produce misleading relationships between unseen samples and prototypes (sample-prototype relationship). To tackle this problem, we propose a Cluster-Prototype Matching (CPM) framework which exploits the distribution information of samples to explore the cluster structure of samples and then use the robust cluster-prototype relationship to correct the biased sample-prototype relationship. Specifically, we first use an iterative cluster generation mod-ule to identify the underlying cluster structure of samples based on their embedding features, which are acquired via a basic ZSL model. Then each identified cluster will be matched with a specific class pro-totype through the Kuhn-Munkres algorithm, based on which we can export a sharp cluster-prototype similarity. Finally, the cluster-prototype similarity is combined with the sample-prototype similarity to determine the class labels of test samples. We apply CPM to five well-established ZSL methods and the experimental results show that CPM can significantly improve the performance of basic models and en-able them achieve or beyond the state-of-the-art. (c) 2021 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Most existing methods for time series clustering rely on distances calculated from the entire raw data using the Euclidean distance or Dynamic Time Warping distance. In this work, we propose to embed the time series onto higher-dimensional spaces to obtain geometric representations of the time series themselves. Particularly, the embedding on R-nxp, on the Stiefel manifold and on the unit Sphere are analyzed for their performances with respect to several yet well-known clustering algorithms. The gain brought by the geometrical representation for the time series clustering is illustrated through a large benchmark of databases. We particularly exhibit that, firstly, the embedding of the time series on higher dimensional spaces gives better results than classical approaches and, secondly, that the embedding on the Stiefel manifold in conjunction with UMAP and HDBSCAN clustering algorithms is the recommended framework for time series clustering.& nbsp; (C) 2021 Elsevier Ltd. All rights reserved.
查看更多>>摘要：The chips contained in any electronic device are manufactured over circular silicon wafers, which are monitored by inspection machines at different production stages. Inspection machines detect and locate any defect within the wafer and return a Wafer Defect Map (WDM), i.e., a list of the coordinates where defects lie, which can be considered a huge, sparse, and binary image. In normal conditions, wafers exhibit a small number of randomly distributed defects, while defects grouped in specific patterns might indicate known or novel categories of failures in the production line. Needless to say, a primary concern of semiconductor industries is to identify these patterns and intervene as soon as possible to restore normal production conditions. Here we address WDM monitoring as an open-set recognition problem, where the aim is to classify WDM in known categories and promptly detect novel patterns. In particular, we propose a comprehensive pipeline for wafer monitoring based on a Submanifold Sparse Convolutional Network, a deep architecture designed to process sparse data at an arbitrary resolution, which is trained on the known classes. To detect novelties, we define an outlier detector based on a Gaussian Mixture Model fitted on the latent representation of the classifier. Our experiments on a real dataset of WDMs show that directly processing full-resolution WDMs by Submanifold Sparse Convolutions yields superior classification performance on known classes than traditional Convolutional Neural Networks, which require a preliminary binning to reduce the size of the binary images representing WDMs. Moreover, our solution outperforms state-ofthe-art open-set recognition solutions in novelty detection. (c) 2021 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Food computing has recently attracted considerable research attention due to its significance for health risk analysis. In the literature, the majority of research effort s are dedicated to food recognition. Rela-tively few works are conducted for food counting and segmentation, which are essential for portion size estimation. This paper presents a deep neural network, named SibNet, for simultaneous counting and extraction of food instances from an image. The problem is challenging due to varying size and shape of food as well as arbitrary viewing angle of camera, not to mention that food instances often occlude each other. SibNet is novel for proposal of learning seed map to minimize the overlap between instances. The map facilitates counting and can be completed as an instance segmentation map that depicts the arbitrary shape and size of individual instance under occlusion. To this end, a novel sibling relation sub-network is proposed for pixel connectivity analysis. Along with this paper, three new datasets covering Western, Chinese and Japanese food are also constructed for performance evaluation. The three datasets and SibNet source code are publicly available. (c) 2021 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Classifier ensemble pruning is a strategy through which a subensemble can be identified via optimizing a predefined performance criterion. Choosing the optimum or suboptimum subensemble decreases the initial ensemble size and increases its predictive performance. In this article, a set of heuristic metrics will be analyzed to guide the pruning process. The analyzed metrics are based on modifying the order of the classifiers in the bagging algorithm, with selecting the first set in the queue. Some of these criteria include general accuracy, the complementarity of decisions, ensemble diversity, the margin of samples, minimum redundancy, discriminant classifiers, and margin hybrid diversity. The efficacy of those metrics is affected by the original ensemble size, the required subensemble size, the kind of individual classifiers, and the number of classes. While the efficiency is measured in terms of the computational cost and the memory space requirements. The performance of those metrics is assessed over fifteen binary and fifteen multiclass benchmark classification tasks, respectively. In addition, the behavior of those metrics against randomness is measured in terms of the distribution of their accuracy around the median. Results show that ordered aggregation is an efficient strategy to generate subensembles that improve both predictive performance as well as computational and memory complexities of the whole bagging ensemble. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
查看更多>>摘要：It is time to stop neglecting the text around your world. In VQA, the surrounding text helps humans to understand complete visual scenes and reason question semantics efficiently. Here, we address the chal-lenging Text-based Visual Question Answering (TextVQA) problem, which requires a model to answer the VQA questions with text reading ability. Existing TextVQA methods mainly focus on the latent relation-ships between detected object instances and scene texts with the given question, but ignore spatial loca-tion relationships and complex relational semantics between visual object instances and OCR texts (e.g. the A of B on C). To deal with these challenges, we propose a novel Text-Instance Graph (TIG) network for TextVQA. The TIG builds an OCR-OBJ graph for overlapping relationships modeling, where each node of graph is updated by utilizing relative objects or OCR texts. To deal with the question with complex logic, we propose a dynamic OCR-OBJ graph network to extend the perception space of graph nodes, which grasps the information of non-directly adjacent node features. Considering a scene about "the brand of the computer on the table", the model would build correlations between "brand" and "table" using "the computer" node as the intermediate node. Extensive experiments on three benchmarks demonstrate the effectiveness and superiority of the proposed method. In addition, our TIG achieves 0.505 ANLS on ST-VQA challenge leaderboard and sets a new state-of-the-art. (c) 2021 Published by Elsevier Ltd.
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