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Abstract A great deal of text and geographical information is provided in the geo-tagged social media data, which offers unprecedented opportunities to get insights into the social behaviors across different local areas. With the increasing size of geo-tagged social media data, a large number of visual mapping elements overlap with each other, which makes it difficult to visually capture topics of interest as well as their spatial distribution. In this paper, we propose a visual abstraction fram...
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#1Yunlong Huang (SCUT: South China University of Technology)
#2Zenghui Sun (SCUT: South China University of Technology)H-Index: 5
Last.Canjie Luo (SCUT: South China University of Technology)H-Index: 3
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Abstract For most previous attention-based scene text recognition methods, images are transformed into high-level feature vectors that form a feature map with height equal to one. Such vectors may contain unnecessary noise that limits recognition performance. To address this issue, in this paper, we propose the effective parts attention network (EPAN) which can attentively highlight the character region for more precise recognition. EPAN consists of a text image encoder and character effective p...
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#1Lipeng Xie (University of Electronic Science and Technology of China)
#2Jin Qi (University of Electronic Science and Technology of China)H-Index: 1
Last.Samad Wali (University of Electronic Science and Technology of China)
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Abstract Nuclei segmentation in histopathology images plays a crucial role in the morphological quantitative analysis of tissue structure and has become a hot research topic. Though numerous efforts have been tried in this research area, the overlapping and touching nuclei segmentation remains a challenging problem. In this paper, we present a novel and effective instance segmentation method for tackling this challenge by integrating Deep Convolutional Neural Networks with Marker-controlled Wate...
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#1Jae Young Kim (Gachon University)H-Index: 4
#2Sion Jang (Gachon University)H-Index: 2
Last.Sungchul Choi (Gachon University)H-Index: 11
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Abstract This paper presents an empirical exploration of the use of capsule networks for text classification. While it has been shown that capsule networks are effective for image classification, the research regarding their validity in the domain of text has been initiated recently. In this paper, we show that capsule networks indeed have the potential for text classification and that they have several advantages over convolutional neural networks. As well, we compare our proposed model to the ...
4 CitationsSource
#1Qi Zheng (HUST: Huazhong University of Science and Technology)
#2Shujian Yu (UF: University of Florida)H-Index: 8
Last.Xinge You (HUST: Huazhong University of Science and Technology)H-Index: 30
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Abstract Low-Rank Matrix Recovery (LRMR) has recently been applied to saliency detection by decomposing image features into a low-rank component associated with background and a sparse component associated with visual salient regions. Despite its great potential, existing LRMR-based saliency detection methods seldom consider the inter-relationship among elements within these two components, thus are prone to generating scattered or incomplete saliency maps. In this paper, we introduce a novel an...
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#1Zhiwei Xue (WUST: Wuhan University of Science and Technology)
#2Yong Zhang (WUST: Wuhan University of Science and Technology)H-Index: 2
Last.Guijun Ma (HUST: Huazhong University of Science and Technology)H-Index: 1
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Abstract To solve the problem of the inaccurate prediction on remaining useful life (RUL) for lithium-ion battery, we proposed an integrated algorithm which combines adaptive unscented kalman filter (AUKF) and genetic algorithm optimized support vector regression (GA-SVR). Firstly, the state space model with double exponential is established to describe the degradation of lithium battery. Then, the AUKF algorithm is introduced to update adaptively both the process noise covariance and the observ...
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#1Cai MengH-Index: 6
Last.Lei LiuH-Index: 1
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Abstract Accurate cerebrovascular segmentation in Digital Subtraction Angiography (DSA) image, as an indispensable step, can help doctors appropriately estimate the degree of cerebrovascular lesions to avoid misdiagnosis. Because of the complexity of cerebrovascular structure and the uneven distribution of contrast media in DSA, automatic segmentation is a challenging task in clinical diagnosis. In recent years, deep convolutional neural networks (CNN) have outperformed the state-of-art methods ...
1 CitationsSource
Abstract Human perception of a signal depends on the ratio of the change of stimulus to the stimulus itself while the change of stimulus to the stimulus itself is usually ignored in hand-crafted feature descriptors. However, it is important for extracting discriminant feature. To address this problem, we firstly develop a local multiple patterns (LMP) feature descriptor based on the Weber's law for feature extraction and face recognition. In LMP, (1) the Weber's ratio is modified to contain the ...
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Abstract Because of the mechanism of TLS system, noise, outliers, various occlusions, varying cloud densities, etc. inevitably exist in the collection of TLS point clouds. To achieve automatic TLS point cloud registration, many methods, based on the hand-crafted features of keypoints, have been proposed. Despite significant progress, the current methods still face great challenges in accomplishing TLS point cloud registration. In this paper, we propose a multi-scale neural network to learn local...
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Abstract Classifying visual information is an apparently simple and effortless task in our everyday routine, but can we automatically predict what we see from signals emitted by the brain? While other researchers have already attempted to answer this question, we are the first to show that a commercially available BCI could be effectively used for visual image classification in real-world scenarios – when testing takes place at a completely different time than training data collection. The task ...
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