395 research outputs found
Blind Deblurring of Text Images Using a Text-Specific Hybrid Dictionary
No full textIn this paper, we propose a blind text image deblurring algorithm by using a text-specific hybrid dictionary. After careful analysis, we find that the text-specific hybrid dictionary has the great ability of providing powerful contextual information for text image deblurring. Here, it is worth noting that our proposed method is inspired by our observation that an intermediate latent image contains not only sharp regions, but also multiple types of small blurred regions. Based upon our discovery, we propose a prior for text images based on sparse representation, which models the relationship between an intermediate latent image and a desired sharp image. To this end, we carefully collect three different image patch pairs, which are 1) Gaussian blur-sharp, 2) motion blur-sharp, and 3) sharp-sharp, in order to construct the text-specific hybrid dictionary. We also propose a new optimization framework suitable for the task of text image deblurring in this paper. Extensive experiments have been conducted on a challenging dataset of synthetic and real-world text images. Our results demonstrate that the proposed method outperforms the state-of-the-art image deblurring methods both quantitatively and qualitatively.
A Modified Hardware-Friendly Multidirectional Extrapolation Hole-Filling Method for Depth-Image-Based Rendering
No full text
A Global-local Embedding Module for Fashion Landmark Detection
No full textDetecting fashion landmarks is a fundamental technique for visual clothing analysis. Due to the large variation and non-rigid deformation of clothes, localizing fashion landmarks suffers from large spatial variances across poses, scales, and styles. Therefore, understanding contextual knowledge of clothes is required for accurate landmark detection. To that end, in this paper, we propose a fashion landmark detection network with a global-local embedding module. The global-local embedding module is based on a non-local operation for capturing long-range dependencies and a subsequent convolution operation for adopting local neighborhood relations. With this processing, the network can consider both global and local contextual knowledge for a clothing image. We demonstrate that our proposed method has an excellent ability to learn advanced deep feature representations for fashion landmark detection. Experimental results on two benchmark datasets show that the proposed network outperforms the state-of-the-art methods. Our code is available at https://github.com/shumming/GLE_FLD
Dramatically Enhanced Mechanosensitivity and Signal-to-Noise Ratio of Nanoscale Crack-Based Sensors: Effect of Crack Depth
No full text[No abstract available] © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim485611sciescopu
Learning to Discriminate Information for Online Action Detection
No full textFrom a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as well as the action of interest. For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others. Our unit, named Information Discrimination Unit (IDU), decides whether to accumulate input information based on its relevance to the current action. This enables our recurrent network with IDU to learn a more discriminative representation for identifying ongoing actions. In experiments on two benchmark datasets, TVSeries and THUMOS-14, the proposed method outperforms state-of-the-art methods by a significant margin. Moreover, we demonstrate the effectiveness of our recurrent unit by conducting comprehensive ablation studies
Landmark-free Clothes Recognition with a Two-Branch Feature Selective Network
No full textIn this Letter, the authors present a 'landmark-free' clothes recognition approach. Recent studies have shown that the use of landmark information has achieved great success in the task of clothes recognition. However, the landmark annotation is very labour intensive and time consuming. It also suffers from inter- and intra-individual variability. To overcome these problems, the authors propose a two-branch feature selective network for category classification and attribute prediction. Note that, in this Letter, they prove that the proposed network has an excellent ability to effectively learn a discriminative feature representation of a 'clothing image'. Experimental results on the benchmark data set show that the proposed network yields comparable performance to the state-of-the-art methods, which strongly depend on the fashion landmark.
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