1,126 research outputs found

    Visual Comfort Assessment for Stereoscopic Image Retargeting

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    In recent years, visual comfort assessment (VCA) for 3D/stereoscopic content has aroused extensive attention. However, much less work has been done on the perceptual evaluation of stereoscopic image retargeting. In this paper, we first build a Stereoscopic Image Retargeting Database (SIRD), which contains source images and retargeted images produced by four typical stereoscopic retargeting methods. Then, the subjective experiment is conducted to assess four aspects of visual distortion, i.e. visual comfort, image quality, depth quality and the overall quality. Furthermore, we propose a Visual Comfort Assessment metric for Stereoscopic Image Retargeting (VCA-SIR). Based on the characteristics of stereoscopic retargeted images, the proposed model introduces novel features like disparity range, boundary disparity as well as disparity intensity distribution into the assessment model. Experimental results demonstrate that VCA-SIR can achieve high consistency with subjective perception

    A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

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    Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism. The sliding window mechanism requires fixed aspect ratios and limits the cropping region with arbitrary size. Moreover, the sliding window method usually produces tens of thousands of windows on the input image which is very time-consuming. Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this problem. Particularly, the proposed method develops an aesthetics aware reward function which especially benefits image cropping. Similar to human's decision making, we use a comprehensive state representation including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The agent is evaluated on several popular unseen cropping datasets. Experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with previous weakly supervised methods.Comment: Accepted by CVPR 201

    Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations

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    With recent advances in biophotonics, techniques such as narrow band imaging, confocal laser endomicroscopy, fluorescence spectroscopy, and optical coherence tomography, can be combined with normal white-light endoscopes to provide in vivo microscopic tissue characterisation, potentially avoiding the need for offline histological analysis. Despite the advantages of these techniques to provide online optical biopsy in situ, it is challenging for gastroenterologists to retarget the optical biopsy sites during endoscopic examinations. This is because optical biopsy does not leave any mark on the tissue. Furthermore, typical endoscopic cameras only have a limited field-of-view and the biopsy sites often enter or exit the camera view as the endoscope moves. In this paper, a framework for online tracking and retargeting is proposed based on the concept of tracking-by-detection. An online detection cascade is proposed where a random binary descriptor using Haar-like features is included as a random forest classifier. For robust retargeting, we have also proposed a RANSAC-based location verification component that incorporates shape context. The proposed detection cascade can be readily integrated with other temporal trackers. Detailed performance evaluation on in vivo gastrointestinal video sequences demonstrates the performance advantage of the proposed method over the current state-of-the-art

    AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generation

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    Advertising posters, a form of information presentation, combine visual and linguistic modalities. Creating a poster involves multiple steps and necessitates design experience and creativity. This paper introduces AutoPoster, a highly automatic and content-aware system for generating advertising posters. With only product images and titles as inputs, AutoPoster can automatically produce posters of varying sizes through four key stages: image cleaning and retargeting, layout generation, tagline generation, and style attribute prediction. To ensure visual harmony of posters, two content-aware models are incorporated for layout and tagline generation. Moreover, we propose a novel multi-task Style Attribute Predictor (SAP) to jointly predict visual style attributes. Meanwhile, to our knowledge, we propose the first poster generation dataset that includes visual attribute annotations for over 76k posters. Qualitative and quantitative outcomes from user studies and experiments substantiate the efficacy of our system and the aesthetic superiority of the generated posters compared to other poster generation methods.Comment: Accepted for ACM MM 202
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