Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio

Stereoscopic Seam Carving With Temporal Consistency

In this paper, we present a novel technique for seam carving of stereoscopic video. It removes seams of pixels in areas that are most likely not noticed by the viewer. When applying seam carving to stereoscopic video rather than monoscopic still images, new challenges arise. The detected seams must be consistent between the left and the right view, so that no depth information is destroyed. When removing seams in two consecutive frames, temporal consistency between the removed seams must be established to avoid flicker in the resulting video. By making certain assumptions, the available depth information can be harnessed to improve the quality achieved by seam carving. Assuming that closer pixels are more important, the algorithm can focus on removing distant pixels first. Furthermore, we assume that coherent pixels belonging to the same object have similar depth. By avoiding to cut through edges in the depth map, we can thus avoid cutting through object boundaries

Real-time content-aware video retargeting on the Android platform for tunnel vision assistance

As mobile devices continue to rise in popularity, advances in overall mobile device processing power lead to further expansion of their capabilities. This, coupled with the fact that many people suffer from low vision, leaves substantial room for advancing mobile development for low vision assistance. Computer vision is capable of assisting and accommodating individuals with blind spots or tunnel vision by extracting the necessary information and presenting it to the user in a manner they are able to visualize. Such a system would enable individuals with low vision to function with greater ease. Additionally, offering assistance on a mobile platform allows greater access. The objective of this thesis is to develop a computer vision application for low vision assistance on the Android mobile device platform. Specifically, the goal of the application is to reduce the effects tunnel vision inflicts on individuals. This is accomplished by providing an in-depth real-time video retargeting model that builds upon previous works and applications. Seam carving is a content-aware retargeting operator which defines 8-connected paths, or seams, of pixels. The optimality of these seams is based on a specific energy function. Discrete removal of these seams permits changes in the aspect ratio while simultaneously preserving important regions. The video retargeting model incorporates spatial and temporal considerations to provide effective image and video retargeting. Data reduction techniques are utilized in order to generate an efficient model. Additionally, a minimalistic multi-operator approach is constructed to diminish the disadvantages experienced by individual operators. In the event automated techniques fail, interactive options are provided that allow for user intervention. Evaluation of the application and its video retargeting model is based on its comparison to existing standard algorithms and its ability to extend itself to real-time. Performance metrics are obtained for both PC environments and mobile device platforms for comparison

Improved content aware scene retargeting for retinitis pigmentosa patients

<p>Abstract</p> <p>Background</p> <p>In this paper we present a novel scene retargeting technique to reduce the visual scene while maintaining the size of the key features. The algorithm is scalable to implementation onto portable devices, and thus, has potential for augmented reality systems to provide visual support for those with tunnel vision. We therefore test the efficacy of our algorithm on shrinking the visual scene into the remaining field of view for those patients.</p> <p>Methods</p> <p>Simple spatial compression of visual scenes makes objects appear further away. We have therefore developed an algorithm which removes low importance information, maintaining the size of the significant features. Previous approaches in this field have included <it>seam carving</it>, which removes low importance seams from the scene, and <it>shrinkability </it>which dynamically shrinks the scene according to a generated importance map. The former method causes significant artifacts and the latter is inefficient. In this work we have developed a new algorithm, combining the best aspects of both these two previous methods. In particular, our approach is to generate a <it>shrinkability </it>importance map using as seam based approach. We then use it to dynamically shrink the scene in similar fashion to the <it>shrinkability </it>method. Importantly, we have implemented it so that it can be used in real time without prior knowledge of future frames.</p> <p>Results</p> <p>We have evaluated and compared our algorithm to the <it>seam carving </it>and image <it>shrinkability </it>approaches from a content preservation perspective and a compression quality perspective. Also our technique has been evaluated and tested on a trial included 20 participants with simulated tunnel vision. Results show the robustness of our method at reducing scenes up to 50% with minimal distortion. We also demonstrate efficacy in its use for those with simulated tunnel vision of 22 degrees of field of view or less.</p> <p>Conclusions</p> <p>Our approach allows us to perform content aware video resizing in real time using only information from previous frames to avoid jitter. Also our method has a great benefit over the ordinary resizing method and even over other image retargeting methods. We show that the benefit derived from this algorithm is significant to patients with fields of view 20° or less.</p