Motion Detection in High Resolution Enhancement

Shifted Superposition (SSPOS) is a resolution enhancement methodwhere apparent high-resolution content is displayed using a lowresolutionprojection system with an opto-mechanical shifter. WhileSSPOS-enhanced projectors have been showing promising resultsin still images, they still suffer from motion artifacts in video contents.Motivated by this, we present a novel approach to apparentprojector resolution enhancement for videos via motion-basedblurring module. We propose the use of a motion detection moduleand a blurring module to compensate for both SSPOS-resulted andnatural motion artifacts in the video content. To accomplish this,we combine both local and global motion estimation algorithms togenerate accurate dense flow fields. The detected motion regionsare enhanced using directional Gaussian filters. Preliminary resultsshow that the proposed method can produce accurate densemotion vectors and significantly reduce the artifacts in videos

Optimizing Apparent Display Resolution Enhancement for Arbitrary Videos

Display resolution is frequently exceeded by available image resolution. Recently, apparent display resolution enhancement techniques (ADRE) have demonstrated how characteristics of the human visual system can be exploited to provide super-resolution on high refresh rate displays. In this paper we address the problem of generalizing the apparent display resolution enhancement technique to conventional videos of arbitrary content. We propose an optimization-based approach to continuously translate the video frames in such a way that the added motion enables apparent resolution enhancement for the salient image region. The optimization takes the optimal velocity, smoothness and similarity into account to compute an appropriate trajectory. Additionally, we provide an intuitive user interface which allows to guide the algorithm interactively and preserve important compositions within the video. We present a user study evaluating apparent rendering quality and demonstrate versatility of our method on a variety of general test scenes.Aktuelle Kameras sind in der Lage, Videos mit sehr hoher Auflösung aufzunehmen (> 4K Pixel). Monitore, Fernseher und Projektoren haben jedoch meist eine deutlich niedrigere Auflösung (FullHD). Bei der Darstellung hochaufgelöster Videos auf diesen Geräten gehen durch das nötige Herrunterrechnen der Videodaten feine Details verloren, z.B. Haare oder die Pigmentierung von Oberflächenmaterialien. Es wird ein Verfahren präsentiert, welches die Darstellung eines beliebigen Videos mit einer Auflösung ermöglicht, die perzeptuell höher ist als die Auflösung des Ausgabegerätes

The Visual Centrifuge: Model-Free Layered Video Representations

True video understanding requires making sense of non-lambertian scenes where the color of light arriving at the camera sensor encodes information about not just the last object it collided with, but about multiple mediums -- colored windows, dirty mirrors, smoke or rain. Layered video representations have the potential of accurately modelling realistic scenes but have so far required stringent assumptions on motion, lighting and shape. Here we propose a learning-based approach for multi-layered video representation: we introduce novel uncertainty-capturing 3D convolutional architectures and train them to separate blended videos. We show that these models then generalize to single videos, where they exhibit interesting abilities: color constancy, factoring out shadows and separating reflections. We present quantitative and qualitative results on real world videos.Comment: Appears in: 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019). This arXiv contains the CVPR Camera Ready version of the paper (although we have included larger figures) as well as an appendix detailing the model architectur

Change Detection Using Synthetic Aperture Radar Videos

Many researches have been carried out for change detection using temporal SAR images. In this paper an algorithm for change detection using SAR videos has been proposed. There are various challenges related to SAR videos such as high level of speckle noise, rotation of SAR image frames of the video around a particular axis due to the circular movement of airborne vehicle, non-uniform back scattering of SAR pulses. Hence conventional change detection algorithms used for optical videos and SAR temporal images cannot be directly utilized for SAR videos. We propose an algorithm which is a combination of optical flow calculation using Lucas Kanade (LK) method and blob detection. The developed method follows a four steps approach: image filtering and enhancement, applying LK method, blob analysis and combining LK method with blob analysis. The performance of the developed approach was tested on SAR videos available on Sandia National Laboratories website and SAR videos generated by a SAR simulator

Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks

Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.Comment: 9 pages, 5 figures, 2 ancillary files; minor changes for camera-ready version. appears in Advances in Neural Information Processing Systems 29 (NIPS 2016