2016 IEEE International Conference on Computational Photography (ICCP)最新文献

Fast, high dynamic range light field processing for pattern recognition 快速，高动态范围光场处理模式识别

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1109/ICCPHOT.2016.7492873

Scott McCloskey, B. Miller

引用次数: 3

High-speed imaging using CMOS image sensor with quasi pixel-wise exposure 采用准像素曝光的CMOS图像传感器进行高速成像

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1117/12.2270485

H. Nagahara, Toshiki Sonoda, K. Endo, Y. Sugiyama, R. Taniguchi

{"title":"High-speed imaging using CMOS image sensor with quasi pixel-wise exposure","authors":"H. Nagahara, Toshiki Sonoda, K. Endo, Y. Sugiyama, R. Taniguchi","doi":"10.1117/12.2270485","DOIUrl":"https://doi.org/10.1117/12.2270485","url":null,"abstract":"Several recent studies in compressive video sensing have realized scene capture beyond the fundamental trade-off limit between spatial resolution and temporal resolution using random space-time sampling. However, most of these studies showed results for higher frame rate video that were produced by simulation experiments or using an optically simulated random sampling camera, because there are currently no commercially available image sensors with random exposure or sampling capabilities. We fabricated a prototype complementary metal oxide semiconductor (CMOS) image sensor with quasi pixel-wise exposure timing that can realize nonuniform space-time sampling. The prototype sensor can reset exposures independently by columns and fix these amount of exposure by rows for each 8×8 pixel block. This CMOS sensor is not fully controllable via the pixels, and has line-dependent controls, but it offers flexibility when compared with regular CMOS or charge-coupled device sensors with global or rolling shutters. We propose a method to realize pseudo-random sampling for high-speed video acquisition that uses the flexibility of the CMOS sensor. We reconstruct the high-speed video sequence from the images produced by pseudo-random sampling using an over-complete dictionary. The proposed method also removes the rolling shutter effect from the reconstructed video.","PeriodicalId":156635,"journal":{"name":"2016 IEEE International Conference on Computational Photography (ICCP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129139040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32

Occlusion-robust 3D sensing using aerial imaging 使用航空成像的闭塞鲁棒3D传感

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1109/ICCPHOT.2016.7492883

M. Yasui, Yoshihiro Watanabe, M. Ishikawa

引用次数: 1

Single-shot diffuser-encoded light field imaging 单镜头漫射编码光场成像

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1109/ICCPHOT.2016.7492880

N. Antipa, Sylvia Necula, Ren Ng, L. Waller

引用次数: 44

White balance under mixed illumination using flash photography 混合照明下使用闪光灯摄影的白平衡

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1109/ICCPHOT.2016.7492879

Zhuo Hui, Aswin C. Sankaranarayanan, Kalyan Sunkavalli, Sunil Hadap

引用次数: 19

Sensor-level privacy for thermal cameras 热像仪的传感器级隐私

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1109/ICCPHOT.2016.7492877

F. Pittaluga, A. Zivkovic, S. Koppal

引用次数: 16

Correcting perceived perspective distortions using object specific planar transformations 使用对象特定的平面变换纠正感知到的透视畸变

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1109/ICCPHOT.2016.7492868

M. A. Tehrani, A. Majumder, M. Gopi

{"title":"Correcting perceived perspective distortions using object specific planar transformations","authors":"M. A. Tehrani, A. Majumder, M. Gopi","doi":"10.1109/ICCPHOT.2016.7492868","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2016.7492868","url":null,"abstract":"Distortions due to perspective projection is often described under the umbrella term of foreshortening in computer graphics and are treated the same way. However, a large body of literature from artists, perceptual psychologists and perception scientists have shown that the perception of these distortions is different in different situations. While the distortions themselves depend on both the depth and the orientation of the object with respect to the camera image plane, the perception of these distortions depends on other depth cues present in the image. In the absence of any depth cue or prior knowledge about the objects in the scene, the visual system finds it hard to correct the foreshortening automatically and such images need user input and external algorithmic distortion correction.In this paper, we claim that the shape distortion is more perceptible than area distortion, and quantify such perceived foreshortening as the non-uniformity across the image, of the ratio e of the differential areas of an object in the scene and its projection. We also categorize foreshortening into uniform and non-uniform foreshortening. Uniform foreshortening is perceived by our visual system as a distortion, even if e is uniform across the image, only when comparative objects of known sizes are present in the image. Non-uniform foreshortening is perceived when there is no other depth cue in the scene that can help the brain to correct for the distortion. We present a unified solution to correct these distortions in one or more non-occluded foreground objects by applying object-specific segmentation and affine transformation of the segmented camera image plane. Our method also ensures that the background undergoes minimal distortion and preserves background features during this process. This is achieved efficiently by solving Laplace's equations with Dirichlet boundary conditions, assisted by a simple and intuitive user interface.","PeriodicalId":156635,"journal":{"name":"2016 IEEE International Conference on Computational Photography (ICCP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127658001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Passive light and viewpoint sensitive display of 3D content 被动光和视点敏感显示3D内容

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1109/ICCPHOT.2016.7492881

Anat Levin, Haggai Maron, Michal Yarom

引用次数: 6

Blind dehazing using internal patch recurrence 内部补片复现盲目除雾

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1109/ICCPHOT.2016.7492870

Yuval Bahat, M. Irani

{"title":"Blind dehazing using internal patch recurrence","authors":"Yuval Bahat, M. Irani","doi":"10.1109/ICCPHOT.2016.7492870","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2016.7492870","url":null,"abstract":"Images of outdoor scenes are often degraded by haze, fog and other scattering phenomena. In this paper we show how such images can be dehazed using internal patch recurrence. Small image patches tend to repeat abundantly inside a natural image, both within the same scale, as well as across different scales. This behavior has been used as a strong prior for image denoising, super-resolution, image completion and more. Nevertheless, this strong recurrence property significantly diminishes when the imaging conditions are not ideal, as is the case in images taken under bad weather conditions (haze, fog, underwater scattering, etc.). In this paper we show how we can exploit the deviations from the ideal patch recurrence for \"Blind De-hazing\" — namely, recovering the unknown haze parameters and reconstructing a haze-free image. We seek the haze parameters that, when used for dehazing the input image, will maximize the patch recurrence in the dehazed output image. More specifically, pairs of co-occurring patches at different depths (hence undergoing different degrees of haze) allow recovery of the airlight color, as well as the relative-transmission of each such pair of patches. This in turn leads to dense recovery of the scene structure, and to full image dehazing.","PeriodicalId":156635,"journal":{"name":"2016 IEEE International Conference on Computational Photography (ICCP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133988346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 86

A picture is worth a billion bits: Real-time image reconstruction from dense binary threshold pixels 一张图片价值十亿比特:从密集的二值阈值像素实时图像重建

2016 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2016-05-13 DOI: 10.1109/ICCPHOT.2016.7492874

Tal Remez, O. Litany, A. Bronstein

{"title":"A picture is worth a billion bits: Real-time image reconstruction from dense binary threshold pixels","authors":"Tal Remez, O. Litany, A. Bronstein","doi":"10.1109/ICCPHOT.2016.7492874","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2016.7492874","url":null,"abstract":"The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artefacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.","PeriodicalId":156635,"journal":{"name":"2016 IEEE International Conference on Computational Photography (ICCP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125121890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16