Real-time foreground segmentation via range and color imaging

Crabb, R.; Tracey, C.; Puranik, Ameya; Davis, James

doi:10.1109/cvprw.2008.4563170

Cited by 71 publications

(50 citation statements)

References 6 publications

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“…Their work was first intended to compute a solution for image analysis and enhancement tasks, such as tone mapping or colourization through a downsampled version of the data. This idea was later applied for depth map enhancement in the context of real-time matting as presented by Crabb et al [9]. The JBU filter enhances an initial depth map D to the higher resolution of a corresponding 2-D guidance image I, as follows…”

Section: Problem Statement and Backgroundmentioning

confidence: 99%

Depth Enhancement by Fusion for Passive and Active Sensing

Garcia

Aouada

Abdella

et al. 2012

Lecture Notes in Computer Science

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Abstract. This paper presents a general refinement procedure that enhances any given depth map obtained by passive or active sensing. Given a depth map, either estimated by triangulation methods or directly provided by the sensing system, and its corresponding 2-D image, we correct the depth values by separately treating regions with undesired effects such as empty holes, texture copying or edge blurring due to homogeneous regions, occlusions, and shadowing. In this work, we use recent depth enhancement filters intended for Time-of-Flight cameras, and adapt them to alternative depth sensing modalities, both active using an RGB-D camera and passive using a dense stereo camera. To that end, we propose specific masks to tackle areas in the scene that require a special treatment. Our experimental results show that such areas are satisfactorily handled by replacing erroneous depth measurements with accurate ones.

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Section: Problem Statement and Backgroundmentioning

confidence: 99%

Depth Enhancement by Fusion for Passive and Active Sensing

Garcia

Aouada

Abdella

et al. 2012

Lecture Notes in Computer Science

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“…So far TOF cameras have not been widely used in video segmentation application. Existing TOF camera-based matting algorithms (Crabb et al 2008;Wu et al 2008;Zhu et al 2009) directly take the depth image from the TOF camera and threshold it to compute a foreground mask for trimap generation. These local approaches are simple but less robust.…”

Section: Tof Camera-based Foreground Extractionmentioning

confidence: 99%

Automatic Real-Time Video Matting Using Time-of-Flight Camera and Multichannel Poisson Equations

et al. 2011

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This paper presents an automatic real-time video matting system. The proposed system consists of two novel components. In order to automatically generate trimaps for live videos, we advocate a Time-of-Flight (TOF) camerabased approach to video bilayer segmentation. Our algorithm combines color and depth cues in a probabilistic fusion framework. The scene depth information returned by the TOF camera is less sensitive to environment changes, which makes our method robust to illumination variation, dynamic background and camera motion. For the second step, we perform alpha matting based on the segmentation Electronic supplementary material The online version of this article (doi:10.1007/s11263-011-0471-x) contains supplementary material, which is available to authorized users. result. Our matting algorithm uses a set of novel Poisson equations that are derived for handling multichannel color vectors, as well as the depth information captured. Realtime processing speed is achieved through optimizing the algorithm for parallel processing on graphics hardware. We demonstrate the effectiveness of our matting system on an extensive set of experimental results.

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“…Indeed, Kopf et al [5] proposed a modified bilateral filter, called Joint Bilateral Upsampling (JBU), to upsample the low resolution depth maps by considering a high resolution guidance image taken from the same scene. Crabb et al [6] implemented this alternative sensor fusion strategy in a real-time method for foreground/background segmentation of a colour video sequence. Yang et al [7] presented another method that uses an iterative refinement module with bilateral filtering of the cost volume.…”

Section: Introductionmentioning

confidence: 99%

Pixel weighted average strategy for depth sensor data fusion

Garcia

Mirbach²,

Ottersten

et al. 2010

2010 IEEE International Conference on Image Processing

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This paper introduces a new multi-lateral filter to fuse lowresolution depth maps with high-resolution images. The goal is to enhance the resolution of Time-of-Flight sensors and, at the same time, reduce the noise level in depth measurements. Our approach is based on the joint bilateral upsampling, extended by a new factor that considers the low reliability of depth measurements along the low-resolution depth map edges. Our experimental results show better performances than alternative depth enhancing data fusion techniques.

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Real-time foreground segmentation via range and color imaging

Cited by 71 publications

References 6 publications

Depth Enhancement by Fusion for Passive and Active Sensing

Depth Enhancement by Fusion for Passive and Active Sensing

Automatic Real-Time Video Matting Using Time-of-Flight Camera and Multichannel Poisson Equations

Pixel weighted average strategy for depth sensor data fusion

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