High dynamic range video reconstruction from a stereo camera setup

Bätz, Michel; Richter, Thomas; Garbas, Jens-Uwe; Papst, Anton; Seiler, Jurgen; Kaup, André

doi:10.1016/j.image.2013.08.016

Cited by 34 publications

(36 citation statements)

References 20 publications

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“…For example, there are a number of methods that exploit stereo camera capturing rigs, where the two cameras are set to capture different exposures [31,156,235,239]. The images require reliable stereo matching in order to align the separated views of the cameras.…”

Section: Multi-camera Methodsmentioning

confidence: 99%

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Eilertsen¹

2018

Linköping Studies in Science and Technology. Dissertations

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Section: Multi-camera Methodsmentioning

confidence: 99%

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Eilertsen¹

2018

Linköping Studies in Science and Technology. Dissertations

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“…NCC is used to measure the correlation between two image templates, i.e., NCC quantifies how similar these image templates are [33][34][35]. The grade of similarity is a scalar number, the greater it is, the more correlated these images are.…”

Section: Normalized Cross-correlationmentioning

confidence: 99%

Parallelization of the Honeybee Search Algorithm for Object Tracking

Perez-Cham

Puente

Soubervielle‐Montalvo

et al. 2020

Applied Sciences

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Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee Search Algorithm (HSA), which is a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles, and was previously designed for three-dimensional reconstruction. This heuristic inspired by the search for food of honeybees, and here adapted to the problem of object tracking using GPU parallel computing, is extended from the original proposal of HSA towards video processing. In this work, the normalized cross-correlation (NCC) criteria is used as the fitness function. Experiments using 314 video sequences of the ALOV benchmark provides evidence about the quality regarding tracking accuracy and processing time. Also, according to these experiments, the proposed methodology is robust to high levels of Gaussian noise added to the image frames, and this confirms that the accuracy of the original NCC is preserved with the advantage of acceleration, offering the possibility of accelerating latest trackers using this methodology.

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“…Hrabar et al 15 presented an HDR stereo method to compute a 3-D occupancy map by merging disparity maps from multiple stereo exposure images for each frame. Bätz et al 16 proposed an HDR reconstruction method using a stereo camera setup, which can take differently exposed left and right images. They estimated disparity maps using NCC and then reconstructed HDR images by selectively merging left and right images.…”

Section: Previous Workmentioning

confidence: 99%

Depth maps and high-dynamic range image generation from alternating exposure multiview images

Heo

Lee

2017

J. Electron. Imaging

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For stereo matching, it is hard to find accurate correspondence for saturated regions, such as too dark or too bright regions, because there is rarely reliable information to match. In this situation, conventional highdynamic range (HDR) imaging techniques combining multiple exposures for each viewpoint can be adopted to generate well-exposed stereo images. This approach is, however, time-consuming and needs much memory to store multiple exposures for each viewpoint. We propose an efficient method to generate HDR multiview images as well as corresponding accurate depth maps. First, we take a single exposure for each viewpoint with alternating exposure setting, such as short and long exposure, as functions of viewpoint changes. Then, we compute an initial depth map for each view only using neighboring images that have the same exposure. To reduce the error of the initial depth maps for the saturated regions, we adopt the fusion move algorithm fusing neighboring depth maps that have different error regions. Finally, using the enhanced depth maps, we generate artifact-free and sharp HDR images using the joint bilateral filtering and a detail-transfer technique. Experimental results show that our method produces both consistent HDR images and accurate depth maps for various indoor and outdoor multiview images.

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High dynamic range video reconstruction from a stereo camera setup

Cited by 34 publications

References 20 publications

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Parallelization of the Honeybee Search Algorithm for Object Tracking

Depth maps and high-dynamic range image generation from alternating exposure multiview images

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