Real- Time Hyperspectral Stereo Processing for the Generation of 3D Depth Information

Heide, Nina Felicitas; Frese, Christian; Emter, Thomas; Petereit, Janko

doi:10.1109/icip.2018.8451194

“…It is important to notice that the authors did not focus on the acceleration, so although it is a local stereo algorithm, the processing time ranges around more than a hundred seconds. Another example is the work of Heide et al [23], who presented an HS stereo system with two snapshot cameras and a local processing chain based on a weighted sum of census transform, Sum of Absolute Differences (SAD), and Sum Gradient Differences (SGD), along with a guided filtering process. In addition, their work focused on the acceleration of the application in a GPU, where they obtained results in the order of hundred of milliseconds.…”

Section: Related Workmentioning

confidence: 99%

GoRG: Towards a GPU-Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications

Sancho

¹

,

Sutradhar

²

,

Rosa

³

et al. 2021

Sensors

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HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon’s action, with their incorporation being a neurosurgeon’s task. The project in which this work is framed generates an unified and more accurate 3D immersive model using HS, MRI, and IOUS information. To do so, the HS images need to include 3D information and it needs to be generated in real-time operating room conditions, around a few seconds. This work presents Graph cuts Reference depth estimation in GPU (GoRG), a GPU-accelerated multiview depth estimation tool for HS images also able to process YUV images in less than 5.5 s on average. Compared to a high-quality SoA algorithm, MPEG DERS, GoRG YUV obtain quality losses of −0.93 dB, −0.6 dB, and −1.96% for WS-PSNR, IV-PSNR, and VMAF, respectively, using a video synthesis processing chain. For HS test images, GoRG obtains an average RMSE of 7.5 cm, with most of its errors in the background, needing around 850 ms to process one frame and view. These results demonstrate the feasibility of using GoRG during a tumor resection operation.

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“…Ordinarily the organizing component B is a round circle in the plane, however it very well may be any shape. The picture and organizing component sets need not be confined to sets in the 2D plane, yet could be characterized in 1, 2, 3 (or higher) measurements [10].…”

Section: Fig 1: Basic Input Image With Sub-pixel Classificationmentioning

confidence: 99%

Effective Morphological Transformation and Sub-pixel Classification of Clustered Images

Rajesh¹

2020

converter

1

0

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The main aim of this research work is to perform the morphological operations with reduced time complexity and area complexity. Morphological operation is the key element in any image processing. Finding the maximum and minimum using a window of defined size will imply to the morphological dilation and erosion respectively. So the proposed algorithm should be fast in the comparison and sorting, this way the time complexity could be reduced. It’s believed that the anchor concept will fetch this cause. The idea behind this is it fixes a pixel and setting it as the center pixel all the surrounding pixels will be processed. Moreover this is now been implemented for rectangular structuring element. This paper attempts the same for flat and 3D structuring elements. Hyper-spectral Imaging is a developing zone of remote detecting applications. Hyper-spectral pictures incorporate more extravagant and better otherworldly data than the multi-spectral pictures got previously. Hyper-otherworldly pictures are described by an exchange off between the unearthly and spatial resolution. The principle issue of the hyper-ghostly information is the generally low spatial goal. For arrangement, the serious issue brought about by low spatial goal is the blended pixels. Blended pixels alluded to the pixels which are involved by more than one land spread class. In the proposed procedure another strategy is utilized to address the issue of blended pixels and to get a better spatial goal of the land spread characterization maps. The strategy misuses the upsides of both picture bunching methods and phantom dimming calculations, so as to decide the fragmentary plenitudes of the classes at a sub-pixel scale. Spatial regularization by Flank planning method is at last performed to spatially find the got classes at sub-pixel level.

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“…Ordinarily the organizing component B is a round circle in the plane, however it very well may be any shape. The picture and organizing component sets need not be confined to sets in the 2D plane, yet could be characterized in 1, 2, 3 (or higher) measurements [10]. ISODATA clustering is an unsupervised hard classification technique.…”

Section: Fig 1: Basic Input Image With Sub-pixel Classificationmentioning

confidence: 99%

“…Calculation here introduced is a combination of the past Van Herk and Gil and Werman calculation. Joseph Gil and Michael Werman in [10] give a quick calculation to discover min, middle, max or some other measurement channel changes. The calculation sets aside steady effort for min and max channels and polylog time for middle channel.…”

Section: Introductionmentioning

confidence: 99%

Effective Morphological Transformation and Sub-pixel Classification of Clustered Images

Rajesh¹

2019

IJNPME

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The main aim of this research work is to perform the morphological operations with reduced time complexity and area complexity. Morphological operation is the key element in any image processing. Finding the maximum and minimum using a window of defined size will imply to the morphological dilation and erosion respectively. So the proposed algorithm should be fast in the comparison and sorting, this way the time complexity could be reduced. It’s believed that the anchor concept will fetch this cause. The idea behind this is it fixes a pixel and setting it as the center pixel all the surrounding pixels will be processed. Moreover this is now been implemented for rectangular structuring element. This paper attempts the same for flat and 3D structuring elements. Hyper-spectral Imaging is a developing zone of remote detecting applications. Hyper-spectral pictures incorporate more extravagant and better otherworldly data than the multi-spectral pictures got previously. Hyper-otherworldly pictures are described by an exchange off between the unearthly and spatial resolution. The principle issue of the hyper-ghostly information is the generally low spatial goal. For arrangement, the serious issue brought about by low spatial goal is the blended pixels. Blended pixels alluded to the pixels which are involved by more than one land spread class. In the proposed procedure another strategy is utilized to address the issue of blended pixels and to get a better spatial goal of the land spread characterization maps. The strategy misuses the upsides of both picture bunching methods and phantom dimming calculations, so as to decide the fragmentary plenitudes of the classes at a sub-pixel scale. Spatial regularization by Flank planning method is at last performed to spatially find the got classes at sub-pixel level.

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Real- Time Hyperspectral Stereo Processing for the Generation of 3D Depth Information

Cited by 14 publications

References 23 publications

GoRG: Towards a GPU-Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications

GoRG: Towards a GPU-Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications

Effective Morphological Transformation and Sub-pixel Classification of Clustered Images

Effective Morphological Transformation and Sub-pixel Classification of Clustered Images

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