Dense Descriptors for Optical Flow Estimation: A Comparative Study

Baghaie, Ahmadreza; D’Souza, Roshan; Yu, Zeyun

doi:10.3390/jimaging3010012

Cited by 9 publications

(5 citation statements)

References 59 publications

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“…In such cases the computed correspondence must be discontinuity preserving. In other words, an image as a projective depiction of a scene may contain several objects which are actually lay on the same plane and can move independently and therefore, the computed flow patterns should account for that [67]. …”

Section: Resultsmentioning

confidence: 99%

Three-dimensional reconstruction of highly complex microscopic samples using scanning electron microscopy and optical flow estimation

et al. 2017

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Scanning Electron Microscope (SEM) as one of the major research and industrial equipment for imaging of micro-scale samples and surfaces has gained extensive attention from its emerge. However, the acquired micrographs still remain two-dimensional (2D). In the current work a novel and highly accurate approach is proposed to recover the hidden third-dimension by use of multi-view image acquisition of the microscopic samples combined with pre/post-processing steps including sparse feature-based stereo rectification, nonlocal-based optical flow estimation for dense matching and finally depth estimation. Employing the proposed approach, three-dimensional (3D) reconstructions of highly complex microscopic samples were achieved to facilitate the interpretation of topology and geometry of surface/shape attributes of the samples. As a byproduct of the proposed approach, high-definition 3D printed models of the samples can be generated as a tangible means of physical understanding. Extensive comparisons with the state-of-the-art reveal the strength and superiority of the proposed method in uncovering the details of the highly complex microscopic samples.

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Section: Resultsmentioning

confidence: 99%

Three-dimensional reconstruction of highly complex microscopic samples using scanning electron microscopy and optical flow estimation

et al. 2017

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“…Many segmentation methods for OCT data have been reported [23,24], most for OCT data of human retinas. However, these do not perform well when applied to mouse retinas because of the low contrast between retinal layers when compared to humans.…”

Section: Retinal Interface Detectionmentioning

confidence: 99%

Characterization of the retinal changes of the 3×Tg-AD mouse model of Alzheimer’s disease

et al. 2020

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Alzheimer's disease (AD) is a progressive neurodegenerative disorder whose diagnosis remains a notable challenge. The literature suggests that cerebral changes precede AD symptoms by over two decades, implying a significantly advanced stage of AD by the time it is usually diagnosed. In the study herein, texture analysis was applied to computed optical coherence tomography ocular fundus images to identify differences between a group of the transgenic mouse model of the Alzheimer's disease (3×Tg-AD) and a group of wild-type mice, at the ages of one and two-months-old. A substantial difference between groups was found at both time-points across all neuroretina's layers. Here, the inner nuclear layer stands out both in the level of statistically significant differences and on the extension of these differences which span through the imaged area. Also, the progression of AD is suggested to be spotted by texture analysis as demonstrated by the significant difference found in the inner plexiform and the outer nuclear layers from the age of one to the age of two-months-old. These findings demonstrate the potential of the use of the retina and texture analysis to the diagnosis of AD and monitor AD progression. Besides, the differences between groups found in this study suggest that the 3×Tg-AD model may be inappropriate to study early changes associated with the AD and other animal models should be tested following the same path and rationale. Moreover, these results also suggest that the human genes present in these transgenic mice may have an impact on the neurodevelopment of offspring which would justify the significant changes found at the age of one-month-old.

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“…Liu et al [144] used SIFT, which is rotation and scale invariant, into the data term to handle rotation. Other scale invariant descriptors [187,188] are also exploited.…”

Section: Rotationmentioning

confidence: 99%