Interest of pseudo-focused images for key-points detection in plenoptic imaging

Assaad, Mohamad Al; Bazeille, Stéphane; Laurain, Thomas; Dieterlen, Alain; Cudel, Christophe

doi:10.1117/12.2588954

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Each form of PFI is generated using a different sort of micro-lens. 1 Thus our suggested technique will take into consideration the extraction of key-points from one type of PFI and increase this number by the number of key-points extracted from the other PFIs. The entire technique is depicted in the organigram in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Our previous work showed the interest of pseudo-focused images (PFIs) in key-points detection. 1 Today, we are interested in enhancing the number of the detected key-points in the raw LF images using a method based on the interaction between the available types of micro-lenses. We are mainly interested in light cameras in autonomous navigation in order to perform accurate scale visual odometry in order to replace some of the methods that are developed in this field 2 , 3 .…”

Section: Introductionmentioning

confidence: 99%

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Enhanced key-point detection for plenoptic imaging

Assaad

Bazeille

Josso-Laurain

et al. 2023

Sixteenth International Conference on Quality Control by Artificial Vision

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Standard imaging techniques do not get as much information from a scene as light-field imaging. Light-field (LF) cameras can measure the light intensity reflected by an object and, most importantly, the direction of its light rays. This information can be used in different applications, such as depth estimation, in-plane focusing, creating full-focused images, etc. However, standard key-point detectors often employed in computer vision applications cannot be applied directly to plenoptic images due to the nature of raw LF images. This work presents an approach for key-point detection dedicated to plenoptic images. Our method allows using of conventional key-point detector methods. It forces the detection of this key-point in a set of micro-images of the raw LF image. Obtaining this important number of key-points is essential for applications that require finding additional correspondences in the raw space, such as disparity estimation, indirect visual odometry techniques, and others. The approach is set to the test by modifying the Harris key-point detector.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced key-point detection for plenoptic imaging

Assaad

Bazeille

Josso-Laurain

et al. 2023

Sixteenth International Conference on Quality Control by Artificial Vision

View full text Add to dashboard Cite

show abstract

“…This descriptor has a scale invariance and is highly robust for detecting features with perspective changes [ 18 ]. To improve the accuracy of the feature extraction, Assaad proposed a method to detect the feature points on the original light field image by applying a feature point detector on the pseudofocused image (PFI) [ 19 ]. However, the above methods require a high computational cost and greatly increase the time complexity of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Light Field Full-Focus Image Feature Point Matching Method with an Improved ORB Algorithm

Zuo

Guan

Duan

et al. 2022

Sensors

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Most of the traditional image feature point extraction and matching methods are based on a series of light properties of images. These light properties easily conflict with the distinguishability of the image features. The traditional light imaging methods focus only on a fixed depth of the target scene, and subjects at other depths are often easily blurred. This makes the traditional image feature point extraction and matching methods suffer from a low accuracy and a poor robustness. Therefore, in this paper, a light field camera is used as a sensor to acquire image data and to generate a full-focus image with the help of the rich depth information inherent in the original image of the light field. The traditional ORB feature point extraction and matching algorithm is enhanced with the goal of improving the number and accuracy of the feature point extraction for the light field full-focus images. The results show that the improved ORB algorithm extracts not only most of the features in the target scene but also covers the edge part of the image to a greater extent and produces extracted feature points which are evenly distributed for the light field full-focus image. Moreover, the extracted feature points are not repeated in a large number in a certain part of the image, eliminating the aggregation phenomenon that exists in traditional ORB algorithms.

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Interest of pseudo-focused images for key-points detection in plenoptic imaging

Cited by 2 publications

References 16 publications

Enhanced key-point detection for plenoptic imaging

Enhanced key-point detection for plenoptic imaging

A Light Field Full-Focus Image Feature Point Matching Method with an Improved ORB Algorithm

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