Depth estimation in Integral Imaging based on a maximum voting strategy

Martínez-Usó, Adolfo; Carmona, Pedro; Sotoca, José Martínez; Pla, Filiberto; Javidi, Bahram

doi:10.1109/jdt.2016.2615565

Cited by 8 publications

(7 citation statements)

References 47 publications

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“…Table 1 summarizes the characteristics of example scenes from the chosen datasets. The raw data from different datasets are of different parameterizations, for example, scene bathroom ( [37]) generated from 3DS max and Chess/Truck ( [39]) captured by camera gantry encoded the LF in the form of horizontal and vertical views, whereas scene Pillars/Bikes ( [38]) captured by Lytro Illum was organized as a sequence of raw 2D lenslet images. Therefore, for consistency of the representation and without losing generality, we simulate the raw sensor image capture using an imaginary plenoptic camera: we parameterize each LF as a 2D representation such that U × V angular views (of size X ×Y pixels) are concatenated into a simulated plenoptic sensor pixel grid.…”

Section: Experiments a Datasetsmentioning

confidence: 99%

Simultaneous Color Restoration and Depth Estimation in Light Field Imaging

Pla

Sjöström

et al. 2022

IEEE Access

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Recent studies in the light field imaging have shown the potential and advantages of different light field information processes. In most of the existing techniques, the processing pipeline of light field has been treated in a step-by-step manner, and each step is considered to be independent from the others. For example, in light field color demosaicing, inferring the scene geometry is treated as an irrelevant and negligible task, and vice versa. Such processing techniques may fail due to the inherent connection among different steps, and result in both corrupted post-processing and defective pre-processing results.In this paper, we address the interaction between color interpolation and depth estimation in light field, and propose a probabilistic approach to handle these two processing steps jointly. This probabilistic framework is based on a Markov Random Fields -Collaborative Graph Model for simultaneous Demosaicing and Depth Estimation (CGMDD)-to explore the color-depth interdependence from general light field sampling. Experimental results show that both image interpolation quality and depth estimation can benefit from their interaction, mainly for processes such as image demosaicing which are shown to be sensitive to depth information, especially for light field sampling with large baselines.INDEX TERMS Light field, demosaicing, depth estimation, Markov random field, graph model.

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Section: Experiments a Datasetsmentioning

confidence: 99%

Simultaneous Color Restoration and Depth Estimation in Light Field Imaging

Pla

Sjöström

et al. 2022

IEEE Access

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“…This 3D reconstruction scheme with SAII can be carried out in the case where the cameras are located on a flat surface [25], but also in the case where the positions of those cameras are spread or in a free pose configuration in 3D space [33]- [35].…”

Section: Photo-consistency Based On Median Distancesmentioning

confidence: 99%

“…For instance, the Normalised Cross-Correlation (NCC), the Sum of Squared Differences (SSD), Mutual Information based measures, etc. Other measures try to explicitly deal with, for instance, occlusions and highlights [23], [25].…”

Section: Introductionmentioning

confidence: 99%

Depth and All-in-Focus Image Estimation in Synthetic Aperture Integral Imaging Under Partial Occlusions

et al. 2019

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A common assumption in the integral imaging reconstruction is that a pixel will be photoconsistent if all viewpoints observed by the different cameras converge at a single point when focusing at the proper depth. However, the presence of occlusions between objects in the scene prevents this from being fulfilled. In this paper, a novel depth and all-in focus image estimation method is presented, based on a photo-consistency measure that uses the median criterion in relation to the elemental images. The interest of this approach is to find a solution to detect which camera correctly sees the partially occluded object at a certain depth and allows for a precise solution to the object depth. In addition, a robust solution is proposed to detect the boundary limits between partially occluded objects, which are subsequently used during the regularization depth estimation process. The experimental results show that the proposed method outperforms other state-of-the-art depth estimation methods in a synthetic aperture integral imaging framework. INDEX TERMS Synthetic aperture integral imaging, depth map estimation, all-in-focus image, partial occlusions, 3D image processing.

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“…Recently, a depth estimation method through a photoconsistency criterion based on a voting strategy has been presented in [80]. The proposed approach, (hereafter called Max-Voting method) is based on a soft-voting procedure that takes into account the level of agreement (similarity) among the different camera views, using a similar strategy to those presented in [81] [82].…”

Section: ) Depth Estimation Using a Photoconsistency -Based Criterionmentioning

confidence: 99%

“…At a certain depth range z ∈ Zrange, the pixel at the position (i, j) of the image I and its square surrounding window W are defined as Wij), we proposed in [80] a criterion based on a voting procedure where each camera votes in favor of the pixel (i, j) at depth level z depending on the similarity of the pixel intensities of each camera as compared to camera R. A threshold value (THR) is also assigned that denotes whether this similarity is good enough. Similarity is measured using the Euclidean distance d between the a*b* values (from the L*a*b* color space) for each pixel.…”

Section: ) Depth Estimation Using a Photoconsistency -Based Criterionmentioning

confidence: 99%

Multidimensional Optical Sensing and Imaging System (MOSIS): From Macroscales to Microscales

et al. 2017

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Abstract-Multidimensional optical imaging systems for information processing and visualization technologies have numerous applications in fields such as manufacturing, medical sciences, entertainment, robotics, surveillance, and defense. Among different three-dimensional (3D) imaging methods, integral imaging is a promising multiperspective sensing and display technique. Compared with other 3D imaging techniques, integral imaging can capture a scene using an incoherent light source and generate real 3D images for observation without any special viewing devices. This review paper describes passive multidimensional imaging systems combined with different integral imaging configurations. One example is the integral imaging based Multidimensional Optical Sensing and Imaging Systems (MOSIS), which can be used for 3D visualization, seeing through obscurations, material inspection, and object recognition from micro scales to long range imaging. This system utilizes many degrees of freedom such as time and space multiplexing, depth information, polarimetric, temporal, photon flux and multispectral information based on integral imaging to record and reconstruct the multidimensionally integrated scene. Image fusion may be used to integrate the multidimensional images obtained by polarimetric sensors, multispectral cameras, and various multiplexing techniques. The multidimensional images contain substantially more information compared with two-dimensional (2D) images or conventional 3D images. In addition, we present recent progress and applications of 3D integral imaging including human gesture recognition in the time domain, depth estimation, mid-wave infrared photon counting, 3D polarimetric imaging for object shape and material identification, dynamic integral imaging implemented with liquid crystal devices, and 3D endoscopy for healthcare applications.

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Depth estimation in Integral Imaging based on a maximum voting strategy

Cited by 8 publications

References 47 publications

Simultaneous Color Restoration and Depth Estimation in Light Field Imaging

Simultaneous Color Restoration and Depth Estimation in Light Field Imaging

Depth and All-in-Focus Image Estimation in Synthetic Aperture Integral Imaging Under Partial Occlusions

Multidimensional Optical Sensing and Imaging System (MOSIS): From Macroscales to Microscales

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