Removing shadows from images using color and near-infrared

Salamati, Neda; Germain, Arthur; Siisstrunk, Sabine

doi:10.1109/icip.2011.6115788

Cited by 28 publications

(18 citation statements)

References 10 publications

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“…We found that the depth variance is higher than predicted for i −2 ∈ [1, 39], which corresponds approximately to i ∈ [0. 16,1]. In this intensity interval we observed an increase in the intensity variance as well.…”

Section: Camera Noise Modelsupporting

confidence: 49%

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Towards Illumination-Invariant 3D Reconstruction Using ToF RGB-D Cameras

Kerl¹,

Souiai²,

Sturm³

et al. 2014

2014 2nd International Conference on 3D Vision

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Creating textured 3D scans of indoor environments has experienced a large boost with the advent of cheap commodity depth sensors. However, the quality of the acquired 3D models is often impaired by color seams in the reconstruction due to varying illumination (e.g., shadows or highlights) and object surfaces whose brightness and color vary with the viewpoint of the camera. In this paper, we propose a direct and simple method to estimate the pure albedo of the texture, which allows us to remove illumination effects from IR and color images. Our approach first computes the illumination-independent albedo in the IR domain, which we subsequently transfer to the color albedo. As shadows and highlights lead to over-and underexposed image regions with little or no color information, we apply an advanced optimization scheme to infer color information in the color albedo from neighboring image regions. We demonstrate the applicability of our approach to various real-world scenes.

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Section: Camera Noise Modelsupporting

confidence: 49%

“…Salamati et al [16] propose to capture a color and an infrared image to detect and remove shadows. However they use no active infrared illumination, but capture the ambient infrared light.…”

Section: Related Workmentioning

confidence: 99%

Towards Illumination-Invariant 3D Reconstruction Using ToF RGB-D Cameras

Kerl¹,

Souiai²,

Sturm³

et al. 2014

2014 2nd International Conference on 3D Vision

View full text Add to dashboard Cite

show abstract

“…In [3] the authors have developed a method to remove the shadows from real images based on probability shadow map. The probability shadow map identifies the amount of shadow that is affecting the surface.…”

Section: Related Workmentioning

confidence: 99%

“…Removing shadows from image is effective in simplifying the tasks of image processing and computer vision algorithms. However, removing the shadow has to maintain the information in the original image and the other details except the shadow [3].…”

Section: Introductionmentioning

confidence: 99%

Shadow Identification in Food Images using Extreme Learning Machine

Abdulateef¹,

Mahmuddin²,

Harun³

2017

ijacsa

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Abstract-Shadow identification is important for food images. Different applications require an accurate shadow identification or removal. A shadow varies from one image to another based on different factors such as lighting, colors, shape of objects, and their arrangement. This makes shadow identification complex problem and lacking systematic approach. Machine learning has high potential to be used for shadow recognition if it is used to train algorithms on wide number of scenarios. In this article, Extreme Learning Machine (ELM) has been used to identify shadow in shadow mask area. This shadow mask area was determined in the image based on edge detection, and morphological operations. ELM has been compared with Support Vector Machine (SVM) for shadow identification and shown better performance.

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“…Differences of light and scene reflections in the visible part of the electromagnetic spectrum (with wavelengths of 400-700 nm) and the NIR band (wavelength range of approximately 700-1100 nm) inspired researchers to use NIR information along with color images in tasks that are traditionally performed using color information only. Some of these applications are image denoising, 1 image dehazing, 2 shadow detection and removal, 3,4 and video conference relighting.…”

Section: Introductionmentioning

confidence: 99%

Gradient-based correction of chromatic aberration in the joint acquisition of color and near-infrared images

Sadeghipoor

Süsstrunk

2015

Digital Photography XI

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Chromatic aberration distortions such as wavelength-dependent blur are caused by imperfections in photographic lenses. These distortions are much more severe in the case of color and near-infrared joint acquisition, as a wider band of wavelengths is captured. In this paper, we consider a scenario where the color image is in focus, and the NIR image captured with the same lens and same focus settings is out-of-focus and blurred. To reduce chromatic aberration distortions, we propose an algorithm that estimates the blur kernel and deblurs the NIR image using the sharp color image as a guide in both steps. In the deblurring step, we retrieve the lost details of the NIR image by exploiting the sharp edges of the color image, as the gradients of color and NIR images are often correlated. However, differences of scene reflections and light in visible and NIR bands cause the gradients of color and NIR images to be different in some regions of the image. To handle this issue, our algorithm measures the similarities and differences between the gradients of the NIR and color channels. The similarity measures guide the deblurring algorithm to efficiently exploit the gradients of the color image in reconstructing high-frequency details of NIR, without discarding the inherent differences between these images. Simulation results verify the effectiveness of our algorithm, both in estimating the blur kernel and deblurring the NIR image, without producing ringing artifacts inherent to the results of most deblurring methods.

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Removing shadows from images using color and near-infrared

Cited by 28 publications

References 10 publications

Towards Illumination-Invariant 3D Reconstruction Using ToF RGB-D Cameras

Towards Illumination-Invariant 3D Reconstruction Using ToF RGB-D Cameras

Shadow Identification in Food Images using Extreme Learning Machine

Gradient-based correction of chromatic aberration in the joint acquisition of color and near-infrared images

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