Detecting Specular Reflections and Cast Shadows to Estimate Reflectance and Illumination of Dynamic Indoor Scenes

Jiddi, Salma; Robert, Philippe; Marchand, Éric

doi:10.1109/tvcg.2020.2976986

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…The Max-ill method directly selects the area with the maximum illumination as the specular reflection point. The method of Jiddi et al (2022) calculates the vector of each highlight region to recover the light source direction. The method of Yi et al (2014) calculates the illuminant directions of the luminance component's three local regions, which meet the requirements of lower complexity and larger average gray value, and synthesize them as the final illuminant direction.…”

Section: Estimation Of Reflected Angle 431 Static Scenariomentioning

confidence: 99%

Remote photoplethysmography based on reflected light angle estimation

Fan,

Liu,

Zhang

et al. 2024

Physiol. Meas.

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Efficient non-contact heart rate detection has received much attention in the field of health testing. In previous studies on factors affecting the accuracy of remote photoplethysmography(rPPG) detection, light intensity, facial reflection angle, and motion artifacts have been investigated, but interference from specularly reflected light has not been studied. In this paper, a total of 84 videos were first acquired from four different angles and used to study the effect of specularly reflected light on the accuracy of heart rate estimation. Then we proposed a reflected light angle estimation method which firstly segments the face into 71 regions, and then extracts the angle and illumination of each region as possible reflected light directions and weights respectively. Secondly, all sample points are fitted by sample interpolation, and the angle value corresponding to the maximum value of the weight in the fitted curve is selected as the estimated angle of reflected light direction. Finally, the reflected light angle value is used as a reference for (region of interest) ROI selection to improve the quality of the obtained (image photoplethysmography) iPPG signal. We find that the accuracy of the estimated heart rate and the SNR of the signal are higher when the reflected light angle is larger. The overall root mean squared error (RMSE), standard deviation (SD) and mean error(MAE) of our proposed reflected light angle estimation method are 2.085, 1.745 and 0.7225, which not only have high accuracy but also have good stability. Our proposed method has low error and is superior to existing methods by comparison in publicly available databases.

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Section: Estimation Of Reflected Angle 431 Static Scenariomentioning

confidence: 99%

Remote photoplethysmography based on reflected light angle estimation

Fan,

Liu,

Zhang

et al. 2024

Physiol. Meas.

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“…Regarding the automatic assessment of illumination artifacts on images, a recent literature review [16] highlighted the crucial impact of shadows on the performance of computer vision applications, which may decrease its performance due to loss or distortion of objects in the acquired image. Additionally, in [17,18], the importance of detecting reflections on image-based solutions is emphasized, since, despite the valuable information that specular reflections may add, their presence is usually a drawback for image segmentation tasks.…”

Section: Related Workmentioning

confidence: 99%

“…An ML pipeline was developed to ensure that the acquired images did not present any illumination artifacts such as shadows or reflections, a key factor to ensure the quality and adequacy of the acquired trap images [16][17][18]. The main goal of this module is to avoid the use of trap images without suitable and homogeneous illumination, which might compromise not only posterior automated steps like trap segmentation or perspective correction, but also key tasks like the correct identification of insects for pest monitoring purposes.…”

Section: Shadows and Reflections Assessment Pipelinementioning

confidence: 99%

AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment

Faria

Nogueira²,

Ferreira

et al. 2021

Agronomy

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The increasing alarming impacts of climate change are already apparent in viticulture, with unexpected pest outbreaks as one of the most concerning consequences. The monitoring of pests is currently done by deploying chromotropic and delta traps, which attracts insects present in the production environment, and then allows human operators to identify and count them. While the monitoring of these traps is still mostly done through visual inspection by the winegrowers, smartphone image acquisition of those traps is starting to play a key role in assessing the pests’ evolution, as well as enabling the remote monitoring by taxonomy specialists in better assessing the onset outbreaks. This paper presents a new methodology that embeds artificial intelligence into mobile devices to establish the use of hand-held image capture of insect traps for pest detection deployed in vineyards. Our methodology combines different computer vision approaches that improve several aspects of image capture quality and adequacy, namely: (i) image focus validation; (ii) shadows and reflections validation; (iii) trap type detection; (iv) trap segmentation; and (v) perspective correction. A total of 516 images were collected, divided into three different datasets and manually annotated, in order to support the development and validation of the different functionalities. By following this approach, we achieved an accuracy of 84% for focus detection, an accuracy of 80% and 96% for shadows/reflections detection (for delta and chromotropic traps, respectively), as well as mean Jaccard index of 97% for the trap’s segmentation.

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“…Recently, Jiddi et al [18] demonstrated illumination estimation using both specular paths and shadow information. Specular paths are a useful cue for estimating incident illumination [16,31] potentially "in the wild," but suitable surfaces must be present in the scene.…”

Section: Incident Illumination Estimationmentioning

confidence: 99%

Objects as Cameras: Estimating High-Frequency Illumination from Shadows

Swedish

Henley

Raskar

2021

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

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We recover high-frequency information encoded in the shadows cast by an object to estimate a hemispherical photograph from the viewpoint of the object, effectively turning objects into cameras. Estimating environment maps is useful for advanced image editing tasks such as relighting, object insertion or removal, and material parameter estimation. Because the problem is ill-posed, recent works in illumination recovery have tackled the problem of lowfrequency lighting for object insertion, rely upon specular surface materials, or make use of data-driven methods that are susceptible to hallucination without physically plausible constraints. We incorporate an optimization scheme to update scene parameters that could enable practical capture of real-world scenes. Furthermore, we develop a methodology for evaluating expected recovery performance for different types and shapes of objects.

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Detecting Specular Reflections and Cast Shadows to Estimate Reflectance and Illumination of Dynamic Indoor Scenes

Cited by 13 publications

References 29 publications

Remote photoplethysmography based on reflected light angle estimation

Remote photoplethysmography based on reflected light angle estimation

AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment

Objects as Cameras: Estimating High-Frequency Illumination from Shadows

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