scholarly journals A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging

2021 ◽  
Vol 11 (23) ◽  
pp. 11494
Author(s):  
Gilberto Alvarado-Robles ◽  
Francisco J. Solís-Muñoz ◽  
Marco A. Garduño-Ramón ◽  
Roque A. Osornio-Ríos ◽  
Luis A. Morales-Hernández
Keyword(s):  
Urban Areas ◽  
Color Space ◽  
Shadow Removal ◽  
Color Transfer ◽  
Local Color ◽  
Qualitative Comparison ◽  
Color Tuning ◽  
Hsv Color Space ◽  
Aerial Imaging ◽  
Texture Preservation

Through the increasing use of unmanned aerial vehicles as remote sensing tools, shadows become evident in aerial imaging; this fact, alongside the higher spatial resolution obtained by high-resolution mounted cameras, presents a challenging issue when performing different image processing tasks related to urban areas monitoring. Accordingly, the state-of-the-art reported works can correct the shadow regions, but the heterogeneity between the corrected shadow and non-shadow areas is still evident and especially noticeable in concrete and asphalt regions. The present work introduces a local color transfer methodology to shadow removal which is based on the CIE L*a*b (Lightness, a and b) color space that considers chromatic differences in urban regions, and it is followed by a color tuning using the HSV color space. The quantitative comparison was executed by using the shadow standard deviation index (SSDI), where the proposed work provided low values that improve up to 19 units regarding other tested methods. The qualitative comparison was visually realized and proved that the proposed method enhances the color correspondence without losing texture information. Quantitative and qualitative results validate the results of color correction and texture preservation accuracy of the proposed method against other published methodologies.

scholarly journals Automatic Shadow Removal by Illuminance in HSV Color Space

2015 ◽  
Vol 3 (3) ◽  
pp. 70-75 ◽  
Author(s):  
Wenbo Huang ◽  
KyoungYeon Kim ◽  
Yong Yang ◽  
Yoo-Sung Kim
Keyword(s):  
Color Space ◽  
Shadow Removal ◽  
Hsv Color Space

Cast Shadow Removal in a Real-Time Environment

2013 ◽  
Vol 275-277 ◽  
pp. 2548-2554
Author(s):  
Hong Ying Zhang ◽  
Hong Li ◽  
Yi Gang Sun
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Space ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Shadow Removal ◽  
Hsv Color Space ◽  
Cast Shadow ◽  
Novel Algorithm

The cast shadows on the background of the object will distinctly affect the recognition of the foreground objects. Due to the limitation of shadow removal methods utilizing texture, a novel algorithm based on Gaussian Mixture Model (GMM) and HSV color space is proposed. Firstly, moving regions are detected using GMM. Secondly, we make two pre-classifiers accurate and adaptive to the change of shadow by using the features of shadow in RGB and HSV color space. Experimental results show that the proposed method is efficient and robust.

Moving Shadow Removal Algorithm Based on HSV Color Space

2014 ◽  
Vol 12 (4) ◽  
Author(s):  
Xinbo Zhang ◽  
Kunpeng Liu ◽  
Xiaoling Wang ◽  
Changhong Yu ◽  
Tao Zhang
Keyword(s):  
Color Space ◽  
Shadow Removal ◽  
Hsv Color Space

HSV color space based pupil position locating method

2014 ◽  
Author(s):  
Peng Cao ◽  
Qijie Zhao ◽  
Dawei Tu ◽  
Hui Shao
Keyword(s):  
Color Space ◽  
Hsv Color Space

FOREGROUND IMAGE EXTRCTION IN THE HSV COLOR SPACE

2010 ◽  
Vol 7 (7) ◽  
pp. 1-4
Author(s):  
Jyh-Yeong Chang ◽  
Jia-Jye Shyu ◽  
Yi-Cheng Luo
Keyword(s):  
Color Space ◽  
Hsv Color Space

scholarly journals Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

2021 ◽  
Vol 13 (4) ◽  
pp. 699
Author(s):  
Tingting Zhou ◽  
Haoyang Fu ◽  
Chenglin Sun ◽  
Shenghan Wang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Mean Shift ◽  
Color Space ◽  
Compensation Method ◽  
Shadow Detection ◽  
Processing Unit ◽  
Shadow Region ◽  
Remote Sensing Images ◽  
Shadow Compensation

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

scholarly journals 3D Reconstruction with Single-Shot Structured Light RGB Line Pattern

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4819
Author(s):  
Yikang Li ◽  
Zhenzhou Wang
Keyword(s):  
3D Reconstruction ◽  
Moving Objects ◽  
Structured Light ◽  
Color Space ◽  
Reconstruction Technique ◽  
Single Shot ◽  
Line Pattern ◽  
Hsv Color Space ◽  
Phase Map ◽  
Line Segmentation

Single-shot 3D reconstruction technique is very important for measuring moving and deforming objects. After many decades of study, a great number of interesting single-shot techniques have been proposed, yet the problem remains open. In this paper, a new approach is proposed to reconstruct deforming and moving objects with the structured light RGB line pattern. The structured light RGB line pattern is coded using parallel red, green, and blue lines with equal intervals to facilitate line segmentation and line indexing. A slope difference distribution (SDD)-based image segmentation method is proposed to segment the lines robustly in the HSV color space. A method of exclusion is proposed to index the red lines, the green lines, and the blue lines respectively and robustly. The indexed lines in different colors are fused to obtain a phase map for 3D depth calculation. The quantitative accuracies of measuring a calibration grid and a ball achieved by the proposed approach are 0.46 and 0.24 mm, respectively, which are significantly lower than those achieved by the compared state-of-the-art single-shot techniques.

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