Detecting Momentary Shadows from Visible and Thermal Image Pair

Outdoor shadows can be classified into two categories: continuous shadows caused by static objects and momentary shadows caused by moving objects. Since the momentary shadows such as shadows due to a photographer are annoying and do not exist in the original scene, they should be detected and removed for improving image quality. In this paper, we propose a method for detecting momentary shadows from a visible and thermal image pair. The key idea of our proposed method is that the continuous shadows have lower temperature than non-shadow areas, while the momentary shadows have almost the same temperature as the non-shadow areas. Therefore, our method combines the shadow areas detected by using an RGB image and the higher-temperature areas detected by using a thermal image, and then detects the areas of momentary shadows via image segmentation. Through a number of experiments using real visible and thermal image pairs, we show that the combination of visible and thermal images are effective for detecting momentary shadows, and that our method works well for momentary shadows with varying duration time.

[1]  Nikos Paragios,et al.  Simultaneous Cast Shadows, Illumination and Geometry Inference Using Hypergraphs , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Rita Cucchiara,et al.  Improving shadow suppression in moving object detection with HSV color information , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[3]  Mohammed Bennamoun,et al.  Automatic Shadow Detection and Removal from a Single Image , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  David A. Forsyth,et al.  Rendering synthetic objects into legacy photographs , 2011, ACM Trans. Graph..

[5]  Brian C. Lovell,et al.  TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition , 2017, 2018 International Conference on Biometrics (ICB).

[6]  Ge Li,et al.  A New Shadow Removal Method Using Color-Lines , 2017, CAIP.

[7]  Cheng Lu,et al.  On the removal of shadows from images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Cheng Lu,et al.  Entropy Minimization for Shadow Removal , 2009, International Journal of Computer Vision.

[9]  Hassan Foroosh,et al.  Estimating Geo-temporal Location of Stationary Cameras Using Shadow Trajectories , 2008, ECCV.

[10]  Chunxia Xiao,et al.  ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[11]  Dimitris Samaras,et al.  Shadow Detection with Conditional Generative Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[12]  Le Hui,et al.  Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Takahiro Okabe,et al.  Spherical harmonics vs. Haar wavelets: basis for recovering illumination from cast shadows , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[14]  Chi-Keung Tang,et al.  Shadow Removal from Single RGB-D Images , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Harry Shum,et al.  Lazy snapping , 2004, ACM Trans. Graph..

[16]  Mohan M. Trivedi,et al.  Moving shadow and object detection in traffic scenes , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[17]  Katsushi Ikeuchi,et al.  Acquiring a Radiance Distribution to Superimpose Virtual Objects onto Real Scene , 2001, MVA.

[18]  Victor Alchanatis,et al.  Image fusion of visible and thermal images for fruit detection. , 2009 .

[19]  Alexei A. Efros,et al.  Estimating natural illumination from a single outdoor image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[21]  W DavisJames,et al.  Background-subtraction using contour-based fusion of thermal and visible imagery , 2007 .

[22]  P. Alam ‘T’ , 2021, Composites Engineering: An A–Z Guide.