Editing Soft Shadows in a Digital Photograph
IEEE Computer Graphics and Applications
Original paper: Weed image classification using Gabor wavelet and gradient field distribution
Computers and Electronics in Agriculture
Synthesizing Frontal Faces on Calibrated Stereo Cameras for Face Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
User-assisted intrinsic images
ACM SIGGRAPH Asia 2009 papers
International Journal of Web and Grid Services
GradientShop: A gradient-domain optimization framework for image and video filtering
ACM Transactions on Graphics (TOG)
Correlation-based intrinsic image extraction from a single image
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Computational Aesthetics'10 Proceedings of the Sixth international conference on Computational Aesthetics in Graphics, Visualization and Imaging
Estimation of intrinsic image sequences from image+depth video
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Image fusion based on bilateral sharpness criterion in DT-CWT domain
International Journal of Computational Vision and Robotics
Hi-index | 0.00 |
We propose a new technique for edge-suppressing operations on images. We introduce cross projection tensors to achieve affine transformations of gradient fields. We use these tensors, for example, to remove edges in one image based on the edge-information in a second image. Traditionally, edge suppression is achieved by setting image gradients to zero based on thresholds. A common application is in the Retinex problem, where the illumination map is recovered by suppressing the reflectance edges, assuming it is slowly varying. We present a class of problems where edge-suppression can be a useful tool. These problems involve analyzing images of the same scene under variable illumination. Instead of resetting gradients, the key idea in our approach is to derive local tensors using one image and to transform the gradient field of another image using them. Reconstructed image from the modified gradient field shows suppressed edges or textures at the corresponding locations. All operations are local and our approach does not require any global analysis. We demonstrate the algorithm in the context of several applications such as (a) recovering the foreground layer under varying illumination, (b) estimating intrinsic images in non-Lambertian scenes, (c) removing shadows from color images and obtaining the illumination map, and (d) removing glass reflections.