Neural Face Editing with Intrinsic Image Disentangling

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte. We show that this network can be trained on "in-the-wild" images by incorporating an in-network physically-based image formation module and appropriate loss functions. Our disentangling latent representation allows for semantically relevant edits, where one aspect of facial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number of facial editing applications.Comment: CVPR 2017 ora

A target dependent colorspace for robust tracking

Presentado al 18th International Conference on Pattern Recognition (ICPR)celebrado en 2006 en Hong Kong (China).The selection of the appropriate colorspace for tracking applications has not been an issue previously considered in the literature. Many color representations have been suggested, based on the invariance to illumination changes. Nevertheless, none of them is invariant enough to deal with general and unconstrained environments. In tracking tasks, we might prefer to represent image pixels into a colorspace where the distance between the target and background colorpoints were maximized, simplifying the task of the tracker. Based on this criterion, we propose an 'object dependent' colorspace, which is computed as a simple calibration procedure before tracking. Furthermore, this colorspace may be easily adapted at each frame. Synthetic and real experiments show how this colorspace allows for a better discrimination of the foreground and background, and permits to track in circumstances where the same tracking algorithm relying on other colorspaces would fail.This work was supported by the project 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).This work was supported by CICYT project DPI2004-05414 from the Spanish Ministry of Science and Technology.Peer Reviewe

Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN