Causally Regularized Learning with Agnostic Data Selection Bias

Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process. Moreover, in many scenarios, the testing data is not even available during the training process, which makes the traditional methods like transfer learning infeasible due to their need on prior of test distribution. Therefore, how to address the agnostic selection bias for robust model learning is of paramount importance for both academic research and real applications. In this paper, under the assumption that causal relationships among variables are robust across domains, we incorporate causal technique into predictive modeling and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm by jointly optimize global confounder balancing and weighted logistic regression. Global confounder balancing helps to identify causal features, whose causal effect on outcome are stable across domains, then performing logistic regression on those causal features constructs a robust predictive model against the agnostic bias. To validate the effectiveness of our CRLR algorithm, we conduct comprehensive experiments on both synthetic and real world datasets. Experimental results clearly demonstrate that our CRLR algorithm outperforms the state-of-the-art methods, and the interpretability of our method can be fully depicted by the feature visualization.Comment: Oral paper of 2018 ACM Multimedia Conference (MM'18

Characterizing the Uncertainty of the Fundamental Matrix

This paper deals with the analysis of the uncertainty of the fundamental matrix. The basic idea is to compute the fundamental matrix and its uncertainty in the same time. We shall show two different methods. The first one is a statistical approach. As in all statistical methods the precision of the results depends on the number of analyzed samples. This means that we can always improve our results if we increase the number of samples but this process is very time consuming. We propose a much simpler method which gives results which are close to the results of the statistical methods. At the end of paper we shall show some experimental results obtained with synthetic and real data

3-D Reconstruction of Urban Scenes from Sequences of Images

In this paper, we address the problem of the recovery of the Euclidean geometry of a scene from a sequence of images without any prior knowledge either about the parameters of the cameras, or about the motion of the camera(s). We do not require any knowledge of the absolute coordinates of some control points in the scene to achieve this goal. Using various computer vision tools, we establish correspondences between images and recover the epipolar geometry of the set of images, from which we show how to compute the complete set of perspective projection matrices for each camera position. These being known, we proceed to reconstruct the scene. This reconstruction is defined up to an unknown projective transformation (i.e. is parameterized with 15 arbitrary parameters). Next we show how to go from this reconstruction to a more constrained class of reconstructions, defined up to an unknown affine transformation (i.e. parameterized with 12 arbitrary parameters) by exploiting known geometr..

A Bayesian approach to spread spectrum watermark detection and secure copyright protection for digital image libraries

Digital watermarks have been proposed as a method for discouraging illicit copying and distribution of copyrighted material � and to create secure digital im� age libraries by adding to images copyright and user� right information. Using a robust digital watermark to detect and trace copyright violations has therefore lot of interest. This paper describes an approach to em� bedding a digital watermark using the Fourier trans� form. The paper also addresses the di�cult problem of oblivious watermark detection. It is shown that� for the CDMA spread spectrum signal described in the paper � it is still possible to positively detect the pres� ence of a watermark without being able to decode it �and even infer the number of bits contained in the watermark � given only the key used to generate it. Fi� nally � through experimental results the usefulness of such measure is shown.

Finding the Collineation Between two Projective Reconstructions

The problem of finding the collineation between two 3-D projective reconstructions has been proved to be useful for a variety of tasks such as calibration of a stereo rig and 3-D affine and/or Euclidean reconstruction. Moreover such a collineation may well be viewed as a point transfer method between two image pairs with applications to visually guided robot control. In spite of this potential, methods for properly estimating such a projective transformation have received little attention in the past. In this paper we describe linear, non-linear and robust methods for estimating this transformation. We test the numerical stability of these methods with respect to image noise and to the number of matched points. Finally we briefly describe three applications: stereo image transfer, Euclidean reconstruction, and self calibration of a stereoscopic camera pair