Using Self-Contradiction to Learn Confidence Measures in Stereo Vision

Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that involves manual interaction, active sensing devices and/or synthetic scenes. To overcome this problem, we propose a new, flexible, and scalable way for generating training data that only requires a set of stereo images as input. The key idea of our approach is to use different view points for reasoning about contradictions and consistencies between multiple depth maps generated with the same stereo algorithm. This enables us to generate a huge amount of training data in a fully automated manner. Among other experiments, we demonstrate the potential of our approach by boosting the performance of three learned confidence measures on the KITTI2012 dataset by simply training them on a vast amount of automatically generated training data rather than a limited amount of laser ground truth data.Comment: This paper was accepted to the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. The copyright was transfered to IEEE (https://www.ieee.org). The official version of the paper will be made available on IEEE Xplore (R) (http://ieeexplore.ieee.org). This version of the paper also contains the supplementary material, which will not appear IEEE Xplore (R

Building Block and Building Rule: Dual Descriptor Method for Biological Sequence Analysis

The emergence of “Systems Biology” in recent years highlights the systematic viewpoint of bio-system modeling. Building on such a background, Dual Descriptor Method, a generic methodology for biological sequence analysis is proposed. From a systematic perspective, Dual Descriptor is defined as a two element set of Composition Weight Map and Position Weight Function which aim at reflecting the composition and permutation information of a sequence. An alternate training algorithm is provided to get an optimum description of the building patterns of the sequences. In this paper, dual descriptor method has been applied to the analysis of two typical problems of molecular biology: gene identification and the prediction of protein function. Satisfactory and insightful results are achieved. Owing to the generality of this methodology, dual descriptor method has wide application perspective for many problems of pattern recognition, especially those involved in “Systems Biology”