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

On the use of uavs in mining and archaeology - geo-accurate 3d reconstructions using various platforms and terrestrial views

During the last decades photogrammetric computer vision systems have been well established in scientific and commercial applications. Especially the increasing affordability of unmanned aerial vehicles (UAVs) in conjunction with automated multi-view processing pipelines have resulted in an easy way of acquiring spatial data and creating realistic and accurate 3D models. With the use of multicopter UAVs, it is possible to record highly overlapping images from almost terrestrial camera positions to oblique and nadir aerial images due to the ability to navigate slowly, hover and capture images at nearly any possible position. Multi-copter UAVs thus are bridging the gap between terrestrial and traditional aerial image acquisition and are therefore ideally suited to enable easy and safe data collection and inspection tasks in complex or hazardous environments. In this paper we present a fully automated processing pipeline for precise, metric and geo-accurate 3D reconstructions of complex geometries using various imaging platforms. Our workflow allows for georeferencing of UAV imagery based on GPS-measurements of camera stations from an on-board GPS receiver as well as tie and control point information. Ground control points (GCPs) are integrated directly in the bundle adjustment to refine the georegistration and correct for systematic distortions of the image block. We discuss our approach based on three different case studies for applications in mining and archaeology and present several accuracy related analyses investigating georegistration, camera network configuration and ground sampling distance. Our approach is furthermore suited for seamlessly matching and integrating images from different view points and cameras (aerial and terrestrial as well as inside views) into one single reconstruction. Together with aerial images from a UAV, we are able to enrich 3D models by combining terrestrial images as well inside views of an object by joint image processing to generate highly detailed, accurate and complete reconstructions

Food Recognition using Fusion of Classifiers based on CNNs

With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years. The best results have been obtained using methods based on very deep convolutional neural networks, which show that the deeper the model,the better the classification accuracy will be obtain. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on different convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on two public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent CNN models

Investigation of marmoset hybrids (Cebuella pygmaea x Callithrix jacchus) and related Callitrichinae (Platyrrhini) by cross-species chromosome painting and comparative genomic hybridization

We report on the cytogenetics of twin offspring from an interspecies cross in marmosets (Callitrichinae, Platyrrhini), resulting from a pairing between a female Common marmoset (Callithrix jacchus, 2n = 46) and a male Pygmy marmoset (Cebuella pygmaea, 2n = 44). We analyzed their karyotypes by multi-directional chromosome painting employing human, Saguinus oedipus and Lagothrix lagothricha chromosome-specific probes. Both hybrid individuals had a karyotype with a diploid chromosome number of 2n = 45. As a complementary tool, interspecies comparative genomic hybridization (iCGH) was performed in order to screen for genomic imbalances between the hybrids and their parental species, and between Callithrix argentata and S. oedipus, respectively. Copyright (C) 2005 S. Karger AG, Basel