Multi-task deep learning with incomplete training samples for the image-based prediction of variables describing silk fabrics

This paper presents a method for the classification of images of silk fabrics with the aim to predict properties such as the placeand time of origin and the production technique. The proposed method was developed in the context of the EU project SILKNOW(http://silknow.eu/). In the context of classification, we address the problem of limited as well as not fully labelled data andinvestigate the connection between the distinct variables. A pre-trained Convolutional Neural Network (CNN) is used for thefeature extraction and a classification network realizing Multi-task learning (MTL) is trained based on these features. The trainingprocedure is adapted to enable the consideration of images that do not have a label for all tasks. Additionally, MTL with fullylabeled training data is investigated for the classification of silk fabrics. The impact of both MTL approaches is compared to singletask learning based on two different class structures. We achieve overall accuracies of 92-95% and average F1-scores of 88-90% inour best experiments. © 2019 Authors

Automated delineation of roof planes from LIDAR data

In this paper, we describe an algorithm for roof line delineation from LIDAR data which aims at achieving models of a high level of detail. Roof planes are initially extracted by segmentation based on the local homogeneity of surface normal vectors of a digital surface model (DSM). A case analysis then reveals which of these roof planes intersect and which of them are separated by a step edge. The positions of the step edges are determined precisely by a new algorithm taking into account domain specific information. Finally, all step edges and intersection lines are combined to form consistent polyhedral models. In all phases of this workflow, decision making is based upon statistical reasoning about geometrical relations between neighbouring entities in order to reduce the number of control parameters and to increase the robustness of the method

Accurate matching and reconstruction of line features from ultra high resolution stereo aerial images

In this study, a new reconstruction approach is proposed for the line segments that are nearly-aligned(<= 10 degrees) with the epipolar line. The method manipulates the redundancy inherent in line pair-relations to generate artificial 3D point entities and utilize those entities during the estimation process to improve the height values of the reconstructed line segments. The best point entities for the reconstruction are selected based on a newly proposed weight function. To test the performance of the proposed approach, we selected three test patches over a built up area of the city of Vaihingen-Germany. Based on the results, the proposed approach produced highly promising reconstruction results for the line segments that are nearly-aligned with the epipolar line

Vision-based indoor localization via a visual slam approach

With an increasing interest in indoor location based services, vision-based indoor localization techniques have attracted many attentions from both academia and industry. Inspired by the development of simultaneous localization and mapping technique (SLAM), we present a visual SLAM-based approach to achieve a 6 degrees of freedom (DoF) pose in indoor environment. Firstly, the indoor scene is explored by a keyframe-based global mapping technique, which generates a database from a sequence of images covering the entire scene. After the exploration, a feature vocabulary tree is trained for accelerating feature matching in the image retrieval phase, and the spatial structures obtained from the keyframes are stored. Instead of querying by a single image, a short sequence of images in the query site are used to extract both features and their relative poses, which is a local visual SLAM procedure. The relative poses of query images provide a pose graph-based geometric constraint which is used to assess the validity of image retrieval results. The final positioning result is obtained by selecting the pose of the first correct corresponding image. © Authors 2019

Building Detection by Dempster-Shafer Fusion of LIDAR Data and Multispectral Aerial Imagery

A method for the classification of land cover in urban areas by the fusion of first and last pulse LIDAR data and multi-spectral images is presented. Apart from buildings, the classes "tree", "grass land", and "bare soil" are also distinguished by a classification method based on the theory of Dempster-Shafer for data fusion. Examples are given for a test site in Germany

Evaluation of a method for fusing LIDAR data and multispectral images for building detection

In this paper, we describe the evaluation of a method for building detection by the Dempster-Shafer fusion of LIDAR data and multispectral images. For that purpose, ground truth was digitised for two test sites with quite different characteristics. Using these data sets, the heuristic model for the probability mass assignments of the method is validated, and rules for the tuning of the parameters of this model are discussed. Further we evaluate the contributions of the individual cues used in the classification process to the quality of the classification results. Our results show the degree to which the overall correctness of the results can be improved by fusing LIDAR data with multispectral images

An iterative inference procedure applying conditional random fields for simultaneous classification of land cover and land use

Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a land cover layer and a land use layer. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result