Development of a mechatronic sorting system for removing contaminants from wool

Automated visual inspection (AVI) systems have been extended to many fields, such as agriculture and the food, plastic and textile industries. Generally, most visual systems only inspect product defects, and then analyze and grade them due to the lack of any sorting function. This main reason rests with the difficulty of using the image data in real time. However, it is increasingly important to either sort good products from bad or grade products into separate groups usingAVI systems. This article describes the development of a mechatronic sorting system and its integration with a vision system for automatically removing contaminants from wool in real time. The integration is implemented by a personal computer, which continuously processes live images under the Windows 2000 operating system. The developed real-time sorting approach is also applicable to many other AVI systems

Local wavelet features for statistical object classification and localisation

This article presents a system for texture-based probabilistic classification and localisation of 3D objects in 2D digital images and discusses selected applications. The objects are described by local feature vectors computed using the wavelet transform. In the training phase, object features are statistically modelled as normal density functions. In the recognition phase, a maximisation algorithm compares the learned density functions with the feature vectors extracted from a real scene and yields the classes and poses of objects found in it. Experiments carried out on a real dataset of over 40000 images demonstrate the robustness of the system in terms of classification and localisation accuracy. Finally, two important application scenarios are discussed, namely classification of museum artefacts and classification of metallography images

Texture wear analysis in textile floor coverings by using depth information

Considerable industrial and academic interest is addressed to automate the quality inspection of textile floor coverings, mostly using intensity images. Recently, the use of depth information has been explored to better capture the 3D structure of the surface. In this paper, we present a comparison of features extracted from three texture analysis techniques. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show that the use of Local Binary Patterns techniques result in a better ranking of the wear labels as well as in a higher amount of discrimination between features related to consecutive degrees of wear

User-Centered Comparison of Web Search Tools

This study explores a user-centered approach to the comparative evaluation of the Web search tool ProThes against popular all-purpose search engines Yandex and Google. An original research design was developed. Data were collected from 12 volunteers who performed 48 search tasks in total. Main outcomes include: (1) search strategy supported through ProThes can be quite effective for focused Web search and (2) ProThes’ interface and system performance must be improved.The research was supported in part by the Russian Fund of Basic Research, grant # 03-07-90342

3D Reconstruction: Novel Method for Finding of Corresponding Points using Pseudo Colors

This paper deals with the reconstruction of spatial coordinates of an arbitrary point in a scene using two images scanned by a 3D camera or two displaced cameras. Calculations are based on the perspective geom-etry. Accurate determination of corresponding points is a fundamental step in this process. The usually used methods can have a problem with points, which lie in areas without sufficient contrast. This paper describes our proposed method based on the use of the relationship between the selected points and area feature points. The proposed method finds correspondence using a set of feature points found by SURF. An algorithm is proposed and described for quick removal of false correspondences, which could ruin the correct reconstruction. The new method, which makes use of pseudo color image representation (pseudo coloring) has been proposed subsequently. By means of this method it is possible to significantly increase the color contrast of the surveyed image, and therefore add more information to find the correct correspondence. Reliability of the found correspondence can be verified by reconstruction of 3D position of selected points. Executed experiments confirm our assumption

Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection

Semidefinite Programming (SDP) and Sums-of-Squares (SOS) relaxations have led to certifiably optimal non-minimal solvers for several robotics and computer vision problems. However, most non-minimal solvers rely on least-squares formulations, and, as a result, are brittle against outliers. While a standard approach to regain robustness against outliers is to use robust cost functions, the latter typically introduce other non-convexities, preventing the use of existing non-minimal solvers. In this paper, we enable the simultaneous use of non-minimal solvers and robust estimation by providing a general-purpose approach for robust global estimation, which can be applied to any problem where a non-minimal solver is available for the outlier-free case. To this end, we leverage the Black-Rangarajan duality between robust estimation and outlier processes (which has been traditionally applied to early vision problems), and show that graduated non-convexity (GNC) can be used in conjunction with non-minimal solvers to compute robust solutions, without requiring an initial guess. Although GNC's global optimality cannot be guaranteed, we demonstrate the empirical robustness of the resulting robust non-minimal solvers in applications, including point cloud and mesh registration, pose graph optimization, and image-based object pose estimation (also called shape alignment). Our solvers are robust to 70-80% of outliers, outperform RANSAC, are more accurate than specialized local solvers, and faster than specialized global solvers. We also propose the first certifiably optimal non-minimal solver for shape alignment using SOS relaxation.Comment: 10 pages, 5 figures, published at IEEE Robotics and Automation Letters (RA-L), 2020, Best Paper Award in Robot Vision at ICRA 202

Shape matching by curve modelling and alignment

Automatic information retrieval in the eld of shape recognition has been widely covered by many research elds. Various techniques have been developed using different approaches such as intensity-based, modelbased and shape-based methods. Whichever is the way to represent the objects in images, a recognition method should be robust in the presence of scale change, translation and rotation. In this paper we present a new recognition method based on a curve alignment technique, for planar image contours. The method consists of various phases including extracting outlines of images, detecting signicant points and aligning curves. The dominant points can be manually or automatically detected. The matching phase uses the idea of calculating the overlapping indices between shapes as similarity measures. To evaluate the effectiveness of the algorithm, two databases of 216 and 99 images have been used. A performance analysis and comparison is provided by precision-recall curves