Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration

Procedural function-based modelling of volumetric microstructures

We propose a new approach to modelling heterogeneous objects containing internal volumetric structures with size of details orders of magnitude smaller than the overall size of the object. The proposed function-based procedural representation provides compact, precise, and arbitrarily parameterised models of coherent microstructures, which can undergo blending, deformations, and other geometric operations, and can be directly rendered and fabricated without generating any auxiliary representations (such as polygonal meshes and voxel arrays). In particular, modelling of regular lattices and cellular microstructures as well as irregular porous media is discussed and illustrated. We also present a method to estimate parameters of the given model by fitting it to microstructure data obtained with magnetic resonance imaging and other measurements of natural and artificial objects. Examples of rendering and digital fabrication of microstructure models are presented

Personal Effects: The Social Character of Scholarly Writing

In Personal Effects, Holdstein and Bleich compile a volume that cuts across the grain of current orthodoxy. These editors and contributors argue that it is fundamental in humanistic scholarship to take account of the personal and collective experiences of scholars, researchers, critics, and teachers. They contend that humanistic inquiry cannot develop successfully at this time without reference to the varieties of subjective, intersubjective, and collective experience of teachers and researchers. In composition studies, they point out, an important strand of theory has continuously mined the personal experience of individual writers ( where they stand even in a destabilized sense of that idea). [S]uch substantive accounts of the \u27inner\u27 academic life provide appropriate and rich contexts for further study and analysis. With this volume, then, these scholars move us to explore the intersections of the social with subjectivity, with voice, ideology, and culture, and to consider the roles of these in the work of academics who study writing and literature. Taken together, the essays in this collection carry forward the idea that the personal, the candidly subjective and intersubjective, must be part of the subject of study in humanities scholarship. They propose an understanding of the personal in scholarship that is more helpful because more clearly anchored in human experience.https://digitalcommons.usu.edu/usupress_pubs/1131/thumbnail.jp

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D feature extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.This work has been partially supported by grant DPI2009-07144 from Ministerio de Ciencia e Innovacion of the Spanish Government and by the University of Alicante projects GRE09-16 and GRE10-35, and Valencia’s Government project GV/2011/034

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ENGL 830 Graduate Seminar: Rhetorical/Critical Theory - Critical Canons, Canonical Criticisms

Course syllabus for ENGL 830 Graduate Seminar: Rhetorical/Critical Theory - Critical Canons, Canonical Criticisms Course description: Focuses on extensive readings in rhetorical and/or critical theory

A study of a soft computing based method for 3D scenario reconstruction

Several recent works deal with 3D data in mobile robotic problems, e.g., mapping. Data comes from any kind of sensor (time of flight, Kinect or 3D lasers) that provide a huge amount of unorganized 3D data. In this paper we detail an efficient approach to build complete 3D models using a soft computing method, the Growing Neural Gas (GNG). As neural models deal easily with noise, imprecision, uncertainty or partial data, GNG provides better results than other approaches. The GNG obtained is then applied to a sequence. We present a comprehensive study on GNG parameters to ensure the best result at the lowest time cost. From this GNG structure, we propose to calculate planar patches and thus obtaining a fast method to compute the movement performed by a mobile robot by means of a 3D models registration algorithm. Final results of 3D mapping are also shown.This work has been supported by grant DPI2009-07144 from Ministerio de Ciencia e Innovacion of the Spanish Government, by the University of Alicante’s projects GRE09-16 and GRE10-35 and Valencian Government project GV/2011/034

Holdstein's Aromanna for dyspepsia, liver and kidney disease ...

Trade card advertising Holdstein's Aromanna, and Du Lac's Swiss Balsam, remedies prepared by G. Holdstein, Woodbury, N.J