Depth-based descriptor for matching keypoints in 3D scenes

Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation and analysis of biomedical images. Successful implementation of higher level image analysis tasks, however, is conditioned by reliable detection of characteristic image local regions termed keypoints. A large number of keypoint detection algorithms has been proposed and verified. In this paper we discuss the most important keypoint detection algorithms. The main part of this work is devoted to description of a keypoint detection algorithm we propose that incorporates depth information computed from stereovision cameras or other depth sensing devices. It is shown that filtering out keypoints that are context dependent, e.g. located at boundaries of objects can improve the matching performance of the keypoints which is the basis for object recognition tasks. This improvement is shown quantitatively by comparing the proposed algorithm to the widely accepted SIFT keypoint detector algorithm. Our study is motivated by a development of a system aimed at aiding the visually impaired in space perception and object identification

Predicting worsted spinning performance with an artificial neural network model

For a given fiber spun to pre-determined yarn specifications, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills, and then generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for predicting worsted spinning performance with an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for predicting spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan YarnspecTM). The ANN is then subsequently trained with commercial mill data to assess the feasibility of the method as a mill-specific performance prediction tool. Incorporating mill-specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately. <br /

The eukaryotic linear motif resource ELM: 10 years and counting

The eukaryotic linear motif (ELM http://elm.eu.org) resource is a hub for collecting, classifying and curating information about short linear motifs (SLiMs). For >10 years, this resource has provided the scientific community with a freely accessible guide to the biology and function of linear motifs. The current version of ELM contains ∼200 different motif classes with over 2400 experimentally validated instances manually curated from >2000 scientific publications. Furthermore, detailed information about motif-mediated interactions has been annotated and made available in standard exchange formats. Where appropriate, links are provided to resources such as switches.elm.eu.org and KEGG pathways.Fil: Dinkel, Holder. European Molecular Biology Laboratory; AlemaniaFil: Van Roey, Kim. European Molecular Biology Laboratory; AlemaniaFil: Michael, Sushama. European Molecular Biology Laboratory; AlemaniaFil: Davey, Norman E.. University Of California ; Estados UnidosFil: Weatheritt, Robert J.. MRC. Laboratory of Molecular Biology; Estados UnidosFil: Born, Diana. Ruprecht-Karls-Universität; AlemaniaFil: Speck, Tobias. Ruprecht-Karls-Universität; AlemaniaFil: Kruger, Daniel. Ruprecht-Karls-Universität; AlemaniaFil: Grebnev, Gleb. University College Dublin; IrlandaFil: Kuban, Marta. Maria Sklodowska-Curie Memorial Cancer Center. Laboratory of Bioinformatics and Biostatistics; PoloniaFil: Strumillo, Marta. Maria Sklodowska-Curie Memorial Cancer Center. Laboratory of Bioinformatics and Biostatistics; PoloniaFil: Uyar, Bora. European Molecular Biology Laboratory; AlemaniaFil: Budd, Aidan. European Molecular Biology Laboratory; AlemaniaFil: Altenberg, Brigitte. European Molecular Biology Laboratory; AlemaniaFil: Seiler, Markus. European Molecular Biology Laboratory; AlemaniaFil: Chemes, Lucia Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires; Argentina. Fundación Instituto Leloir; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Glavina, Juliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Sánchez Miguel, Ignacio Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Diella, Francesca. European Molecular Biology Laboratory; AlemaniaFil: Gibson, Toby J. European Molecular Biology Laboratory; Alemani

Towards Real-Time Control of a Semibatch Crystallization Process by Electrical and Ultrasound Tomographic Techniques

This research work presents a feasibility study to demonstrate the application of Electrical Resistance Tomography and transmission-based Ultrasound Computed Tomography for monitoring and control of micron-sized calcium carbonate crystallization process. Herein, precipitated calcium carbonate production is bind to a carbon dioxide absorption process based on hollow-fiber membrane contactor.ERT acquisition system is equipped with 16 electrodes with operating frequency of 156 KHz and image capturing frame rate of 2 Hz. The ultrasound tomography equipment consists of 32 piezoelectric transducers at a frequency of 200 KHz. These sensors are sensitive to changes in suspension density and conductivity. Furthermore, a process control framework is developed by utilizing the fundamental relations of settling velocity of particles. Through simulations in the LabVIEW software, the PI-based feedback controller demonstrates a possibility of setpoint tracking by manipulating the control variable (mixing speed). Upon further investigations, this approach can be used as a multi-dimensional process analytical technology tool for quality assurance and malfunction diagnosis when out-of-specification events occur throughout the entire process

Monitoring and Visualization of Crystallization Processes Using Electrical Resistance Tomography: CaCO3 and Sucrose Crystallization Case Studies

In the current research work, electrical resistance tomography (ERT) was employed for monitoring and visualization of crystallization processes. A first-of-its-kind MATLAB-based interactive GUI application "ERT-Vis" is presented. Two case studies involving varied crystallization methods were undertaken. The experiments were designed and performed involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media, and the cooling crystallization of sucrose representing the lower conductivity solution-solute combination. The software successfully provided key insights regarding the process in both crystallization systems. It could detect and separate the solid concentration distributions in the low as well as high conductivity solutions using the visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps by reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data