A computer vision approach for weeds identification through Support Vector Machines

This paper outlines an automatic computervision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the SupportVectorMachines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the SupportVectorMachines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies

Accuracy Evaluation of Dense Matching Techniques for Casting Part Dimensional Verification

Product optimization for casting and post-casting manufacturing processes is becoming compulsory to compete in the current global manufacturing scenario. Casting design, simulation and verification tools are becoming crucial for eliminating oversized dimensions without affecting the casting component functionality. Thus, material and production costs decrease to maintain the foundry process profitable on the large-scale component supplier market. New measurement methods, such as dense matching techniques, rely on surface texture of casting parts to enable the 3D dense reconstruction of surface points without the need of an active light source as usually applied with 3D scanning optical sensors. This paper presents the accuracy evaluation of dense matching based approaches for casting part verification. It compares the accuracy obtained by dense matching technique with already certified and validated optical measuring methods. This uncertainty evaluation exercise considers both artificial targets and key natural points to quantify the possibilities and scope of each approximation. Obtained results, for both lab and workshop conditions, show that this image data processing procedure is fit for purpose to fulfill the required measurement tolerances for casting part manufacturing processes.This research was partially funded by ESTRATEUS project (Reference IE14-396). given are accurate and use the standard spelling of funding agency names at https://search.crossref.org/funding, any errors may affect your future funding

Close Range Photogrammetry for Direct Multiple Feature Positioning Measurement without Targets

The main objective of this study is to present a new method to carry out measurements so as to improve the positioning verification step in the wind hub part dimensional validation process. This enhancement will speed up the measuring procedures for these types of parts. An industrial photogrammetry based system was applied to take advantage of its results, and new functions were added to existing capabilities. In addition to a new development based on photogrammetry modelling and image processing, a measuring procedure was defined based on optical and vision system considerations. A validation against a certified procedure by means of a laser-tracker has also been established obtaining deviations of ±0.125 μm/m

Robust 3D Object Model Reconstruction and Matching for Complex Automated Deburring Operations

The deburring processes of parts with complex geometries usually present many challenges to be automated. This paper outlines the machine vision techniques involved in the design and set up of an automated adaptive cognitive robotic system for laser deburring of metal casting complex 3D high quality parts. To carry out deburring process operations of the parts autonomously, 3D machine vision techniques have been used for different purposes, explained in this paper. These machine vision algorithms used along with industrial robots and a high tech laser head, make a fully automated deburring process possible. This setup could potentially be applied to medium sized parts of different light casting alloys (Mg, AlZn, etc.)

Robotic solutions for Footwear Industry

Since September 2010, the ROBOFOOT consortium, a group of 10 partners, including Footwear Industry, Research institutes and Robotic solution providers is working together to promote the introduction of robotics in the European Footwear Manufacturing Industry. This paper presents the initial results achieved, in particular they are described the user requirements and operations selected and the technical achievements reached so far. The approach followed allows the coexistence of current working practices and facilities with robotic solutions. Due to the nature of the industry, either in size and financial capability, it has been identified as one of the requirements by end-users taking part in the project.Peer reviewe

On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures

Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are trained using only defect free textured images for each type of analyzed texture, labeling the samples for the SVMs in an automatic way. This work is based on two image processing streams using image sensors: (1) the CA first processes the incoming image from the input to the output, producing a reconstructed image, from which a measurement of correct or defective image is obtained; (2) the second process uses the latent layer information as input to the SVM to produce a measurement of classification. Both measurements are effectively combined, making an additional research contribution. The results obtained achieve a percentage of success of 92% on average, outperforming results of previous works

Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices

The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art

Natural multimodal communication for human-robot collaboration

Publisher Copyright: © The Author(s) 2017.This article presents a semantic approach for multimodal interaction between humans and industrial robots to enhance the dependability and naturalness of the collaboration between them in real industrial settings. The fusion of several interaction mechanisms is particularly relevant in industrial applications in which adverse environmental conditions might affect the performance of vision-based interaction (e.g. poor or changing lighting) or voice-based interaction (e.g. environmental noise). Our approach relies on the recognition of speech and gestures for the processing of requests, dealing with information that can potentially be contradictory or complementary. For disambiguation, it uses semantic technologies that describe the robot characteristics and capabilities as well as the context of the scenario. Although the proposed approach is generic and applicable in different scenarios, this article explains in detail how it has been implemented in two real industrial cases in which a robot and a worker collaborate in assembly and deburring operations.Peer reviewe

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans—anteaters, sloths, and armadillos—have essential functions forecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosys-tem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts withdomestic dogs, these species have been threatened locally, regionally, or even across their fulldistribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths.Our data set includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae(3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data onDasypus pilo-sus(Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized,but new genetic studies have revealed that the group is represented by seven species. In thisdata paper, we compiled a total of 42,528 records of 31 species, represented by occurrence andquantitative data, totaling 24,847 unique georeferenced records. The geographic range is fromthe southern United States, Mexico, and Caribbean countries at the northern portion of theNeotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regardinganteaters,Myrmecophaga tridactylahas the most records (n=5,941), andCyclopessp. havethe fewest (n=240). The armadillo species with the most data isDasypus novemcinctus(n=11,588), and the fewest data are recorded forCalyptophractus retusus(n=33). Withregard to sloth species,Bradypus variegatushas the most records (n=962), andBradypus pyg-maeushas the fewest (n=12). Our main objective with Neotropical Xenarthrans is to makeoccurrence and quantitative data available to facilitate more ecological research, particularly ifwe integrate the xenarthran data with other data sets of Neotropical Series that will become available very soon (i.e., Neotropical Carnivores, Neotropical Invasive Mammals, andNeotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure,habitat loss, fragmentation effects, species invasion, and climate change effects will be possiblewith the Neotropical Xenarthrans data set. Please cite this data paper when using its data inpublications. We also request that researchers and teachers inform us of how they are usingthese data