Vision-based Robot Manipulator for Industrial Applications

This paper presents a multi-stage process of the development of a vision-based object sorting robot manipulator for industrial applications. The main aim of this research is to integrate vision system with the existing Scorbot in order to widen the capability of the integrated camera-robot system in industrial applications. Modern industrial robot Scorbot-ER 9 Pro is the focus of this research. Currently, the robot does not have an integrated vision system. Thus; a camera has been integrated to robot gripper to achieve the target objectives. The main difficulties include establishing a relevant sequence of operations, developing a proper communication between camera and robot as well as the integration of the system components such as Matlab, Visual Basics, and Scorbas

EDGE DETECTION PARAMETER OPTIMIZATION BASED ON THE GENETIC ALGORITHM FOR RAIL TRACK DETECTION

One of the most important parameters in an edge detection process is setting up the proper threshold value. However, that parameter can be different for almost each image, especially for infrared (IR) images. Traditional edge detectors cannot set it adaptively, so they are not very robust. This paper presents optimization of the edge detection parameter, i.e. threshold values for the Canny edge detector, based on the genetic algorithm for rail track detection with respect to minimal value of detection error. First, determination of the optimal high threshold value is performed, and the low threshold value is calculated based on the well-known method. However, detection results were not satisfactory so that, further on, the determination of optimal low and high threshold values is done. Efficiency of the developed method is tested on set of IR images, captured under night-time conditions. The results showed that quality detection is better and the detection error is smaller in the case of determination of both threshold values of the Canny edge detector

An efficient robust hyperheuristic clustering algorithm

Observations on recent research of clustering problems illustrate that most of the approaches used to deal with these problems are based on meta-heuristic and hybrid meta-heuristic to improve the solutions. Hyperheuristic is a set of heuristics, meta- heuristics and high-level search strategies that work on the heuristic search space instead of solution search space. Hyperheuristics techniques have been employed to develop approaches that are more general than optimization search methods and traditional techniques. In the last few years, most studies have focused considerably on the hyperheuristic algorithms to find generalized solutions but highly required robust and efficient solutions. The main idea in this research is to develop techniques that are able to provide an appropriate level of efficiency and high performance to find a class of basic level heuristic over different type of combinatorial optimization problems. Clustering is an unsupervised method in the data mining and pattern recognition. Nevertheless, most of the clustering algorithms are unstable and very sensitive to their input parameters. This study, proposes an efficient and robust hyperheuristic clustering algorithm to find approximate solutions and attempts to generalize the algorithm for different cluster problem domains. Our proposed clustering algorithm has managed to minimize the dissimilarity of all points of a cluster using hyperheuristic method, from the gravity center of the cluster with respect to capacity constraints in each cluster. The algorithm of hyperheuristic has emerged from pool of heuristic techniques. Mapping between solution spaces is one of the powerful and prevalent techniques in optimization domains. Most of the existing algorithms work directly with solution spaces where in some cases is very difficult and is sometime impossible due to the dynamic behavior of data and algorithm. By mapping the heuristic space into solution spaces, it would be possible to make easy decision to solve clustering problems. The proposed hyperheuristic clustering algorithm performs four major components including selection, decision, admission and hybrid metaheuristic algorithm. The intensive experiments have proven that the proposed algorithm has successfully produced robust and efficient clustering results

Use of remote sensing for land use policy formulation

The overall objectives and strategies of the Center for Remote Sensing remain to provide a center for excellence for multidisciplinary scientific expertise to address land-related global habitability and earth observing systems scientific issues. Specific research projects that were underway during the final contract period include: digital classification of coniferous forest types in Michigan's northern lower peninsula; a physiographic ecosystem approach to remote classification and mapping; land surface change detection and inventory; analysis of radiant temperature data; and development of methodologies to assess possible impacts of man's changes of land surface on meteorological parameters. Significant progress in each of the five project areas has occurred. Summaries on each of the projects are provided

Hyperspectral Image Analysis of Food for Nutritional Intake

The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers have developed various semiautomatic or automatic dietary assessment systems; most of them are based on the conventional color images such as RGB. The main disadvantage of such systems is their inability to differentiate foods of similar color or same ingredients in various colors, or different forms such as cooked or mixed forms. Although adding features such as shape, size and texture improve the overall performance, they are sensitive to changes in the illumination, rotation, scale, etc. A balance between quality and quantity of features representation, and system efficiency must also be considered. Hyperspectral technology combines conventional imaging technology with spectroscopy in a three-dimensional data-cube to obtain both the spatial and spectral information of the objects. However, the high dimensionality of hyperspectral data in addition to the redundancy between spectral bands limits performance, especially in online or onboard data processing applications. Thus, various features selection/extraction are also used to select the optimal feature subsets. The results are promising and verify the feasibility of using hyperspectral technology in dietary assessment applications

Program and Book of Abstracts: 2018 Undergraduate Research Celebration

The Ramaley Celebration is a highly anticipated event that features student presentations of their research accomplishments. At Winona State, undergraduate research is highly valued as an integral part of the educational process and the Ramaley Celebration is one way we recognize and affirm this. Furthermore, the wonderful diversity of the student presenters, the research projects, and the disciplines represented all provide a strong reminder of the distinctiveness and breadth of research across the entire WSU community. For our purposes, we define “research” very broadly as “an inquiry or investigation that makes an original intellectual or creative contribution to the discipline” (Council on Undergraduate Research).https://openriver.winona.edu/urc2018/1005/thumbnail.jp

Desarrollo de una metodología para identificación de características fisicoquímicas de productos agrícolas a partir de su correlación con técnicas de visión de máquina

La ausencia de prácticas automatizadas en la cosecha y poscosecha de los productos agrícolas, tiene como consecuencias un incremento en las pérdidas y una disminución en la competitividad de los mismos. La estimación de características fisicoquímicas en los productos mediante imágenes digitales puede permitir mejorar procesos tales como: selección y clasificación tanto para productores como consumidores. La metodología desarrollada con este fin permite correlacionar aspectos visibles en las imágenes tales como: color, textura, tamaño y forma con parámetros fisicoquímicos medidos. Se proponen cuatro fases fundamentales: i. identificación y medición de las características, ii. procesamiento de imágenes y extracción de características, iii. estimación de las características fisicoquímicas y, finalmente, iv. validación de la correlación. Esto, permite generar sistemas de visión de máquina automáticos para estimar a futuro, dichas propiedades fisicoquímicas; mediante una técnica no destructiva y rápida. Los resultados obtenidos al aplicar la metodología para estimar algunas de las principales características fisicoquímicas en diferentes productos, son superiores al 80% en términos del coeficiente de correlación, con una disminución significativa del porcentaje de error respecto a la desviación estándar de la muestra.Abstract. The absence of automation technologies for harvest and postharvest practices on agricultural products, has as a consequence an increase in losses and a decline in the competitiveness of the same. The estimation of physicochemical characteristics in agricultural products using digital images can allow improving processes such as selection and classification for both: producers and consumers. The methodology developed for this purpose enables correlate aspects in visible images such as color, texture, size and shape with measured physicochemical parameters. Four stages was proposed: i. identification and measurement of the characteristics, ii. image processing and feature extraction, iii. physicochemical characteristics estimation and iv. validation of the correlation. By following these steps is possible to construct machine vision systems for future automatic estimations of these physicochemical properties; with a nondestructive and rapid technique. The results obtained by applying of these methodology to estimate some of the main physicochemical characteristics in different products, are over 80% in terms of the correlation coefficient with a significant decrease of the rate error relative to the standard deviation of the sample set.Maestrí

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research