Application of an automated labour performance measuring system at a confectionery company

Published ArticleThis paper focuses on the implementation of a labour performance measuring system at a confectionery company. The computer vision based system is based on the work sampling methodology. It consists of four cameras linked to a central computer via USB extenders. The computer uses a random function in C++ in order to determine when measurements are to be taken. OpenCV is used to track the movement of a target worker's dominant hand at a given work station. Tracking is accomplished through the use of a bandwidth colour filter. The speed of the worker's hand is used to identify whether the worker is busy, idle or out of the frame over the course of the sampling period. Data collected by the system is written into a number of text files. The stored data is then exported to a Microsoft Excel 2007 spread sheet where it is analysed and a report on the labour utilisation is generated

Review and analysis of work sampling methods : the case of an automated labour performance measurement system using the work sampling method

Published ArticleThis paper analyses work sampling and time study as work measurement methods with the view of employing them in an automated labour performance measurement system. These are compared with respect to Hawthorn effect, labour intensiveness, cost, tediousness and knowledge extensiveness. The analysis proves that work sampling is a better option for developing an automated labour performance measurement system that employs computer vision. Web cameras are used to feed real-time images to a central computer via USB extenders. The computer runs a standalone C++ application that uses a random function to establish when measurements are to be taken. The developed video camera footage is converted into a pixel matrix using OpenCV. This matrix is then filtered and analysed, enabling the tracking of a worker. The data generated is stored in text files. After the work sampling period has elapsed, the data is transferred into Microsoft Excel for analysis. Finally a report of the labour utilisation is generated in Microsoft Excel and then send to the analyst for review

Where to look when identifying roadkilled amphibians?

Roads have multiple effects on wildlife; amphibians are one of the groups more intensely affected by roadkills. Monitoring roadkills is expensive and time consuming. Automated mapping systems for detecting roadkills, based on robotic computer vision techniques, are largely necessary. Amphibians can be recognised by a set of features as shape, size, colouration, habitat and location. This species identification by using multiple features at the same time is known as “jizz”. In a similar way to human vision, computer vision algorithms must incorporate a prioritisation process when analysing the objects in an image. Our main goal here was to give a numerical priority sequence of particular characteristics of roadkilled amphibians to improve the computing and learning process of algorithms. We asked hundred and five amateur and professional herpetologists to answer a simple test of five sets with ten images each of roadkilled amphibians, in order to determine which body parts or characteristics (body form, colour, and other patterns) are used to identify correctly the species. Anura was the group most easily identified when it was roadkilled and Caudata was the most difficult. The lower the taxonomic level of amphibian, the higher the difficulty of identifying them, both in Anura and Caudata. Roadkilled amphibians in general and Anura group were mostly identified by the Form, by the combination of Form and Colour, and finally by Colour. Caudata was identified mainly on Form and Colour and on Colour. Computer vision algorithms must incorporate these combinations of features, avoiding to work exclusively in one specific feature.Special thanks to all respondents (Suppl. Mat. Appendix A3-Contributors / Respondents) and to all who have contributed to diffusion and promotion of the Roadkills survey: Road Ecology Group on Facebook, Asociación Herpetológica Granadina, Asociación Herpetológica Española (AHE), Herpnet and all individuals who have also believed appropriate the disclosure of the survey. Thanks to an anonymous reviewer for English linguistic advising and for the comments and suggestions that greatly improved the last version of the manuscript. M. Franch and C. Silva are funded by a grant (UMINHO/ BI/175/2013, UMINHO/BI/172/2013) (from the Foundation for Science and Technology Portugal (FCT) and cofinanced by FEDER through COMPETE –POFC (“Intelligent systems for mapping amphibian mortality on Portuguese roads”, PTDC/BIA-BIC/4296/2012). N. Sillero is supported by an FCT contract IF/01526/2013.info:eu-repo/semantics/publishedVersio

Plataforma de evaluación de algoritmos de reconocimiento de caracteres numéricos en imágenes digitales.

La investigación tuvo como objetivo evaluar las técnicas de reconocimiento de dígitos en imágenes: K-Nearest Neighbor (KNN), TESSERACT y Redes Neuronales Artificiales (RNA); lo que permitió determinar la técnica con mayor nivel de precisión que podrá ser utilizada en diferentes aplicaciones de visión artificial. Se planteó la hipótesis de investigación “La selección adecuada de la técnica de reconocimiento de caracteres numéricos en imágenes digitales permitirá determinar la técnica con el mayor grado de precisión y minimizar el consumo de recursos en computadores SBC”. Se utilizó la prueba no paramétrica de Kruskal-Wallis en la comparación del grado de precisión de las técnicas con un nivel de confianza del 95%. Los indicadores que se utilizaron son: grado de precisión, tiempo empleado para el reconocimiento, cantidad de memoria ram y nivel de uso del CPU. Se utilizó una muestra ponderada de 30 fotografías tomadas a medidores de energía eléctrica de la ciudadela Las Acacias de la ciudad de Riobamba. De acuerdo a los resultados de las pruebas estadísticas se determinó que la técnica de reconocimiento con mayor grado de precisión fue KNN alcanzando un promedio de 49,33% frente a 29,833% y 22,00% de TESSERACT y RNA respectivamente. El tiempo promedio empleado por KNN para el reconocimiento fue de 1,22 segundos, en promedio se utilizó 15,7 megabytes de memoria ram y 11,64% de uso del CPU. Se utilizó OpenCV, Python y C++ bajo la distribución Raspbian de Linux para evaluar cada una de las técnicas seleccionadas. Se recomienda profundizar en el estudio de la técnica KNN enfocadas a diferentes aplicaciones de visión artificial en la industria, así como evaluar nuevas técnicas de reconocimiento de patrones como SVM, Deep Learning y Aprendizaje no Supervisado, que se han convertido en campos de investigación activos en muchas universidades del mundo.The research aimed to evaluate the techniques of digit recognition in images: K-Nearest Neighbor (KNN), TESSERACT and Artificial Neural Networks (RNA); Which allowed to determine the technique with greater level of precision that can be used in different applications of artificial vision. The research hypothesis "The proper selection of numerical carácter recognition technique in digital images will allow to determine the technique with the highest degree of accuracy and to minimize the consumption of resources in SBC computers." The nonparametric Kruskal-Wallis test was used in the comparison of the degree of precision of the techniques with a confidence level of 95%. The indicators that were used are: degree of precision, time spent for recognition, amount of RAM and level of CPU usage. A sample was used of 30 photographs taken at electric power meters of Las Acacias in Riobamba city. According to the statistical tests results, was determined that the most accurate recognition technique was KNN reaching an average of 49.33% versus to 29.833% and 22.00% of TESSERACT and RNA respectively. The average time spent by KNN for the recognition was 1.22 seconds, on average 15.7 megabytes of RAM and 11.64% of CPU usage were used. OpenCV, Python and C ++ were used under the Linux Raspbian distribution to evaluate each selected techniques. It is recommended to deepen the study of the KNN technique focused on different applications of artificial visión in the industry, as well as to evaluate new recognition techniques of Pattern such as SVM, Deep Learning and Non-Supervised Leanring, which have become active research fields in many universities around the world

Plataforma de evaluación de algoritmos de reconocimiento de caracteres numéricos en imágenes digitales.

La investigación tuvo como objetivo evaluar las técnicas de reconocimiento de dígitos en imágenes: K-Nearest Neighbor (KNN), TESSERACT y Redes Neuronales Artificiales (RNA); lo que permitió determinar la técnica con mayor nivel de precisión que podrá ser utilizada en diferentes aplicaciones de visión artificial. Se planteó la hipótesis de investigación “La selección adecuada de la técnica de reconocimiento de caracteres numéricos en imágenes digitales permitirá determinar la técnica con el mayor grado de precisión y minimizar el consumo de recursos en computadores SBC”. Se utilizó la prueba no paramétrica de Kruskal-Wallis en la comparación del grado de precisión de las técnicas con un nivel de confianza del 95%. Los indicadores que se utilizaron son: grado de precisión, tiempo empleado para el reconocimiento, cantidad de memoria ram y nivel de uso del CPU. Se utilizó una muestra ponderada de 30 fotografías tomadas a medidores de energía eléctrica de la ciudadela Las Acacias de la ciudad de Riobamba. De acuerdo a los resultados de las pruebas estadísticas se determinó que la técnica de reconocimiento con mayor grado de precisión fue KNN alcanzando un promedio de 49,33% frente a 29,833% y 22,00% de TESSERACT y RNA respectivamente. El tiempo promedio empleado por KNN para el reconocimiento fue de 1,22 segundos, en promedio se utilizó 15,7 megabytes de memoria ram y 11,64% de uso del CPU. Se utilizó OpenCV, Python y C++ bajo la distribución Raspbian de Linux para evaluar cada una de las técnicas seleccionadas. Se recomienda profundizar en el estudio de la técnica KNN enfocadas a diferentes aplicaciones de visión artificial en la industria, así como evaluar nuevas técnicas de reconocimiento de patrones como SVM, Deep Learning y Aprendizaje no Supervisado, que se han convertido en campos de investigación activos en muchas universidades del mundo.The research aimed to evaluate the techniques of digit recognition in images: K-Nearest Neighbor (KNN), TESSERACT and Artificial Neural Networks (RNA); Which allowed to determine the technique with greater level of precision that can be used in different applications of artificial vision. The research hypothesis "The proper selection of numerical carácter recognition technique in digital images will allow to determine the technique with the highest degree of accuracy and to minimize the consumption of resources in SBC computers." The nonparametric Kruskal-Wallis test was used in the comparison of the degree of precision of the techniques with a confidence level of 95%. The indicators that were used are: degree of precision, time spent for recognition, amount of RAM and level of CPU usage. A sample was used of 30 photographs taken at electric power meters of Las Acacias in Riobamba city. According to the statistical tests results, was determined that the most accurate recognition technique was KNN reaching an average of 49.33% versus to 29.833% and 22.00% of TESSERACT and RNA respectively. The average time spent by KNN for the recognition was 1.22 seconds, on average 15.7 megabytes of RAM and 11.64% of CPU usage were used. OpenCV, Python and C ++ were used under the Linux Raspbian distribution to evaluate each selected techniques. It is recommended to deepen the study of the KNN technique focused on different applications of artificial visión in the industry, as well as to evaluate new recognition techniques of Pattern such as SVM, Deep Learning and Non-Supervised Leanring, which have become active research fields in many universities around the world

Ch OpenCV for interactive open architecture computer vision

In this paper, design and implementation of an interactive open architecture computer vision software package called Ch OpenCV is presented. Benefiting from both Ch and OpenCV, Ch OpenCV has many salient features. It is interactive, capable of interface with binary static or dynamical C/Cþþ libraries, integrated with advanced numerical features and embeddable. It is especially suitable for rapid prototyping, web-based applications, and teaching and learning about computer vision. Applications of Ch OpenCV including web-based image processing are illustrated with examples