0001Automatic grain size determination in microstructures using image processing

H. Peregrina-Barretoa, I.R. Terol-Villalobos, J.J. Rangel-Magdaleno, A.M. Herrera-Navarro, L.A. Morales-Hernández, F. Manríquez-Guerrero

  • Laboratorio de Investigación en Control Reconfigurable, Querétaro, Querétaro, CP 76209, Mexico.
  • Universidad Autónoma de Querétaro-Campus San Juan del Río, San Juan del Río, Querétaro, CP 76800, Mexico.
  • Centro de Investigación y Desarrollo Tecnológico en Electroquímica, San Fandila-Pedro Escobedo, Querétaro, CP 76703, Mexico


In microstructure analysis, the grain size determination is an important task. However, it takes a long time when it is made manually. Nowadays, automatic techniques for grain size determination have been implemented. Although these automatic techniques are documented on the ASTM standards, one of the main drawbacks is related to the quality of the digital images. There are many factors that can affect the quality of an image, such as the illumination conditions, causing noise, low contrast, bad defined boundaries, among others. When a metallographic image presents these characteristics, individual grain identification becomes a difficult task. The present work is focused on a novel methodology that enables the clear definition of the grain and the boundary regions for an accurate automatic grain size determination through some efficient image processing techniques.

Measurements_01_2013_Autom grain sizeArtículo original: http://www.sciencedirect.com/science/article/pii/S0263224112002539

Broken bars detection on induction motor using MCSA and mathematical morphology: An experimental study

José Rangel-Magdaleno and Juan Ramirez-Cortés, Electronics Department INAOE, Puebla, México, jrangel@ianoep.mx, jmram@inaoep.mx
Hayde Peregrina Barreto, Pi-Core Department, LICORE AC, Querétaro, México, h.peregrina@licore.org


0001 (2)Currently, industry demands early failure detection on his processes, machines, production lines, etc. One of the most widely used motors in industry is the induction motor. This kind of motors represent around the 85% of industry power consumption. For this reason, the detection of motor failure is an important task.A common induction motor failure is the broken bars. The broken bars are used by manufacturing defects or machine stress. Although broken bars represent around the 10% of motor failure, depending of the failure level, they may produce the total motor loss or even stop a line production. Other characteristics produced by broken bars are the increase of power consumption and vibration.


Thermal image processing for quantitative determination of temperature variations in plantar angiosomes

H.Peregrina-Barretoa, L.A. Morales-Hernándezb, J.J. Rangel-Magdalenoc, P.D. Vázquez-Rodríguezb 

  • Laboratorio de Investigación en Control Reconfigurable, Querétaro, México.
  • Universidad Autónoma de Querétaro, Querétaro, México.
  • Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México. hperegrina@ieee.org


Thermal image analysis has been extended to a wide variety of application in the medicine field. Thermography and image processing are useful tools because they facilitate a noninvasive and fast diagnostic. In the study of the diabetic foot, it is important the early detection of ulceration risks. Such detection becomes difficult, due to the lack of symptoms in the early phase. A late detection may lead to an extremity amputation. In the literature it has been established a relation between temperature and ulceration regions and, in recent works, some qualitative solutions for risk ulceration diagnostic based on a thermal analysis of the plantar foot have been proposed. In this work, an analysis and quantitative comparison of the temperature between the feet by using the concept of angiosome on diabetic patients is proposed, the goal of this study is to provide useful information in the early foot ulcers detection in patients with diabetic neuropathy.


Optical spectroscopy as a first step for noninvasive plant nutrition evaluation

Alejandro Espinosa-Calderon1,2+, Rafael A. Borquez-Lopez3*, Enrique Rico-Garcia1, Martin Olmos-Lopez4, Ramon G. Guevara-Gonzalez1

Universidad Autónoma de Querétaro, Querétaro, Mex.
Laboratorio de Investigación en Control Reconfigurable A.C., Querétaro, Mex.
Instituto Tecnológico de Sonora, Obregón, Mex.
Centro de Investigaciones en Óptica, A.C., León, Mex.

+ a.espinosa@licore.org * rborquez@itson.mx


Leaf analysis is a common practice in agriculture to assess the nutrient levels in plants. Optical spectroscopy showed absorption peaks in plant leaves of different species around 450 and 700 nm. Such absorption might be related to the nutrition level of these plants and could be measurable through image processing.