September 2018
Betsy Johanna Villa Brochero > Other Awards
EU4M is a two-year Erasmus Mundus Joint Master Degree in Mechatronic Engineering that offers a high-quality education in Mechatronics responding to the needs of industry and academia by offering a truly integrated approach to the different areas Mechatronics consist of: Mechanics, Electronics and IT control systems.
The JMD has two main educational objectives: first, to provide students with the technical knowledge that allows them to deal with any challenge related with the design, fabrication and maintenance of mechatronic systems; second, to help students open up to other working and living styles, through the exposure to other countries and cultures.
September 2014
Betsy Johanna Villa Brochero > Other Awards
Scholarship to carry out an academic mission in Germany and visit different research centers and universities in Germany related to the career of Mechatronic Engineering.
April 2014
Betsy Johanna Villa Brochero > Other Awards
Recognition for obtaining the 5th best average during the period 2013-2 in the career of Mechatronic Engineer at the Universidad Autonoma del Caribe
Full Member
Ms. Betsy Johanna Villa Brochero
Membership since:
2020
Field of activity:
Other Memberships/Affiliations
IEEE
Degrees:
2015
Undergraduate
Engineering sciences
2020
Master
Engineering sciences
Publications resulting from Research
Digital image processing applied on static sign language recognition system
Link: http://ojs.uac.edu.co/index.php/prospectiva/article/view/1488
Sign language L.S.C. (Lengua de Señas Colombiana) is a native language which chiefly uses manual communication to convey meaning. This can involve simultaneously combining hand shapes, movement, and orientation of the hands, arms or body, and even facial expressions to convey a speaker’s ideas. Currently in Colombia, there is an absence of technology focus on teaching and interpreting this language; for this reason, it’s interesting and a social commitment to e carry out initiatives that promote life quality improvement for the country’s deaf-mute population. In this article, the design and implementation process of a static hand gesture recognition system is shown, for this task we used Matlab as computing environment and the Scale-Invariant Features Transform (SIFT) method to extract characteristics from the image. Our system allows the acquired image visualization and its corresponding translation to the Colombian sign language. Through key points identification and their comparison with SIFT features in the system database makes it possible to retrieve the translation. The system can recognize 20 static letters from the Colombian Sign Languages, a graphical interface was implemented in Matlab that provides better visualization, simple access to the system, and high usability. A better response of the system is noticed when a standardized element of the image is used, in our case, a surgical glove. As future work, we propose to apply neural networks to the improvement of the tool, and a real-time implementation, which can generate a greater impact on the current needs of the Colombian population
Link: http://ojs.uac.edu.co/index.php/prospectiva/article/view/1488
Sign language L.S.C. (Lengua de Señas Colombiana) is a native language which chiefly uses manual communication to convey meaning. This can involve simultaneously combining hand shapes, movement, and orientation of the hands, arms or body, and even facial expressions to convey a speaker’s ideas. Currently in Colombia, there is an absence of technology focus on teaching and interpreting this language; for this reason, it’s interesting and a social commitment to e carry out initiatives that promote life quality improvement for the country’s deaf-mute population. In this article, the design and implementation process of a static hand gesture recognition system is shown, for this task we used Matlab as computing environment and the Scale-Invariant Features Transform (SIFT) method to extract characteristics from the image. Our system allows the acquired image visualization and its corresponding translation to the Colombian sign language. Through key points identification and their comparison with SIFT features in the system database makes it possible to retrieve the translation. The system can recognize 20 static letters from the Colombian Sign Languages, a graphical interface was implemented in Matlab that provides better visualization, simple access to the system, and high usability. A better response of the system is noticed when a standardized element of the image is used, in our case, a surgical glove. As future work, we propose to apply neural networks to the improvement of the tool, and a real-time implementation, which can generate a greater impact on the current needs of the Colombian population