The XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA 2019), the most traditional academic event specialized on signal processing in Colombia, will lead scientists to Universidad Industrial de Santander campus Bucaramanga. On behalf of the whole organizing committee, it is our great pleasure to welcome you in the city of parks from April 24th until April 26th, 2019 to attend a symposium that will feature inspiring plenary talks tutorials on emerging topics in the field of signal and image processing, and a high-level technical program including posters and oral sessions.

STSIVA 2019 is organized by the High Dimensional Signal Processing Research Group (HDSP) of the Universidad Industrial de Santander (UIS). UIS is a public university that serves the northeastern region of Colombia, with a main campus located in Bucaramanga, Santander, Colombia. UIS is regarded as one of the leading multidisciplinary research universities in Colombia by student population, research groups, academic output, technological development, and number of publications.

The XXII edition of STSIVA is aimed at national and international:
  • °Students in the area of signal processing, image processing, artificial vision and related areas.
  • °Professionals in engineering (electrical, electronics, computer science, audio, video, biomedical).
  • °Faculty and researchers working on the related areas.
Calendar / Deadlines
Full paper Submisison deadline: Friday, December 14, 2018
Notification: Friday, February 15, 2019
Camera ready papers: Friday, March 22, 2019
STSIVA 2019: April 24 - April 26, 2019
Conference topics
  • Techniques and Applications of Signal Processing
  • Image and Multi-Dimensional Signal Processing
  • Video Processing: Motion Estimation, Activities and Surveillance
  • Computer Vision: 3D Reconstruction and Recognition
  • Biomedical Signal and Image Processing
  • DSP-and FPGA-based Applications
  • Signals, Images and Computer Vision in Education
  • Voice, Audio and Multimedia Processing
  • Machine Learning and Pattern Recognition