Ignacio Bravo Muñoz

Embedded Vision Modules for Tracking and Counting People

This paper shows the algorithm implementation for a field-programmable gate array (FPGA)-based design for people counting using a low-level head-detection method. The hardware (HW) implementation on an FPGA allows the capture and online processing in real time on the same chip. Different annular patterns are used to process in parallel the image and detect heads of different sizes. Preprocessing and edge extraction are also made using reconfigurable HW. The developed system exploits HW processing, as the vision algorithm has been modified and tuned for HW implementation. It uses minimal area resources of a Spartan3 (1.5 Mgates), and its real-time performance is comparable with more sophisticated algorithms while utilizing very low cost circuits.

Improving the Calibration of Image Sensors Based on IOFBs, Using Differential Gray-Code Space Encoding

This paper presents a fast calibration method to determine the transfer function for spatial correspondences in image transmission devices with Incoherent Optical Fiber Bundles (IOFBs), by performing a scan of the input, using differential patterns generated from a Gray code (Differential Gray-Code Space Encoding, DGSE). The results demonstrate that this technique provides a noticeable reduction in processing time and better quality of the reconstructed image compared to other, previously employed techniques, such as point or fringe scanning, or even other known space encoding techniques.