Design and development of image edge detector using FPGA board.

FPGAs are well tailored for a student to experiment and FGPA boards help to work on high-end applications like embedded, computer architecture and software/hardware co-design and can be used to implement almost any DSP algorithm. Practical work promotes experiential learning. Implementation of image processing is done using many boards where we aimed at working on zynq boards and attain efficient results. Spartan3E and basys3 are the boards belonging to FPGA family which we used aiming image processing which usually necessitates multiple outcomes involving simple algorithms and sometimes sophisticated transformations. Disparate algorithms lead to many applications where we focused on edge detection in different fields. Many steps involve in image processing where edge detection is one of them which leads to object recognition and using for trace recognition which is reliable in our daily applications. Finding the boundaries to recognize an object is the key point required for image processing. Since there are limitations while going for hardware implementation we would like to experiment on conventional operators. Images used in our project cover conventional lena image using sobel, canny, robert and prewitt techniques. Daily life applications are also covered in sports zone and tracking of objects by taking images of a badminton player and SRA building. Our results show that canny operator works efficiently and show less latency than other techniques. [ABSTRACT FROM AUTHOR]