Enhanced Edge Detection for Image Segmentation and its Real-Time Implementation
Lourdu Jennifer J.R., Joy Vasantha Rani S P · 2024
Edge detection techniques play a major role in image segmentation which is one of the pre-processing steps in the image or video processing pipeline. These methods remove superfluous information while preserving important pixels. They are capable of detecting the change in brightness in digital images to identify borders/edges. It is an acknowledged fact that the Canny edge algorithm performs better than the other edge-detecting techniques. However, some processes are complex, and others are susceptible to noise. Before applying the edge detection to the images, in this study, the Canny edge detection is improvised utilizing histogram equalization techniques like CLAHE and noise-removal with bilateral/gaussian filtering. This method made use of OpenCV libraries and was implemented in a Jupyter notebook with Python. Experimental results show that the improved algorithms are more flexible in recognizing more edge features, more noise-resistant, and capable of differentiating targets from the background. The Zynq Ultrascale+ MPSoC is utilized in the hardware implementation of the suggested techniques. Since the Programmable Logic (PL) portion of the board can handle more sophisticated computations in high-speed applications, the Programming System (PS) portion of the board is used for implementation. When compared to previous relevant papers and simple, sophisticated techniques, the approaches provide a good performance.