Fast Edge Detection Using Structured Forests

An important and very crucial component of many systems involving images is the edge detection which includes
object detectors and image segmentation algorithms. Edge patches always shows shapes of its inherent structure,
like T junctions or lines. Taking advantage of the shapes available in the images we get to know of an edge
detector which is accurate and efficient. In this approach the structured labels is robustly mapped to a space that is
discrete by which evaluation of the measure of the standard information gain is possible. The outcome is a way
that shows a real time performance which is many times faster than any of the traditional and modern approaches
available today, which also achieves the best results for edge detection both the Berkeley Segmentation Dataset
and Benchmark (BSDS 500) and the NYU depth dataset. This approach also shows its strength as a an edge
detector that can be used for any regular purpose by displaying how this method generalize across all datasets