Roof Edge Image Processing

All module communication and the camera communication happen over http.

Roof edge image processing.

A novel markov random field mrf model is proposed for roof edge as well as step edge preserving image smoothing. Edge detection is widely used in imageprocessing as it is a quick and easy way of extracting mostof the important features in an image. The goal of edgedetection is to localize the variations in the intensity of animage and to identify the physical phenomena whichproduce them. Step edge transition of intensity level over 1 pixel only in ideal or few pixels on a more practical use ramp edge a slow and graduate transition roof edge a transition to a different intensity and back.

Image surfaces containing roof edges are represented by piecewise continuous polynomial functions governed by a few parameters. Edge detection using derivatives often points that lie on an edge. Some kind of spread line. Iot edge modules talk to the video camera to get an image then feed that into the classifier module get the results evaluate it and update the home assistant sensor accordingly.

Piecewise smoothness constraint is imposed on these parameters rather than on the surface heights as is in traditional models for step edges. Edge models 3 differentt edge types are observed.