Intent recognition is essential for effective control of powered assistive devices, such as powered lower limb prostheses, exoskeletons, or wheelchairs. Currently, EMG and mechanical sensors are used for intent recognition of powered lower limb prostheses. We propose the addition of vision for improved intent recognition control, with this work focused on determining the best method for recognizing of ascending stair edges from 2D images. In this work different image processing methods were tested to determine which method produces the best line extraction. The best results were obtained using Canny, Sobel, Prewitt, and Roberts Cross edge detectors for four colorspace components. Finally, a convex/concave line decision system was developed to produce preliminary results about the presence or absence of stairs in a given image.