SolisMontero

This presentation describes a new line segment detection and extraction algorithm for computer vision, image segmentation, and shape recognition applications. This algorithm uses a compilation of different imaging processing steps such as normalization, Gaussian smooth, thresholding, and Laplace edge detection to extract edge contours from colour input images. Contours of each connected component are divided into short segments, which are classified by their orientation into nine discrete categories. Straight lines are recognized as the minimal number of such consecutive short segments with the same direction. This solution gives us surprisingly a more accurate, faster and simpler answer with fewer parameters than the widely used Hough Transform algorithm for detecting lines segments among any orientation and location inside images. Its easy implementation, simplicity, speed, the ability to divide an edge into straight line segments using the actual morphology of objects, inclusion of endpoint information, and the use of the OpenCV library are key features and advantages of this solution. The algorithm was tested on several simple shape images as well as real pictures giving more accuracy. This line detection algorithm is robust to image transformations such as rotation, scaling and translation, and to the selection of parameter values.