Abstract: A method is described for extracting lineal features from an image using extended local information to provide robustness and sensitivity. The method utilizes both gradient magnitude and direction information, and incorporates explicit lineal and end-stop terms. These terms are combined non-linearly to produce an energy landscape in which local minima correspond to lineal features called \fIsticks\fR that can be represented as line segments. A gradient descent (stick-growing) process is used to find these minima. The method is compared to two others, and found to have improved gap-crossing characteristics.