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Stereoscopic vision systems have been used manually for decades to capture threedimensional information of the environment in different applications. With the growth experienced in recent years by the techniques of computer image processing, stereoscopic vision has been increasingly incorporating automated systems of different nature. The central problem in the automation of a stereoscopic vision system is the determination of the correspondence between pixels of the pair of stereoscopic images that come from the same point in three-dimensional scene. The research undertaken in this work comprises the design of a global strategy to solve the stereoscopic correspondence problem for a specific kind of hemispherical image from forest environments. The images are obtained through an optical system based on the lens known as fisheye because this optic system can recover 3D information in a large field-of-view around the camera; in our system it is 183o×360o. This is an important advantage because it allows one to image the trees in the 3D scene close to the system from the base to the top, unlike in systems equipped with conventional lenses where close objects are partially mapped (Abraham & Forstner, 2005). The focus is on obtaining this information from tree trunks using stereoscopic images. The technicians carry out forest inventories which include studies on wood volume and tree density as well as the evolution and growth of the trees with the measurements obtained. Because the trees appear completely imaged, the stereoscopic system allows the calculation of distances from the device to significant points into the trees in the 3D scene, including diameters along the stem, heights and crown dimensions to be measured, as well as determining the position of the trees. These data may be used to obtain precise taper equations, leaf area or volume estimations (Montes et al., 2009). As the distance from the device to each tree can be calculated, the density of trees within a determined area can be also surveyed and growing stock; tree density, basal area (the section of stems at 1.30 m height in a hectare) and other interesting variables may be estimated at forest stand level using statistical inference (Gregoire, 1998).

Segmentation and Stereoscopic Correspondence in Images Obtained with Omnidirectional Projection for Forest Environments