Improving the accuracy of image field depth using a stereo matching algorithm is a key challenge in the field of machine vision. A so-called shaped adaptive window local stereo matching algorithm is proposed to solve the problem that traditional adaptive window algorithms are susceptible to uneven illumination, which affects their accuracy, and that the shape of the window does not accurately describe the boundary of the image that is to be matched. Because the traditional Census transform is vulnerable to fluctuations in illumination of the central pixel, a so-called three-dimensional pixel information technique which combines with the Census transform is proposed. The matching cost is obtained by comparing the central pixel with non-central pixels and by comparing the non-central pixels with one another. Moreover, for better conformance to the image contour, and to better approximate the image boundary of the examined object to obtain higher matching accuracy by the subsequent cost aggregation operation, a double helix path method is proposed. This method obtains a window that is shaped differently from the traditional rectangle or cross-based windows. In this method, the double paths which search and examine the pixel region simultaneously shorten the time required for window construction; meanwhile, the double helix path creates a more variable window shape, which more easily conforms to the image contour. The experimental results show that, compared with most conventional stereo matching algorithms on the Middlebury platform, the proposed algorithm is better able to characterise the image boundary to improve matching precision and is more robust in cases of uneven illumination.