Lars Vidar Magnusson

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This paper investigates how Felzenszwalb's and Huttenlocher's graph-based segmentation algorithm can be improved by automatic programming. We show that computers running Automatic Design of Algorithms Through Evolution (ADATE), our system for automatic programming , have induced a new graph-based algorithm that is 12 percent more accurate than the original(More)
We have considered edge detection as a classification problem, and we have applied two popular machine learning techniques to the problem and compared their best results to that of automatic programming. We show that ADATE, our system for automatic programming, is capable of producing solutions that are as good as, or better than, the best solutions(More)
In this paper, we employ automatic programming, a relatively unknown evolutionary computation strategy, to improve the non-max suppression step in the popular Canny edge detector. The new version of the algorithm has been tested on a dataset widely used to benchmark edge detection algorithms. The performance has increased by 1.9%, and a pairwise student-t(More)
We have used automatic programming, a machine learning technique related to inductive logic programming and genetic programming, to make the Canny edge detector better at identifying contours in natural images. We present an improved version of the filter used in the first stage of the Canny algorithm. We show that the mean performance of the Canny(More)
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