Abstract
In this work, we present a method for detecting interest points in digital images that is robust under a certain class of brightness transformations. Importance of such method is due to the fact that current video surveillance systems perform well under controlled environments but tend to suffer when variations in illumination are present.
Novelity of the method is based on the use of so-called sign representation of images. In contrast to representation of a digital image by its brightness function, sign representation associates with an image a graph of brightness increasing relation on pixels. As a result, the sign representation determines not a single image but a class of images, whose brightness functions are differ by monotonic transforms.
Other feature of the method is in interpretation of interest points. This concept in image processing theory is not rigorously defined; in general, a point of interest can be characterized by increased “complexity” of image structure in its vicinity. Since the sign representation associates with an image a directed graph, we consider interest points as “concentrators” of paths from/to vertices of the graph.
The results of experiments confirm the efficiency of the method.
This research has been supported by the Russian Foundation for Basic Research grant no. 19-07-00873.
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Karkishchenko, A., Mnukhin, V. (2021). Interest Points Detection Based on Sign Representations of Digital Images. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_22
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