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• METHOD FOR DETECTING FEATURE POINTS OF AN IMAGE USING A SIGN REPRESENTATIONS

  A. N. Karkishchenko, V. B. Mnukhin

  2020-11-22

  Abstract ▼

  The aim of the study is to develop a method for detecting feature points of a digital image
  that is stable with respect to a certain class of brightness transformations. The need for such a
  method is due to the needs of detecting feature points of images in video surveillance systems and
  face recognition, often working in a changing light environment. A feature of the proposed method
  that distinguishes it from a number of well-known approaches to the problem of distinguishing
  characteristic points is the use of the so-called sign representation of images. In contrast to the
  usual defining of a digital image by a discrete brightness function, with a sign representation, the
  image is set in the form of an oriented graph corresponding to the binary relation of the increase
  in brightness on a set of pixels. Thus, the sign representation determines not a single image, but a
  set of images, the brightness functions of which are connected by strictly monotonic brightness
  transformations. It is this property of the sign representation that determines its effectiveness for
  solving the problems caused by the goal set above. A feature of the method under consideration is
  a special approach to the interpretation of the characteristic points of the image. This concept in
  image processing theory is not strictly defined; we can say that the characteristic point is characterized
  by increased "complexity" of the image structure in its vicinity. Since the sign representation
  of the image can be represented in the form of a directed graph, in this paper, to evaluate the
  complexity measure of the local neighborhood of its vertices, it is proposed to use the ranking
  method known in the spectral theory of graphs based on the Perron-Frobenius theorem. Its essence
  lies in the fact that the value of the component of the so-called Perron eigenvector of the
  adjacency matrix of this graph acts as a measure of the complexity of the vertex. To conduct experimental
  studies of the proposed approach, a set of programs was developed, the results of
  which confirm the efficiency of the method and demonstrate that with its help it is possible to obtain
  results close to the expected ones on model examples. The paper also offers a number of recommendations
  on the use of this method.