Author:
Viacheslav Antsiperov
Affiliation:
Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences, Mokhovaya 11-7, Moscow and Russia
Keyword(s):
Photon-Counting Sensors, Target Detection and Identification, Semiclassical Theory of Light Detection, Poisson Point Process, Machine Learning, EM-Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Feature Selection and Extraction
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
;
Theory and Methods
Abstract:
The article presents the results of developing a machine learning approach to the problem of object identification (recognition) in images (data) recorded by photo-counting sensors. Such images are significantly different from the traditional ones, taken with conventional sensors in the process of time exposure and spatial averaging of the incident radiation. The result of radiation registration by photo-counting sensors (image) is rather a continuous stream of data, whose time frame is characterized by a relatively small number of photocounts. The latter leads to a low signal-to-noise ratio, low contrast and fuzzy shapes of the objects. For this reason, the well-known methods, designed for traditional image recognition, are not effective enough in this case and new recognition approaches, oriented to a low-count images, are required. In this paper we propose such an approach. It is based on the machine learning paradigm and designed for identifying (low count) objects given by point
-sets. Consistently using a discrete set of coordinates of photocounts rather than a continuous image reconstructed, we formalize the problem in question as the problem of the best fitting of this set of counts, considered as the realization of a certain point process, to the statistical description of one of the previously registered point processes, which we call precedents. It is shown, that applying the Poisson point process model for formalizing the registration process in photo-counting sensors, it is possible to reduce the problem of object identification to the problem of maximizing the tested point--set likelihood with respect to the classes of modelling object distributions up to shape size and position. It is also demonstrated that these procedures can be brought to an algorithmic realization, analogous in structure to the popular EM algorithms. At the end of the paper we, for the sake of illustration, present some results of applying the developed algorithms to the identification of objects in a small artificial data base of low-count images.
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