Abstract
In this paper, a combined algorithm for counting objects in images is proposed. The pattern-matching algorithm is used to identify the patterns that are present in an image. Proposed algorithm consists of two parts. Pattern matching subroutine is based on normalized cross correlation technique which is widely used in image processing application. Pattern matching can be used to recognize and/or locate specific objects in an image. It is one of the emerging areas in computational object counting. Neural network subroutine is needed to filter out false positives that may occur during cross correlation function evaluation.Furthermore, an experimental evaluation is carried out to estimate the performance of the proposed efficient pattern matching algorithm for images of blood microscopy and chamomile field image. In the first case, the task is to count erythrocytes in the blood sample. In the second case, it is needed to count the flowers in the field.
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