|
Nowadays, image segmentation is of great importance in features extraction and object recognition for
video image sequence. Many traditional segmentation techniques have special application and exists some
limitation in some degree. After analyzing the advantages and disadvantages of the present image
segmentation and object recognition methods, according to the characteric of infrared image, this paper
proposes a very simple yet effective algorithm to optimize the threshold value, which is in accordance with
the status of the reference pixels. The proposed algorithm varies the threshold value with bidirectional
line-by-line scanning (the forward scanning and the backward scanning) fusion model. The purpose of the
technique is to discriminate targets from the background, which is equivalent to assigning the label "F"
(representing "foreground") to object pixels, and the label "B" (representing "background") to background
pixels. Based on these bidirectional scanning intersections in the corresponding regions, this paper applies
the conditional probability density function (PDF) to fuse and optimize the threshold value. At the same
time, the optimal threshold values for target segmentation and recognition were acquired. Therefore, this
paper designs a novel background frame differencing method that refers to previously conclusions made by
neighboring pixels. Change different infrared image sequences, the experiment results show this fusion
method can eliminate the boundaries blurring, especially the transition regions between object (foreground)
and background. As a conclusion, for different infrared image sequences with complex illumination change,
noise change, etc., the proposed method gives better segmentation and recognition results for objects than
other traditional methods, such as the fixed threshold method, the single directional scanning technique,
and so on. On the other hand, the proposed method has lower complexity and higher real-time, which is
helpful for hardware design and engineering application.
|
