- Grouping of optical flows :
- A network system is proposed for segmenting and extracting multiple moving objects in 2D images. The system uses an interconnected neural network in which grouping factors, such as edge proximity, smoothness of edge orientation, and smoothness of velocity perpendicular to an edge, are embedded. The system groups edges so that the network energy may be minimized, i.e. edges may be
organized into perceptually plausible configuration. Experimental
results are provided to indicate the performance and noise robustness
of the system in extracting objects in synthetic images.
- Generation of organized structure :
- This study considers a approach to such a problem, where the perceptual grouping as pointed out by Gestalt psychologists is applied. More precisely, the method is as follows. The connected component elements are extracted from the input image. Then, based on the factors for perceptual grouping (such as proximity, similarity, good continuity and closure), which are quantitatively
given expressions by the psychological experiment conducted by the
authors, elements which have possibilities of forming a global
structure are determined and connected, based on such information as
relative position of elements.
Using the evaluation function, the optimal candidate for the figure is
extracted from the result. When observing overlapped multiple figures,
a human can separate the figures (figure segmentation). This aspect
also is considered in this study. The foregoing idea is implemented on
a computer. Simulation experiments are made for several patterns, and
the operation of the proposed system is verified.
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