The TOMBO (Thin Observation Module by Bound Optics) is a compound imaging system inspired by biological
visual systems. The image of an object captured by the TOMBO system is composed of multiple images
observed from multiple view-points. Owing to disparities between the individual images, the object distance
can be measured. In this paper, we propose a novel method for 3D information acquisition using the TOMBO
system. The conventional image reconstruction method on the TOMBO system assumes that a planar object
is located at a specific distance. Therefore, if the actual and the assumed object distances are different, the
correct reconstructed image is not obtained. To reconstruct the correct image of 3D objects, we execute the
image reconstruction process with several candidates of the object distance. The distance where high frequency
components are successfully reconstructed is determined as the object distance. Using the distances of all objects,
we can generate a composite image focusing on the objects. Moreover, object extraction is demonstrated by
using the measured object distances and the composite image. We reduce the processing time by adaptation of
the processing for a GPU (Graphics Processing Unit). Experimental results indicate effectiveness of the proposed
method.
Information techniques featuring adaptability, autonomy, and diversity found in the behavior of livings are promising. The purpose of this study is to explore object selection method adaptable to unexpected change of the environment. An attractor selection is used as an algorithm for flexible adjustment to various change of the environment. An attractor is a convergence point in a state space and corresponds to a stable point of a given system. The attractor selection chooses an attractor according to the suitability for a given environmental condition. The proposed object selection algorithm finds a solution from several images captured with different focus settings. To obtain these images a compound-eye imaging system is assumed to be used. In the object selection, an object is regarded as an attractor. The location and the features of the object are expressed as variables in the state space. In this study, hue in the Hue-Saturation-Value color model is used as a parameter of an environmental condition. In the simulation, two objects of different hue were located at different distances. One of the objects might be selected by the proposed algorithm. The correct operations of the algorithm are confirmed. The results show that the attentive object is correctly switched according to the change of selecting condition. The adaptability and the robustness of the method has been confirmed.
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