This papar proposes an optical camera which detects not the distant objects but the close objects, namely
Nearsighted camera. In general, the optical camera can detect not only the close object but also the distant
objects located in front of the camera. However, when we consider some tasks for the robots such as the obstacle
avoidance and the object holding, the distant objects sometimes interfere the task executions. The procedures
may become easier if the robot can detect only the close objects related to the tasks. Although there are some
studies about the object recognition utilizing the difference of the distance from the camera to the objects such
as depth from (de)focus and stereo vision, the computational cost is large. The authors, therefore, focus on close
object detection and aim at developing an optical camera for close object detection. Though there are some
active devices for detecting the close object such as ultrasonic distance sensor and infrared radiation sensor,
there are few passive devices for the close object recognition. We explain the design method of the fundamental devices in the nearsighted camera, and show the prototype of the nearsighted camera. To evaluate the features of nearsighted camera, we conduct the experiments for confirming the performance of the fundamental device in the nearsighted camera. We also conduct the experiments for detecting some characters in near field. The experimental results show that nearsighted camera reacts not to the distant objects but to the close objects.
To understand a comprehensive atmospheric state, it is important to classify clouds in satellite images into
appropriate classes. Many researches utilizing various features concerning the cloud texture have been reported
in cloud classification. However, some clouds can not be classified uniquely only with the texture features.
According to the knowledge of the experts, they classify the clouds in two stages. They firstly categorize the
clouds into the provisional classes according to the brightnesses of the satellite images. They then classify each
provisional class into the objective class based on the texture, shape and velocity of the cloud employing the
meteorological knowledge about the time and location of the image. In this paper, we propose a novel method
for the cloud classification that consists of two stages and utilizes cloud movement as human experts adopt. We
firstly classify the clouds into 20 classes based on their brightnesses of the two-band spectral images. We then
closely analyze the classes according to five features such as the brightnesses, deviations of brightness and cloud
velocity estimated by varying window size adaptively. The experimental results are shown to verify the proposed
method.
This paper introduces a method for quasi-motion extraction from a blurred image utilizing edge field analysis
(EFA). Exposing a film for a certain time, we can directly photograph the trajectory of the moving object as an
edge in a blurred image. As the edge trajectories are not exactly the same but similar to the optical flows, they
allow us to treat the edge image as a pseudo-vector field. We define three line integrals in the edge image on
closed curve similar to vector analysis. These integrals correspond to three flow primitives of the scene such as
the translation, rotation and divergence. As the images, we utilized some images such as the storm, the bottle
rocket and a moving object with random patterns. In order to evaluate the proposed method, we conducted the
experiments of estimating the eye of the storm, the center of the explosion in terms of bottle rocket, and the
centers of the rotation and divergence of the moving object.
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