Analysis of traffic information is one of the applications of remote sensing. Several studies have been reported for
vehicle extraction from satellite images or aerial images by using image processing methods. The analysis of these
images is not influenced by the ground damage and can obtain a lot of information over a wide area. In such studies, the
shadow areas casted by buildings are the cause of errors in extracting vehicles in urban areas. This is because the shadow
areas are dark and the positions of vehicles in the areas are unclear. In this paper, we propose a method of extracting
shadow areas casted by buildings using three-dimensional digital map data of buildings and extracting vehicles in the
areas using image processing methods. The conventional method of extracting shadow areas uses the image intensity,
however, this method has the problem that objects having low intensity are mis-extracted. Our method solves this
problem by estimating the position and shape of shadow areas by using three-dimensional digital map data and metadata
of a satellite image. In vehicle extraction, we use edge detection method for detecting the outlines of vehicles. The
detection of the vehicle edges is difficult, since the intensities of vehicle edges are different in the sunny areas and in the
shadow areas. However, by extracting shadow areas using the map data in advance and computing the threshold of the
edge detection dynamically, our method can detect the vehicle edges and obtain the vehicle positions correctly. We
developed relevant software on the computer, and we analyzed actual images to evaluate the effectiveness of our method.
We propose a method of using satellite images to analyze road conditions after a large-scale earthquake accompanied by
a tsunami. Remote sensing using satellite images can be used to collect information over a wide area in a short time.
Such information is particularly valuable for organizing relief efforts quickly and effectively after large-scale disasters
such as the Great East Japan Earthquake on March 11, 2011. Although a large number of studies have focused on the
extraction of damaged buildings and debris on roads, there have been few studies on the extraction of road areas flooded
by a tsunami. Also, since the Great East Japan Earthquake, there has been increased concern about tsunami damage in
addition to earthquake damage, meaning that a method of extracting both earthquake damage and tsunami damage is
required. The purpose of this study is to analyze the safety of roads around a stricken area in detail to help support relief
activities during times of disasters.
We propose a flexible probabilistic method for the extraction of earthquake-damaged areas from aerial images.
We segment an aerial image into regions and classify each region on the basis of the features appearing in damaged
areas. We consider the similarity of neighboring regions in the classification. As a result of segmentation, the
classification is independent of the color of each region. Our results show the likelihood of a region being damaged
and enable the flexible estimation of damage based on human decisions. The result is displayed on a digital map
that can be used for various rescue and humanitarian activities.
There have been many reports on the analysis of the Earth's surface by remote sensing. The purpose of this study is to
analyze traffic information, and we have been studying methods of collecting traffic information by remote sensing. To
collect traffic information, sensors installed on the roadside are frequently used. However, methods using sensors only
collect information around the positions of the sensors. In this study, we attempt to solve this problem by using satellite
images, which have recently become increasingly available. We propose a method of collecting traffic information over
a large area using satellite images as well as three-dimensional digital maps. We assess traffic conditions by computing
the number of edges of vehicles per road section as follows. First, the edges of vehicles are detected in satellite images.
During this processing, three-dimensional digital maps are used to increase the accuracy of vehicle edge detection. The
number of vehicles per road section, which is computed from the number of edges of vehicles, is computed and referred
to as the vehicle density. Traffic conditions can be assessed from the vehicle density and are considered useful for
collecting information on traffic congestion. In this study, we experimentally confirm that congested roads can be
extracted from satellite images by our method.
Three-dimensional (3D) shape measurement of a human face is useful in various applications, and many researches have been reported until now. In particular, many applications require methods of measuring time series data of 3D shapes by using simple devices. However, in the present, there are a lot of measurement methods with large scale devices under strong restraint conditions, and hence applications of the method are limited. In our research, we use binocular stereo methods with color slits. We measure 3D shape by using a couple of stereo images, and we continue to do the measurement sequentially at high speed. Therefore, our method enables real-time measurement of a human face in motion. Up to now, when featureless objects such as a human face are measured, general methods have found the corresponding points by projecting the slit lights onto the subject and by scanning the subject. Consequently, these methods require a long time to measure the subject, and hence the shape cannot be accurately measured when the object moves. In our method, we extract the color slits projected on the face and make these correspond between the images at a time, and hence, we can achieve high-speed stereo matching even when the subject moves.
Template matching is used in many applications, such as object recognition and motion tracking. In this study, we propose a template matching method that is robust against rotation and occlusion. For this purpose, we first divide a template image into several blocks. In the division, each block size is variable on the basis of the brightness distribution in the block region. Next, we search the matching position of each block by using a color histogram matching method whose result is rotational invariant. Then, from the matching coordinates of each block, we compute the Helmert transformation parameters and vote to the coordinates in the space composed of the parameters. Finally, we obtain the matching position of the template by searching the optimum Helmert transformation parameters from the coordinates where the sum of the vote is the maximum. We evaluate the efficacy of our method by means of several experiments. This method enables the robust extraction of an object which is rotated or occluded and is usable in many applications.
We propose to set a 3-D search volume for tracking a 3-D palm motion efficiently using two cameras. If we perform template matching for right and left images independently, two points in two images do not always correspond to each other. Then, we cannot always track the correct 3-D position. Instead of finding the corresponding point in each image, we set the search volume in the 3-D space, not in the 2-D image planes, so that only valid 2-D pairs are considered in the proposed search process. The tracking process is as follows. First, we set the search volume. The 3-D coordinates of the search volume are projected on two in each image plane. We perform template matching at the projected pixel in each image. The similarity of the 3-D position is computed from two dissimilarities in the two images. We search for the position that has the maximum similarity in the search volume, and we obtain the correct correspondence result. We incorporate this technique into our tracking system, and we compare the proposed method with a method that tracks a palm motion without epipolar constraint. Our experimental results show that use of the proposed 3-D search volume makes the method accurate and efficient for tracking the 3-D motion.