Vehicle Class Recognition Using 3d Cg Models

This paper describes a robust method for recognizing vehicle classes. In our previous work, we have developed a vehicle recognition system based on local-feature configuration, which is a generalization of the eigen-window method. This system could recognize one vehicle class very accurately, but there have been limitations in recognizing several classes, when they are quite similar to each other. In this paper, we describe the improvements of our recognition system to distinguish four classes, namely sedan, wagon, mini-van and hatchback. The system requires training images of all target vehicle classes. These training images are easily created using a 3-dimentional computer graphic (3D-CG) tool. Using CG training images dispenses with much of the trouble of collecting real training images, and causes no effect on accuracy. Outdoor experimental results have shown that this recognition system can classify vehicles in real images with an accuracy of more than 80%

Local-feature Based Vehicle Recognition System Using Parallel Vision Board

This paper describes a robust method for recognizing vehicles. Our system is based on local-feature configuration, and we have already shown that it works very well in infrared images. The algorithm is based on our previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) it can detect even if part of vehicles is occluded. (2) it can detect even if vehicles are translated due to running out of the lanes. (3) it does not require us to segment vehicle areas from input images. It is true that we have first developed our system with infrared images, but it is not essential for our system to employ infrared images. In this paper, applying our system on images of super wide-angle, we have shown that our system is effective to optical images, performing two outdoor experiments. Our system is good at detecting locations of vehicles, hence it will be useful for not only vehicle detection but also such application, ETC, DSRC or so, that system needs to know with which vehicle it communicates

Transparent Surface Modeling from a Pair of Polarization Images

We propose a method for measuring surface shapes of transparent objects by using a polarizing filter. Generally, the light reflected from an object is partially polarized. The degree of polarization depends upon the incident angle which, in turn, depends upon the surface normal. Therefore

Vehicle Recognition with Local-Feature Based Algorithm Using Parallel Vision

This paper describes a robust method for recognizing vehicles. Our system is based on local-feature configuration, and we have already shown that it works very well in infrared images. The algorithm is based on our previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) it can detect even if part of vehicles is occluded. (2) it can detect even if vehicles are translated due to running out of the lanes. (3) it does not require us to segment vehicle areas from input images. It is true that we have first developed our system with infrared images, but it is not essential for our system to employ infrared images. In this paper, applying our system on images of super wide-angle, we have shown that our system is effective to optical images, performing an outdoor experiment. Our system is good at detecting locations of vehicles, hence it will be useful for not only vehicle detection but also such application, ETC, DSRC or so, that system needs to know which vehicle it communicates with

Determining Shapes of Transparent Objects from Two Polarization Images

In the field of computer vision, beneficial methods of measuring surface shape of transparent objects such as glasses have rarely been proposed. In this paper, we propose a convenient and inexpensive method for measuring the surface shape of transparent objects. The degree of polarization of the light reflected from the object surface depends on the reflection angle which, in turn, depends on the object's surface normal# thus, by measuring the degree of polarization, we are able to calculate the surface normal of the object. But unfortunately, the relationship between degree of polarization and surface normal is not 1 to 1# thus, to obtain the true surface normal, wehave to resolvethisambiguity problem. In this paper, we explain the method of resolving the ambiguityby using the differential-geometrical property of the object surface

「画像の認識・理解シンポジウム(MIRU2005) 」 2005年7月 Analysis of City Range Image Using Digital Map

Abstract In existing research about 3D city map, geometrical information of buildings ’ facade is derived from video image. In order to improve quality of the geometrical information, range image is used in our research. At first range image of existing buildings is acquired using laser scanners mounted on data acquisition vehicle which runs in the street. After removing obstacles from original range image, edge detection based segmentation can be used to extract range image of different buildings. Through matching patterns from edge map with that from existing 2D digital map, one building’s corresponding range image can be determined. Thus precise geometrical information of the building’s facade can be obtained from its corresponding range image

Recognizing Vehicles in Infra-red Images Using

This paper describes a method for vehicle recognition, in particular, for recognizing a vehicle's make and model. Our system is designed to take into account the fact that vehicles of the same make and model number come in different colors; to deal with this problem, our system employs infra-red images, thereby eliminating color differences. Another reason for the use of infra-red images is that it enables us to use the same algorithm both day and night. This ability is particularly important because the algorithm must be able to locate many feature points, especially at night. Our algorithm is based on configuration of local features. For the algorithm, our system first makes a compressed database of local features of a target vehicle from training images given in advance; the system then matches a set of local features in the input image with those in training images for recognition. This method has the following three advantages: (1) It can detect even if part of the target vehicle is occluded. (2) It can detect even if the target vehicle is translated due to running out of the lanes. (3) It does not require us to segment a vehicle part from input images. We havetwo implementations of the algorithm. One is referred to as the eigen-window method, while the other is called the vector-quantaization method. The former method is good at recognition, but is not very fast. The latter method is not very good at recognition but it is suitable for an IMAP parallel image-processing board; hence, it can be fast

Vehicle Class Recognition of Street-Parking Vehicles from Side-View Range Images

This paper describes a novel method for recognizing the classes of street-parking vehicles. We have already developed the following two systems: one is vehicle recognition system based on local-feature configuration, and the other is detecting street-paring vehicles from side-view range images. In this paper, we combine these two systems to develop a new system with which we can not only count the number of street-parking vehicles but also recognize their class of vehicle type such as sedan, wagon, mini-van or so. We have confirmed that our classification algorithm is still robust on range images, performing outdoor experiments. Our system can recognize four vehicle classes of sedan, wagon, mini-van and hatchback from outdoor range images with accuracy of about 80%

3D Database System of Mercede Church: The Use of 3D Models as an Interface to Information

Advancement in 3D modelling technologies has enabled us to obtain high resolution 3D models of real objects, for example historic buildings. Unfortunately, however, the application tools for large models have not been existed, which allow experts and other people outsides of the field of computer engineering to utilize the 3D models effectively. We introduce a GUI-based application tool enabling users to easily add and manipulate optional information on the certain area of 3D models, and to retrieve that information when viewing the 3D models. The "3D database system" can deal with much information by connecting to existing database systems. In addition to that, we realize efficient selecting surfaces and accessing information on the regions for large models, using a segmentation method of 3D models' surfaces. We demonstrated this tool using the 3D model of Mercede Church, in Panama City