This induced voltage measurement test and electromagnetic field simulation are related to the possibility of control signal malfunction by power line. Through an experiment, this research analyzed whether the voltage causing control malfunction according to the on/off status of power permitted to power line was induced to control signal line. Also, the research calculated the voltage induced to control signal line and examined the phenomenon by conducting an electro-magnetic field-specific simulation through the finite element method for the cable model used in the experiment.

This paper describes an approach to detect the entrance of building with hopeful that it will be applied for autonomous navigation robot. The entrance is an important component which connects internal and external environments of building. We focus on the method of entrance detection using multiple cues. The information of entrance characteristics such as relative height and position on the building is considered. We adopt the probabilistic model for entrance detection by defining the likelihood of various features for entrance hypotheses. To do so we first detect building’s surfaces. Secondly, wall region and windows are extracted. The remained regions except the wall region and windows are considered as candidate of entrance. Finally, the entrance is identified by its probabilistic model.

This paper describes an approach to training a database of building images under the supervision of a user. Then it will be applied to recognize buildings in an urban scene. Given a set of training images, we first detect the building facets and calculate their properties such as area, wall color histogram and a list of local features. All facets of each building surface are used to construct a common model whose initial parameters are selected randomly from one of these facets. The common model is then updated step-by-step by spatial relationship of remaining facets and SVD-based (singular value decomposition) approximative vector. To verify the correspondence of image pairs, we proposed a new technique called cross ratio-based method which is more suitable for building surfaces than several previous approaches. Finally, the trained database is used to recognize a set of test images. The proposed method decreases the size of the database approximately 0.148 times, while automatically rejecting randomly repeated features from the scene and natural noise of local features. Furthermore, we show that the problem of multiple buildings was solved by separately analyzing each surface of a building.

This paper describes an approach to analyze geometrical information of building images for understanding outdoor environment of autonomous navigation robot. Line segments and color information are used to classily a building with the other objects such as sky, trees, and roads. The line segments and their two neighboring regions are extracted from detected edges in image. The model of line segment (MLS) consists of color information of neighbor regions. This model rules out the line segments of non-building face. A building face converges into dominant vanishing points (DVPs) which include one vertical point and one of five horizontal points in maximum. The intersection of vertical and horizontal lines creates a facet of building. The geometrical characteristics such as the center coordinates, area, aspect ratio and aligned coexistence are used for extracting the windows in the building facet. In experiments, 150 building faces and 1607 windows were detected from the database of outdoor environment. We found that this result shows 94.46% detection rate. These experimental images were all taken in Ulsan metropolitan city in Korea under difference of viewpoints, daytime, camera system and weather condition.

This paper presents the method to recognize objects for autonomous robot navigation in outdoor environment. The proposition of the method segments from an image taken by a moving robot in an outdoor environment. The method begins with object segmentation, which uses multiple features to obtain the object of segmented region. Multiple features are color, context information, line segments, edge, Hue Co-occurrence Matrix (HCM), Principal Components (PCs) and Vanishing Points (VPs). We model the objects of outdoor environment that define their characteristics individually. We segment the region as a mixture using the proposed features and methods. Objects can be detected when we combine predefined multiple features. Next, the stage classifies the object into natural and artificial ones. We detect sky and trees of natural objects. And we detect building of artificial objects. The last stage shows the combination of appearance and context information. We implement the result of object segmentation using multiple features through experiments.

The most important things to realize such an intelligent system are core functions such as landmark detection, recognition and reconstruction. Since where we have core functions, the intelligent system can propagate other procedures like navigation, mapping, localization, etc. Thus, this paper describes an approach to construct a structural data for core functions by using geometrical structure of building. Firstly, line segments are detected. Then several processes such as rejecting noises, calculating dominant vanishing points, filtering the edges of building are used to detect the building surfaces. The criteria are created for decision of building detection function. Secondly, for each surface, a generative model including area, wall histogram and a list of local features are computed for the recognition function. Finally, the geometrical features as windows, doors, floors or rooms are estimated for reconstructing the building. The proposed method has been performed with large databases and sound results of all functions.

This paper describes an approach to detect the components, specially the windows, of building for understanding and exploring environment of outdoor robot. Firstly, building surface and wall region are extracted then the wall region is binarized. The noises such as the thin connection, small regions are rejected. The context information and multiple cues such as the coordinates, aspect ratio and aligned coexistence are used to detect the windows. The proposed approach has been experimented with 150 surfaces comprising 1607 windows. We obtained 93.34% detection rate. Furthermore, the method also extracts the candidate regions for detecting the doors of building.

This paper describes the method to know objects for autonomous robot navigation in an unknown outdoor environment. The method segments the objects from an image taken by moving robot on outdoor environment. In the beginning object segmentation, this uses multiple features to obtain the objects of segmented region. Multiple features are color, context information, line segments, edge, Hue Co-occurrence Matrix (HCM), Principal Components (PCs) and Vanishing Points (VPs). The model of the objects for outdoor environment defines their characteristics individually. We segment the region as mixture using the proposed features and methods. Next the stage classifies the object into natural and artificial ones. We detect sky and trees of natural object and detect building of artificial object using the combination of appearance and context information. Then we estimate the dimensions of building. Extensive experiments with the object segmentation and analysis on outdoor environment confirm the validity of the approach.

This paper describes an approach to extract windows by analyzing geometrical characteristics of building surface. Firstly, building surfaces are detected and then wall region is extracted by using hue color of pixel; this step was well described in our previous works. The non-wall regions are considered as candidates of other components of building such as windows, doors, columns and so on. To extract the windows, the image of candidates is recovered in rectangular shape. Then the ambiguous candidates which have irregular shape, for example, long and thin or very small are coarsely rejected. The geometrical characteristics such as the center coordinates, area, aspect ratio and the aligned coexistence are used for extracting the windows. The proposed approach has been experimented for a database with 150 building surfaces comprising 1607 windows. We obtained 93.34% extraction rate.

This paper describes an approach to segment and recognize multiple buildings in the urban environment for robot intelligence. By grouping line segments which coincide with a common vanishing point, the non-building and building images are distinguished. The facets of building are detected and represented by the meshes of skewed parallelograms. The doors, wall region and windows are then estimated by merging the skewed parallelograms with similar color. To recognize a test image, each facet is described by its area, wall color histogram and a list of scale invariant feature transform (SIFT) descriptors. We selected a small number of SIFT features adapted with visual properties of buildings to represent the facet. To analyze multiple buildings, maximum numbers of dominant vanishing points are calculated for vertical and horizontal directions are one and five, respectively. In the first experiment, a set of 880 images is classified into building and non-building images. The second experiment is for recognizing a set of 80 test images from 500 image database. All images were taken from more than 100 buildings in Ulsan metropolitan city in South Korea under different conditions like viewpoints, camera systems, weather and seasons. We obtain 97% and 97.5% rate of correct segmentation and recognition, respectively.

This paper describes an approach to build a common model of building from different viewpoints. Then we apply to recognize building surfaces. For each image, buildingpsilas characters such as facets, areas, hue color histogram and a list of local features are calculated by our previous works. All correspondent facets are selected by supervision of user when the database is training. To calculate the characters of common model, we proposed a new method by using singular value decomposition (SVD). Given two or more similar vectors, SVD-based method computes an approximate vector which not only represents to the components but also automatically reduces the random noise. By using the common model, the number of facets and local features in the database are remarkably reduced. Therefore, the recognition rate is improved.

This paper describes an approach to recognize building surfaces. A building image is analyzed to extract the natural characters such as the surfaces and their areas, vanishing points, wall region and a list of SIFT feature vectors. These characters are organized as a hierarchical system of features to describe a model of building and then stored in a database. Given a new image, the characters are computed in the same form with in database. Then the new image is compared against the database to choose the best candidate. A cross ratio based algorithm, a novel approach, is used to verify the correct match. Finally, the correct match is used to update the model of building. The experiments show that the approach method clearly decreases the size of database, obtains high recognition rate. Furthermore, the problem of multiple buildings can be solved by separately analyzing each surface of building.

This paper presents a method to segment the region of objects in outdoor scene for autonomous robot navigation. The proposition of the method segments from an image taken by moving robot on outdoor Scene. The method begins with object segmentation, which uses multiple features to obtain the object of segmented region. Multiple features are color, edge, line segments, Hue Co-occurrence Matrix (HCM), Principal Components (PCs) and Vanishing Points (VPs). Model the objects of outdoor scene that define their characteristics individually. We segment the region as mixture using the proposed features and methods. Objects can be detected when we combine predefined multiple features. Next, the stage classifies the object into natural and artificial ones. We detect sky and trees of natural object and building of artificial object. Finally, the last stage shows the combination of appearance and context information. We confirm the result of object segmentation through experiments by using multiple features and context information.

This paper describes an approach to recognize buildings. The characters of building such as facets, their area, vanishing points, wall histogram and a list of local features are extracted and then stored in a database. Given a new image, the facet with biggest area is compared against the database to choose the closest pose. Novel methods of cross ratio-based refinement and SVD (singular value decomposition) based method are used to increase the recognition rate, increase the number of correspondences between image pairs and decrease the size of database. The proposed approach has been performed with 50 interest buildings containing 1050 images and a set of 50 test images. All images are taken under general conditions like different weather, seasons, scale, viewpoints and multiple buildings. We obtained 100(%) recognition rate.

This paper describes an approach to detect the buildings in the urban environment. Visual and geometrical features of line segments are used to classify the building in the images. The buildings are also distinguished with other objects like sky, tree, bush and roads. Firstly, the line segments of building and non-building patterns are separated. The natural features are the contrast between two neighbored regions of segment, vanishing points, the appeared density, the vertical and horizontal alongside distributions. Those features are used to step-by-step reduce the segments of non-building pattern. The rests called the basic segments are grouped to create a mesh of skewed parallelograms. Each mesh represents a partial face of buildings. Finally, the faces or facets of building are detected by combining the neighbored partial faces. The building facet is refined again by its area. The proposed approach has been experimented for over 800 test images with the high rate of detection results.

This paper describes a method to know objects in outdoor environment for autonomous robot navigation. The proposition of the method segments and recognizes the object from an image taken by moving robot in outdoor environment. Features are color, straight line, edge, HCM (Hue Co-occurrence Matrix), PCs (Principal Components), vanishing point and geometrical information. We classify the object natural and artificial. We detect tree of natural object and building of artificial object. Then we define their characteristics individually. In the process, we segment regions objects included by preprocessing. Objects can be recognized when we combine predefined multiple features. The correct object recognition of proposed system is over 92% among our test database which consist about 1200 images. We confirm the result of image segmentation using multiple features and object recognition through experiments.

A new remote control method of a moving robot is proposed, where a moving robot is moved according to the pointing position and orientation of the remote controller. The remote controller consists of the camera and gyroscopes. The landmark in a moving robot is recognized by the camera in the remote controller and a robot is moved in the camera's pointing position and orientation. The 'virtual link' term is used since the robot is moved as if there is a link between the robot and the remote controller. Gyroscopes are also used in the remote controller so that fast estimation of the camera's pointing position and orientation is possible. The proposed method is verified through experiments.