This paper presents a technique for the detection of roads in a spaceborne synthetic aperture radar (SAR) image using a genetic algorithm (GA). Roads in a spaceborne SAR image can be modeled as curvilinear structures that possess width. Curve segments, which represent the candidate positions for roads, are extracted from the image using a curvilinear structure detector, and the roads are accurately detected by grouping those curve segments. For this purpose, the authors designed a grouping method based on a GA, which is a global optimization method. They combined perceptual grouping factors with it and tried to reduce its overall computational cost by introducing a concept of region growing. In this process, a selected initial seed is grown into a finally grouped segment by the iterated GA process, which considers segments only in a search region. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. The authors applied their method to ERS-1 SAR and SIR-C/X-SAR images that have a resolution of about 30 m. The experimental results show that our method can accurately detect road networks as well as single-track roads and is much faster than a globally applied GA approach.

In this paper we introduce a new tool, called a pseudo-distance map (PDM), for extracting skeletons from grayscale images without region segmentation or edge detection. Given an edge-strength function (ESF) of a gray-scale image, the PDM is computed from the ESF using the partial differential equations we propose. The PDM can be thought of as a relaxed version of a Euclidean distance map. Therefore, its ridges correspond to the skeleton of the original gray-scale image and it provides information on the approximate width of skeletonized structures. Since the PDM is directly computed from the ESF without thresholding it, the skeletonization result is generally robust and less noisy. We tested our method using a variety of synthetic and real images. The experimental results show that our method works well on such images.

This paper presents an edge-enhancing super-resolution algorithm using anisotropic diffusion technique. Because we solve the super-resolution problem by incorporating anisotropic diffusion, our technique does more than merely reconstruct a high-resolution image from several overlapping noisy low-resolution images and preserve them. In addition to reducing image noise during the restoration process, our method also enhances edges. We apply this technique to a video stream which can be aligned by 3/spl times/3 projective transformations.

This paper presents a technique for the detection of roads in a spaceborne SAR image using a genetic algorithm. Roads in a spaceborne SAR image can be modelled as curvilinear structures with some thickness. Curve segments, which represent the candidate positions of roads, are extracted from the image using a curvilinear structure detector, and roads are detected accurately by grouping those curve segments. For this purpose, we designed a grouping method based on a genetic algorithm (GA), which is a global optimization method. We combined perceptual grouping factors with it, and tried to reduce its overall computational cost by introducing a concept of region growing. In this process, a selected initial seed is grown into a finally grouped segment by the iterated GA process which considers segments only in a search region. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. We applied our method to ERS-1 SAR images that have a resolution of about 30 meters. The experimental results show that our method can detect roads accurately and is much faster than a globally applied GA approach.

This paper presents an automatic and map-based road detection algorithm for spaceborne SAR images, aiming at subpixel accuracy on average. We propose a two-step algorithm which is composed of the coarse and the fine matching. In the coarse matching step, we find roughly road positions by using water areas or the generalized Hough transform. The fine matching step detects roads accurately by applying the active contour models (snake) to the novel field called the potential field. In the fine matching step, we first extract ridges or ravines of curvilinear structures in SAR images, then construct the potential field by smoothing extracted structures. The proposed method utilizes digital maps to extract information of the road model in the coarse matching and to initialize positions and shapes of snakes in the fine matching. Experimental results show the performance of detecting roads with the average error less than one pixel.

This paper describes a method for calibrating a stereo-camera using its pure translational motion. The left and right cameras translate in several directions simultaneously. For each camera and each translation, an image sequence is obtained, feature points being extracted and tracked. An epipole is estimated for each image sequence and calibration is carried out using the relation between known translations and estimated epipoles. After finding calibration and rotation matrices of each camera, 3D reconstruction of feature points is performed. Reconstructed points are used to find translational relation between both cameras. The method was tested with real image data, and experimental results show that it is applicable in practice.

In this paper we present a two-stage classifier for the recognition of freight train ID numbers under outdoor environment. This kind of recognition is different from ordinary OCR in that images of numerals are usually corrupted by "noises" caused by dirty materials, varying outdoor illumination, and so on. We focused our attention on developing a robust classifier against such noises. The two-stage classifier is constructed by cascade connection of a main classifier and an auxiliary classifier. Since ID numbers are printed with relatively small size and shape variations, we adopted a template-matching technique in the main classifier. Three top-scored candidates from the main classifier are given to the auxiliary classifier which consists of 10 expert neural networks. These neural nets exploit local characteristics of each numeral. We collected several hundreds of sample images to test our algorithm, and some promising results were obtained.