• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.141-143
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• 2004

This paper represents 3D line segment extraction method, which can be used in generating 3D rooftop model. The core of our method is that 3D line segment is extracted by using line fitting of elevation data on 2D line coordinates of ortho-image. In order to use elevations in line fitting, the elevations should be reliable. To measure the reliability of elevation, in this paper, we employed the concept of self-consistency. We carry out the experiment of 3D line extraction using synthetic images generated from Avenches data set of Ascona aerial images.

• Journal of IKEEE
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• v.13 no.2
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• pp.181-185
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• 2009

In this paper, we are concerned with a 3D line segment extraction method by area-based stereo matching technique. The main idea is based on line fitting of elevation data on 2D line coordinates of ortho-image. Elevation data and ortho-image can be obtained by well-known area-based stereo matching technique. In order to use elevation in line fitting, the elevation itself should be reliable. To measure the reliability of elevation, in this paper, we employ the concept of self-consistency. We test the effectiveness of the proposed method with a quantitative accuracy analysis using synthetic images generated from Avenches data set of Ascona aerial images. Experimental results indicate that our method generates 3D line segments almost 7.5 times more accurate than raw elevations obtained by area-based method.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.59-64
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• 2002

This paper presents a method for line segment extraction for 3-d building reconstruction. Building roofs are described as a set of planar polygonal patches, each of which is extracted by watershed-based image segmentation, line segment matching and coplanar grouping. Coplanar grouping and polygonal patch formation are performed per region by selecting 3-d line segments that are matched using epipolar geometry and flight information. The algorithm has been applied to high resolution aerial images and the results show accurate 3-d building reconstruction.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.317-322
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• 2018

We use edge detection technique for the input image to extract the entire edges of the object in the image and then select only the edges that construct the outline of the object. By examining the positional relation between these pixels composing the outline, a simplified version of the outline of the object in the input image is generated by removing unnecessary pixels while maintaining the condition of connection of the outline. For each pixel constituting the outline, its direction is calculated by examining the positional relation with the next pixel. Then, we group the consecutive pixels with same direction into one and then change them to a line segment instead of a point. Among those line segments composing the outline of the object, a line segment whose length is smaller than a predefined minimum length of acceptable line segment is removed by merging it into one of the adjacent line segments. As a result, an outline composed of line segments of over a certain length is obtained through this process.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.655-670
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• 2014

In this paper, we propose an efficient and robust lane detection method for detecting immediate left and right lane boundaries of the lane in the roads. The proposed method are based on hyperbolic lane model and the reliable line segment clustering. The reliable line segment cluster is determined from the most probable cluster obtained from clustering line segments extracted by the efficient LSD algorithm. Experiments show that the proposed method works robustly against lanes with difficult environments such as ones with occlusions or with cast shadows in addition to ones with dashed lane marks, and that the proposed method performs better compared with other lane detection methods on an CMU/VASC lane dataset.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.174-179
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• 2005

This paper presents 3D line segment extraction method, which can be used in generating 3D rooftop model. The core of our method is that 3D line segment is extracted by using line fitting of elevation data on 2D line coordinates of ortho-image. In order to use elevations in line fitting, the elevations should be reliable. To measure the reliability of elevation, in this paper, we employ the concept of self-consistency. We test the effectiveness of the proposed method with a quantitative accuracy analysis using synthetic images generated from Avenches data set of Ascona aerial images. Experimental results indicate that the proposed method shows average 30 line errors of .16 - .30 meters, which are about $10\%$ of the conventional area-based method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.709-713
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• 2005

A new algorithm is proposed for the extraction of line segments to construct 3D wire-frame models of building from the high-resolution aerial images. The purpose of this study Is the accurate and effective extraction of line segments, considering the problems such as discordance of lines and blurred edges existing in the conventional methods. Using the edge map extracted from aerial images, chain code tracking of edges was performed. Then, we extract the line segments considering the strength of edges and the direction of them. SUSAN (Smallest Uni-value Segment Assimilating Nucleus) algorithm proposed by Smith was used to extract an edge map. The proposed algorithm consists of 4 steps: removal of the horizontal, vertical and diagonal components of edges to reduce non-candidate point of line segments based on the chain code tracking of the edge map, removal of contiguous points, removal of the same angle points, and the extraction of the start and end points to be line segments. By comparing the proposed algorithm with Boldt algorithm, better results were obtained regarding the extraction of the representative line segments of buildings, having relatively less extraction of unnecessary line segments.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.40-43
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• 1997

This paper presents a 3D object recognition method for generation of 3D environmental map or obstacle recognition of mobile robots. An active light source projects a stripe pattern of light onto the object surface, while the camera observes the projected pattern from its offset point. The system consists of a laser unit and a camera on a pan/tilt device. The line segment in 2D camera image implies an object surface plane. The scaling, filtering, edge extraction, object extraction and line thinning are used for the enhancement of the light stripe image. We can get faithful depth informations of the object surface from the line segment interpretation. The performance of the proposed method has demonstrated in detail through the experiments for varies type objects. Experimental results show that the method has a good position accuracy, effectively eliminates optical noises in the image, greatly reduces memory requirement, and also greatly cut down the image processing time for the 3D object recognition compared to the conventional object recognition.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.375-382
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• 2002

Handwritten character recognition is classified into on-line handwritten character recognition and off-line handwritten character recognition. On-line handwritten character recognition has made a remarkable outcome compared to off-line hacdwritten character recognition. This method can acquire the dynamic written information such as the writing order and the position of a stroke by means of pen-based electronic input device such as a tablet board. On the contrary, Any dynamic information can not be acquired in off-line handwritten character recognition since there are extreme overlapping between consonants and vowels, and heavily noisy images between strokes, which change the recognition performance with the result of the preprocessing. This paper proposes a method that effectively extracts the stroke including dynamic information of characters for off-line Korean handwritten character recognition. First of all, this method makes improvement and binarization of input handwritten character image as preprocessing procedure using watershed algorithm. The next procedure is extraction of skeleton by using the transformed Lu and Wang's thinning: algorithm, and segment pixel array is extracted by abstracting the feature point of the characters. Then, the vectorization is executed with a maximum permission error method. In the case that a few strokes are bound in a segment, a segment pixel array is divided with two or more segment vectors. In order to reconstruct the extracted segment vector with a complete stroke, the directional component of the vector is mortified by using right-hand writing coordinate system. With combination of segment vectors which are adjacent and can be combined, the reconstruction of complete stroke is made out which is suitable for character recognition. As experimentation, it is verified that the proposed method is suitable for handwritten Korean character recognition.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.176-180
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• 2003

The paper presents an efficient method of extracting line segment in a local map of a robot's surroundings. The local map is composed of 2-D grids that have both the occupancy and orientation probabilities using sonar sensors. To find the shape of an object in a local map from orientation information, the orientations are clustered into several groups according to their values. The line segment is , then, extracted from the clusters based on Hough transform. The proposed technique is illustrated by experiments in an indoor environment.