• Proceedings of the KSRS Conference
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• v.2
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• pp.643-646
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• 2006

This paper addresses the approach to extract linear features from satellite imagery using an efficient segmentation method. The extraction of linear features from satellite images has been the main concern of many scientists. There is a need to develop a more capable and cost effective method for the Iranian map revision tasks. The conventional approaches for producing, maintaining, and updating GIS map are time consuming and costly process. Hence, this research is intended to investigate how to obtain linear features from SPOT satellite imagery. This was accomplished using a discontinuity-based segmentation technique that encompasses four stages: low level bottom-up, middle level bottom-up, edge thinning and accuracy assessment. The first step is geometric correction and noise removal using suitable operator. The second step includes choosing the appropriate edge detection method, finding its proper threshold and designing the built-up image. The next step is implementing edge thinning method using mathematical morphology technique. Lastly, the geometric accuracy assessment task for feature extraction as well as an assessment for the built-up result has been carried out. Overall, this approach has been applied successfully for linear feature extraction from SPOT image.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.313-317
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• 2003

This paper suggested the initial edge map through the pre-processing of vague image before apply the GVF snake algorithm. The reason obtain for detail object outline and time efficiency GVF snake algorithm feasible extracted concave edge but mistake interested object edge for the around others. So it need to trim about the object around edges. The method is using Pixel morphological reconstruction, edge extraction mask and threshoding. The result, defend fallen local minimum edge energy and reduce iteration.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.159-161
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• 2003

Fitting the projected wire-frame model to the detected edge pixels on images by using least-squares approach, called Least-squares Model-image Fitting (LSMIF), is the key of the Model-based Building Extraction (MBBE). It is implemented by iteratively adjusting the model parameters to minimize the squares sum of distances from the extracted edge pixels to the projected wire-frame. This paper describes a series of experiments and studies on various factors affect the fitting results, including the edge detectors, the weighting rules, the initial value of parameters, and the number of overlapped images. The experimental result is not only helpful to clarify the influences of each factor, but is also able to enhance the robustness of the LSMIF algorithm.

• 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.

• Journal of Digital Contents Society
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• v.16 no.6
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• pp.951-959
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• 2015

Scene text extraction is important because it offers some important information on different image based applications pouring in current smart generation. Edge-Enhanced MSER(Maximally Stable Extremal Regions) which enhances the boundaries using the canny operator after extracting the basic MSER shows excellent performance in terms of text extraction. But according to setting the threshold of the canny operator, the result images using Edge-Enhanced MSER are different, so there needs a method figuring out the threshold. In this paper, we propose a AEMSER(Adaptive Edge-enhanced MSER) that applies the method extracting the boundary using the middle value of histogram to Edge-Enhanced MSER to get the canny operator's threshold. The proposed method can acquire better result images than the existing methods because it extracts the area only for the obvious boundaries.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.167-170
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• 2000

In this paper, we propose the edge compensation algorithm which connects the adjacent edges without losing the information of the skeletons on the edge image. The proposed edge compensation algorithm is composed of succeeding two steps. In the first step, the uplifted image is obtained by applying the uplifting process to the edge image. The next step is to extract the edge image from the uplifted image using the skeleton extraction algorithm. Experimental results show that the proposed method connects the adjacent edges without the distortion of the original edge information compared to the traditional method

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.97-100
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• 1996

In this paper, we describe an algorithm which estimate road following direction using the vanishing point property and obstacle detection. This method of detecting the lane markers in a set of continuous lane highway images using linear approximation is presented. This algorithm is designed for accurate and robust extraction of this data as well as high processing speed. Also, this algorithm reckon distance and chase about an obstacle. It include four algorithms which are lane prediction, lane extraction, road following parameter estimation and obstacle detection algorithm. High accuracy was proven by quantitative evaluation using simulated images. Both robustness and the practicality of real time video rate processing were then confirmed through experiment using VTR real road images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1167-1174
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• 2006

Text region detection from a natural scene is useful in many applications such as vehicle license plate recognition. Therefore, in this paper, we propose a text region extraction method using pattern histogram of character-edge maps. We create 16 kinds of edge maps from the extracted edges and then, we create the 8 kinds of edge maps which compound 16 kinds of edge maps, and have a character feature. We extract a candidate of text regions using the 8 kinds of character-edge maps. The verification about candidate of text region used pattern histogram of character-edge maps and structural features of text region. Experimental results show that the proposed method extracts a text regions composed of complex background, various font sizes and font colors effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.980-997
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• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2464-2478
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• 2013

Image stitching techniques produce an image with a wide field-of-view by aligning multiple images with a narrow field-of-view. While conventional algorithms successfully stitch images with a small parallax, structure misalignment may occur when input images contain a large parallax. This paper presents an image stitching algorithm that aligns images with a large parallax by regional linear warping. To this end, input images are first approximated as multiple planar surfaces, and different linear warping is applied to each planar surface. For approximating input images as multiple planar surfaces, the concept of dominant edges is introduced. Dominant edges are defined as conspicuous edges of lines in input images, and extracted dominant edges identify the boundaries of each planar surface. Dominant edge extraction is conducted by detecting distinct changes of local characteristics around strong edge pixels. Experimental results show that the proposed algorithm successfully stitches images with a large parallax without structure misalignment.