• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.40-47
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• 2010

This paper presents a simultaneous localization and mapping (SLAM) of an indoor hallway environment using Rao-Blackwellized particle filter (RBPF) along with a line segment as a landmark. Based on the fact that fluent line features can be extracted around the ceiling and side walls of hallway using vision sensor, a horizontal line segment is extracted from an edge image using Hough transform and is also tracked continuously by an optical flow method. A successive observation of a line segment gives initial state of the line in 3D space. For data association, registered feature and observed feature are matched in image space through a degree of overlap, an orientation of line, and a distance between two lines. Experiments show that a compact environmental map can be constructed with small number of horizontal line features in real-time.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.137-140
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• 1996

Finding line segments in an intensity image has been one of the most fundamental issues in computer vision. In complex scenes, it is hard to detect the locations of point features. Line features are more robust in providing greater positional accuracy. In this paper we present a robust "line features extraction" algorithm which extracts line feature in a single pass without using any assumptions and constraints. Our algorithm consists of five steps: (1) edge scanning, (2) edge normalization, (3) line-blob extraction, (4) line-feature computation, and (5) line linking. By using edge scanning, the computational complexity due to too many edge pixels is drastically reduced. Edge normalization improves the local quantization error induced from the gradient space partitioning and minimizes perturbations on edge orientation. We also analyze the effects of edge processing, and the least squares-based method and the principal axis-based method on the computation of line orientation. We show its efficiency with some real images.al images.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.29-36
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• 2015

In this paper, we propose the horizontal line detection using the Haar-like features and linear regression in infrared images. In the marine environment horizon image is very useful information on a variety of systems. In the proposed method Haar-like features it was noted that the standard deviation be calculated in real time on a static area. Based on the pixel position, calculating the standard deviation of the around area in real time and, if the reaction is to filter out the largest pixel can get the energy map of the area containing the straight horizontal line. In order to select a horizontal line of pixels from the energy map, we applied the linear regression, calculating a linear fit to the transverse horizontal line across the image to select the candidate optimal horizontal. The proposed method was carried out in a horizontal line detecting real infrared image experiment for day and night, it was confirmed the excellent detection results than the legacy methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1766-1784
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• 2014

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

• Korean Journal of Optics and Photonics
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• v.8 no.5
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• pp.403-409
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• 1997

We proposed a system that can perform pattern recognition based on parrallel optical feature extraction and performed experiments on this. The feature to be extracted are both 6 simple line orientations and two eigenvectors of the covariance matrix of the patterns that cannot be distinguished with the line orientation features alone. Our system consists of a feature-extraction part and a pattern-recognition part. The former that extracts the features in parallel with the multiplexed Vander Lugt filters was implemented optically, while the latter that performs the pattern recognition by the use of the extracted features was implemented in a computer. In the pattern recognition part, two methods are tested;one is to use an artificial neural network, which is trained to recognize the features directly, the other is to count the numbers of specific features simply and then to compare them with the stored reference feature numbers. We report the preliminary experimental results tested for 15 alpabet patterns with only straight line segments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.83-88
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• 2010

This study is going to suggest the technology to recognize a starfish by judging various starfish images. In case of recognition of single objects of the asterias amurensis, a starfish can be judged by using concave features and short line features but in case of multiple objects, it is impossible to extract the features of a starfish using concave features or short line so that it can't be recognized as a starfish. Accordingly, it is going to suggest the recognition technology using the features such as numbers of standard deviation, relative degree standard deviation and valid deviation of a long line by using the region central moment and a long line of multiple objects. As a result of experiments of the suggested technology, there were cases that recognition failed because the conditions of the standard deviation of a long line or the numbers of valid deviation of the relative degree couldn't satisfy the conditions but around 95% of a high recognition rate was shown.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.10
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• pp.1174-1180
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• 2011

Not all line or point features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity because it is difficult to determine the correspondence of line or point features with previously registered feature. Confused line and point features in cluttered environments leads to poor SLAM performance. We introduce a sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The reliable line feature is expressed by its end points and engaged togather in EKF SLAM to overcome the geometric limits and maintain the map consistency. Experimental results demonstrate the validity and robustness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.818-823
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• 2008

In this paper, we propose a feature extraction method which extracts directional features of handwritten numerals by using the projection runlength. Our directional featrures are obtained from four directional images, each of which contains horizontal, vertical, right-diagonal and left-diagonal lines in entire numeral shape respectively. A conventional method which extracts directional features by using Kirsch masks generates edge-shaped double line directional images for four directions, whereas our method uses the projections and their runlengths for four directions to produces single line directional images for four directions. To obtain the directional projections for four directions from a numeral image, some preprocessing steps such as thinning and dilation are required, but the shapes of resultant directional lines are more similar to the numeral lines of input numerals. Four [$4{\times}4$] directional features of a numeral are obtained from four directional line images through a zoning method. By using a hybrid feature which is made by combining our feature with the conventional features of a mesh features, a kirsch directional feature and a concavity feature, higher recognition rates of the handwrittern numerals can be obtained. For recognition test with given features, we use a multi-layer perceptron neural network classifier which is trained with the back propagation algorithm. Through the experiments with the handwritten numeral database of Concordia University, we have achieved a recognition rate of 97.85%.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.40.2-40.2
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• 2010

Polycyclic aromatic hydrocarbon (PAH) features have emerged as one of the most important infrared (IR) features since these PAH features dominate mid-IR spectra of galaxies and are ubiquitous within galactic and extragalactic objects. These PAH features have the potentials to become reliable star formation rate (SFR) proxies and diagnostics of physical conditions of interstellar medium, such as ionization states of dust grains and grain sizes. While constructing an unbiased library of 44 sample galaxies selected from 5MUSES sample, AKARI mJY Unbiased Survey of Extragalactic Sources in 5MUSES (AMUSES) intends to measure and to calibrate the PAH 3.3 mm emission feature which has not been studied extensively due to its weak strength and dearth of capable instruments. Out of 20 target galaxies, we detected the 3.3mm feature from eight galaxies and measured their line strengths, line widths and line ratios with other PAH emission features. Sample galaxies whose spectral energy distributions (SEDs) are classified as starburst-type have clearly stronger 3.3mm emission features than ones with AGN-type SEDs. We also found that there is a correlation between the PAH 3.3mm luminosity and total IR luminosity within our sample galaxies, albeit a large scatter. We further discuss implications of our results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2203-2208
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• 2014

The Hough transform (HT), that is a typical algorithm for detecting lines in images, needs considerable computational costs and easily detects pseudo-lines on the real world images, because of the large amount of features generated by their complex background or noise. This paper proposes an improved HT that add a preprocessing to estimate the validity of features to the conventional HT. The feature estimation can remove a lot of inessential features for the line detection using a pattern of $3{\times}3$ block features. Experiments using various images show that the proposed algorithm saves computational costs by removing 14%~58% of features depending on images and besides it is superior to the conventional HT in valid line detection.