• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1934-1935
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• 2011

In order for a service robot to perform several tasks, basically autonomous navigation technique such as localization, mapping, and path planning is required. The localization (estimation robot's pose) is fundamental ability for service robot to navigate autonomously. In this paper, we propose a new system for point pattern matching based visual global localization using spot lightings in ceiling. The proposed algorithm us suitable for system that demands high accuracy and fast update rate such a guide robot in the exhibition. A single camera looking upward direction (called ceiling vision system) is mounted on the head of the mobile robot and image features such as lightings are detected and tracked through the image sequence. For detecting more spot lightings, we choose wide FOV lens, and inevitably there is serious image distortion. But by applying correction calculation only for the position of spot lightings not whole image pixels, we can decrease the processing time. And then using point pattern matching and least square estimation, finally we can get the precise position and orientation of the mobile robot. Experimental results demonstrate the accuracy and update rate of the proposed algorithm in real environments.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.585-593
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• 2006

In this paper, we propose a vision-based simultaneous localization and map-building (SLAM) algorithm. SLAM problem asks the location of mobile robot in the unknown environments. Therefore, this problem is one of the most important processes of mobile robots in the outdoor operation. To solve this problem, Extended Kalman filter (EKF) is widely used. However, this filter requires computational power (${\sim}O(N)$, N is the dimension of state vector). To reduce the computational complexity, we applied compressed extended Kalman filter (CEKF) to stereo image sequence. Moreover, because the mobile robots operate in the outdoor environments, we should estimate full d.o.f.s of mobile robot. To evaluate proposed SLAM algorithm, we performed the outdoor experiments. The experiment was performed by using new wheeled type mobile robot, Robhaz-6W. The performance results of CEKF SLAM are presented.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.3
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• pp.316-327
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• 2007

In this study, a Korean native cattle strain (Hanwoo) evidencing high performance in terms of both meat quality and quantity was employed in the generation of 150,000 BAC clones with an average insert size of 140 kb, and corresponding to about a 6X coverage of bovine chromosomal DNA. The BAC clones were pooled in a mini-scale via three rounds of a pooling protocol, and the efficiency of this pooling protocol was evaluated by testing the accuracy of accessibility to the positive clones, via a PCR-based screening method. Two sets of primers designed from each of two known genes were tested, and each yielded 2 or 3 positive clones for each gene, thereby indicating that the BAC library pooling system was appropriate with regard to the accession of the target BAC clones. Analyses of $3.3{\times}10^6$ base pairs obtained from the 7,090 BAC end sequence (BES) showed that 34.88% of the DNA sequence harbored the repetition sequence. Analysis of the 7,090 BES to the $1^{st}$ and $2^{nd}$ generation radiation hybrid map of the cattle genome, using the COMPASS program designed for the construction of a cattle-human comparative mapping, resulted in the localization of a total of 1,374 clones proximal to 339 $1^{st}$ generation markers, and 1,721 clones proximal to 664 $2^{nd}$ generation markers. Collectively, the BAC library and pooling system of the BAC clones from the Korean cattle, coupled with the chromosome-localized BAC clones, will provide us with novel tools for the excavation of desired clones for genome mapping and sequencing, and will also furnish us with additional information regarding breed differences in cattle.

• Mycobiology
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• v.50 no.5
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• pp.259-268
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• 2022

The nuclear import of proteins is a fundamental process in the eukaryotes including plant. It has become evident that such basic process is exploited by nuclear effectors that contain nuclear localization signal (NLS) and are secreted into host cells by fungal pathogens of plants. However, only a handful of nuclear effectors have been known and characterized to date. Here, we first summarize the types of NLSs and prediction tools available, and then delineate examples of fungal nuclear effectors and their roles in pathogenesis. Based on the knowledge on NLSs and what has been gleaned from the known nuclear effectors, we point out the gaps in our understanding of fungal nuclear effectors that need to be filled in the future researches.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.200-204
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• 2008

In this paper, to solve localization problem for out-door navigation of robotic vehicles, a particle filter based 3D object recognition framework that can estimate the pose of a building or its entrance is presented. A particle filter framework of multiple evidence fusion and model matching in a sequence of images is presented for robust recognition and pose estimation of 3D objects. The proposed approach features 1) the automatic selection and collection of an optimal set of evidences 2) the derivation of multiple interpretations, as particles representing possible object poses in 3D space, and the assignment of their probabilities based on matching the object model with evidences, and 3) the particle filtering of interpretations in time with the additional evidences obtained from a sequence of images. The proposed approach has been validated by the stereo-camera based experimentation of 3D object recognition and pose estimation, where a combination of photometric and geometric features are used for evidences.

• KSCI Review
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• v.14 no.1
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• pp.241-248
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• 2006

The recognition of a relatively big and rarely movable object. such as refrigerator and air conditioner, etc. is necessary because these objects can be crucial global stable features of Simultaneous Localization and Map building(SLAM) in the indoor environment. In this paper. we propose a novel method to recognize these big objects using a sequence of 3D scenes. The particles representing an object to be recognized are scattered to the environment and then the probability of each particles is calculated by the matching test with 3D lines of the environment. Based on the probability and degree of convergence of particles, we can recognize the object in the environment and the pose of object is also estimated. The experimental results show the feasibility of incremental object recognition based on particle filtering and the application to SLAM

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.297-303
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• 2007

In this paper, the stroke-based text extraction algorithm for digital video is proposed. The proposed algorithm consists of four stages such as text detection, text localization, text segmentation and geometric verification. The text detection stage ascertains that a given frame in a video sequence contains text. This procedure is accomplished by morphological operations for the pixels with higher possibility of being stroke-based text, which is called as seed points. For the text localization stage, morphological operations for the edges including seed points ate adopted followed by horizontal and vortical projections. Text segmentation stage is to classify projected areas into text and background regions according to their intensity distribution. Finally, in the geometric verification stage, the segmented area are verified by using prior knowledge of video text characteristics.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.114-122
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• 2011

We propose an automatic localization technique based on Uniform Arc Length Sampling (UALS) of 360 degree range sensor data. The proposed method samples 3D points from dense a point-cloud which is acquired by the sensor, registers the sampled points to a digital surface model(DSM) in real-time, and determines the location of an Unmanned Ground Vehicle(UGV). To reduce the sampling and registration time of a sequence of dense range data, 3D range points are sampled uniformly in terms of ground sample distance. Using the proposed method, we can reduce the number of 3D points while maintaining their uniformity over range data. We compare the registration speed and accuracy of the proposed method with a conventional sample method. Through several experiments by changing the number of sampling points, we analyze the speed and accuracy of the proposed method.