• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.666-669
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• 2000

Localization is the process of aligning the robot's local coordinates with the global coordinates of a map. A mobile robot's location is basically computed by a dead reckoning scheme, but this position information becomes increasingly inaccurate during navigation due to odometry errors. In this paper, the method of building a map of a robot's environment using ultrasonic sensor data and the occupancy grid map scheme is briefly presented. Then, the search and matching algorithms to compensate for the odometry error by comparing the local map with the reference map are proposed and verified by experiments. It is shown that the compensated error is not accumulated and exists within the limited range.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.209-215
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• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.848-855
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• 2016

Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.132-142
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• 2014

Stereo camera is drawing attention as an essential sensor for future intelligence robot system since it has the advantage of acquiring not only distance but also other additive information for an object. However, it cannot match correlated point on target image for low textured region or periodic patterned region such as wall of building or room. In this paper, we propose a stereo matching technique that increase the matching performance by fusing belief propagation stereo matching algorithm and local distance measurements of 2D-laser range finder in order to overcome this kind of limitation. The proposed technique adds laser measurements by referring quad-tree based segment information on to the local-evidence of belief propagation stereo matching algorithm, and calculates compatibility function by reflecting over-segmented information. Experimental results of the proposed method using simulation and real test images show that the distance information for some low textured region can be acquired and the discontinuity of depth information is preserved by using segmentation information.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.75-85
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• 2013

To acquire depth information using stereo vision, it is required to find correspondence points between stereo image pair. Conventional stereo vision systems usually use two cameras to get disparity data. Therefore, conventional stereo matching methods cannot resolve the tradeoff problem between accuracy and precision with respect to the length of baseline. Besides, belief propagation method, which is being used recently, has a problem that matching performance is dependent on the fixed weight parameter ${\lambda}$. In this paper, we propose a modified belief propagation stereo matching technique based on multi-baseline stereo vision to solve the tradeoff problem. The proposed method calculates EMAD(extended mean of absolute differences) as local evidence. And proposed method decides weight parameter ${\lambda}$ adaptively to local texture information. The proposed method shows higher initial matching performance than conventional methods and reached optimum solution in less iteration. The matching performance is increased about 4.85 dB in PSNR.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.136-144
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• 2002

In this paper, a novel approach that makes use of both shape and color information to retrieve character images in terms of similarity distance from a large-capacity image database or from a streaming image database, in particular, character image logo or trademark. In order to combine both features of completely different characteristics bipartite matching has been employed in computing similarity distance, The proposed method turned out to bealso very effective in matching natural object or human-drawn images whose shape varies substantially.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.59-62
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• 2011

A new method is proposed for stereo vision where solution to disparity map is presented in terms of Line constraint and Reliability space -- the first constraint proposes a progressive framework for stereo matching which applies local area pixel-values from corresponding lines in the left and right image pairs. The second states that reliability space based on corresponding points records the disparity and then we are able to apply the median filter in order to reduce the noises which occur in the process. A coarse to fine result is presented after the median filtering, which improves the final result qualitatively. Experiment is evaluated by rectified stereo matching images pairs from Middlebury datasets and has proved that those two adopted strategies yield good matching quantitative results in terms of fast running speed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.554-559
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• 2009

This paper proposes a new method of weighted template matching fur machine-printed numeral recognition. The proposed weighted template matching, which emphasizes the feature of a pattern using adaptive Hamming distance on local feature areas, improves the recognition rate while template matching processes an input image as one global feature. The experiment compares confusion matrices of the template matching, error back propagation neural network classifier, and the proposed weighted template matching respectively. The result shows that the proposed method improves fairly the recognition rate of the machine-printed numerals.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.164-174
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• 2015

In a real scenario, radiometric change has frequently occurred in the stereo image acquisition process using multiple cameras with geometric characteristics or moving a single camera because it has different camera parameters and illumination change. Conventional stereo matching algorithms have a difficulty in finding correct corresponding points because it is assumed that corresponding pixels have similar color values. In this paper, we present a new method based on the local descriptor reflecting intensity, gradient and texture information. Furthermore, an adaptive weight for local descriptor based on the entropy is applied to estimate correct corresponding points under radiometric variation. The proposed method is tested on Middlebury datasets with radiometric changes, and compared with state-of-the-art algorithms. Experimental result shows that the proposed scheme outperforms other comparison algorithms around 5% less matching error on average.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.174-182
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• 2013

Most methods for fusion-based finger vein recognition were to fuse different features or matching scores from more than one trait to improve performance. To overcome the shortcomings of "the curse of dimensionality" and additional running time in feature extraction, in this paper, we propose a finger vein recognition technology based on matching score-level fusion of a single trait. To enhance the quality of finger vein image, the contrast-limited adaptive histogram equalization (CLAHE) method is utilized and it improves the local contrast of normalized image after ROI detection. Gabor features are then extracted from eight channels based on a bank of Gabor filters. Instead of using the features for the recognition directly, we analyze the contributions of Gabor feature from each channel and apply a weighted matching score-level fusion rule to get the final matching score, which will be used for the last recognition. Experimental results demonstrate the CLAHE method is effective to enhance the finger vein image quality and the proposed matching score-level fusion shows better recognition performance.