• Journal of the Semiconductor & Display Technology
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• v.15 no.4
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• pp.97-102
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• 2016

Image stitching is a process of combining two or more images with overlapping area to create a panorama of input images, which is considered as an active research area in computer vision, especially in the field of augmented reality with 360 degree images. Image stitching techniques can be categorized into two general approaches: direct and feature based techniques. Direct techniques compare all the pixel intensities of the images with each other, while feature based approaches aim to determine a relationship between the images through distinct features extracted from the images. This paper proposes a novel image stitching method based on feature pixels with approximated clustering filter. When the features are extracted from input images, we calculate a meaning of the minutiae, and apply an effective feature extraction algorithm to improve the processing time. With the evaluation of the results, the proposed method is corresponding accurate and effective, compared to the previous approaches.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.852-862
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• 2017

In this work a Discrete Cosine Transform (DCT)-based feature dimensionality reduced approach for fingerprint matching is proposed. The DCT is applied on a small region around the core point of fingerprint image. The performance of our proposed method is evaluated on a small database of Bologna University and two large databases of FVC2000. A dimensionally reduced feature vector is formed using only approximately 19%, 7%, and 6% DCT coefficients for the three databases from Bologna University and FVC2000, respectively. We compared the results of our proposed method with the discrete wavelet transform (DWT) method, the rotated wavelet filters (RWFs) method, and a combination of DWT+RWF and DWT+(HL+LH) subbands of RWF. The proposed method reduces the false acceptance rate from approximately 18% to 4% on DB1 (Database of Bologna University), approximately 29% to 16% on DB2 (FVC2000), and approximately 26% to 17% on DB3 (FVC2000) over the DWT based feature extraction method.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.946-950
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• 2010

Many sentiment categorization systems based on machine learning methods use morphological analyzers in order to extract linguistic features from sentences. However, the morphological analyzers do not generally perform well in a customer review domain because online customer reviews include many spacing errors and spelling errors. These low performances of the underlying systems lead to performance decreases of the sentiment categorization systems. To resolve this problem, we propose a feature extraction method based on simple longest matching of Eojeol (a Korean spacing unit) and phoneme patterns. The two kinds of patterns are automatically constructed from a large amount of POS (part-of-speech) tagged corpus. Eojeol patterns consist of Eojeols including content words such as nouns and verbs. Phoneme patterns consist of leading consonant and vowel pairs of predicate words such as verbs and adjectives because spelling errors seldom occur in leading consonants and vowels. To evaluate the proposed method, we implemented a sentiment categorization system using a SVM (Support Vector Machine) as a machine learner. In the experiment with Korean customer reviews, the sentiment categorization system using the proposed method outperformed that using a morphological analyzer as a feature extractor.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.233-242
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• 1998

In anatomical aspects, magnetic resonance image offers more accurate information than other medical images such as X ray ultrasonic and CT images. This paper introduces a method that recognizes disk diseases from spine MR images. In this method, image enhancement, image segmentation and feature extraction for sagittal plane and axial plane images are performed to separate the disk region. And then template matching method is used to extract disease region for axial plane imges. Finally, disease feature vectors are integrated and disease discrimination processes are performed. Experimental results show that the proposed method discriminates between normal and diseased disk with a considerable recognition ratio.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.39-46
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• 2013

Recently, the mobile industries grow up rapidly and their performances are improved. So the usage of mobile devices is increasing in our life. Also mobile devices equipped with a high-performance camera, so the image stitching can carry out on the mobile devices instead of the desktop. However the mobile devices have limited hardware to perform the image stitching which has a lot of computational complexity. In this paper, we have proposed improved feature descriptor extraction and matching method for efficient image stitching on mobile environment. Our method can reduce computational complexity using extension of orientation window and reduction of dimension feature descriptor when feature descriptor is generated. In addition, the computational complexity of image stitching is reduced through the classification of matching points. In our results, our method makes to improve the computational time of image stitching than the previous method. Therefore our method is suitable for the mobile environment and also that method can make natural-looking stitched image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.256-265
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• 2003

To improve reliability and practicality, a fingerprint-based biometric system needs to be robust to rotations of an input fingerprint and the processing speed should be fast. Accordingly, this paper presents a new filterbank-based fingerprint feature extraction and matching method that is robust to diverse rotations and reasonably fast. The proposed method fast extracts fingerprint features using a directional filter bank, which effectively decomposes an image into several subband outputs Since matching is also performed rapidly based on the Euclidean distance between the corresponding feature vectors, the overall processing speed is so fast. To make the system robust to rotations, the proposed method generates a set of feature vectors considering various rotations of an input fingerprint and then matches these feature vectors with the enrolled single template feature vector. Experimental results demonstrated the high speed of the proposed method in feature extraction and matching, along with a comparable verification accuracy to that of other leading techniques.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.648-656
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• 2001

In this paper, we propose a new feature-based vehicle tracking system using trajectory matching for intelligent traffic surveillance. The proposed system consists of three parts: feature extraction, feature tracking, and feature grouping using trajectory matching. For feature extraction and feature tracking, features of vehicles are selected based on the measure of cornerness and are tracked using linear Kalman filtering. We then group features from the same vehicle in the grouping step. We suggest a new grouping algorithm using the spatial information of features and trajectory matching to solve the over-grouping Problems of the feature-based tracking method. Finally, our proposed tracking system demonstrates good performance for typical traffic scenes with partial occlusion and neighboring conditions.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.85-92
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• 2019

In this paper, we propose a efficient feature point extraction method that can solve the problem of performance degradation by introducing a preprocessing process when extracting feature points by utilizing the characteristics of 360-degree realistic media. 360-degree realistic media is composed of images produced by two or more cameras and this image combining process is accomplished by extracting feature points at the edges of each image and combining them into one image if they cover the same area. In this production process, however, the stitching process where images are combined into one piece can lead to the distortion of non-seamlessness. Since the realistic media of 4K-class image has higher resolution than that of a general image, the feature point extraction and matching process takes much more time than general media cases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.929-939
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• 2010

The growth of multimedia technology forces the development of video detection for large database management and illegal copy detection. To meet this demand, this paper proposes a fast video detection method to apply to a large database. The fast video detection algorithm uses spatial features using the gray value distribution from frames and temporal features using the temporal similarity map. We form the video signature using the extracted spatial feature and temporal feature, and carry out a stepwise matching method. The performance was evaluated by accuracy, extraction and matching time, and signature size using the original videos and their modified versions such as brightness change, lossy compression, text/logo overlay. We show empirical parameter selection and the experimental results for the simple matching method using only spatial feature and compare the results with existing algorithms. According to the experimental results, the proposed method has good performance in accuracy, processing time, and signature size. Therefore, the proposed fast detection algorithm is suitable for video detection with the large database.

• Korean Journal of Remote Sensing
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• v.11 no.2
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• pp.43-57
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• 1995

This paper presents reconstruction of disparity in images. To achieve this, the algorithm was made up of two different procedures - one is extraction of boundaries for man-made structures and the other is matching of the structures. In the extraction of boundaries for man-made structures, we assume that man-made structures are composed of lines and the lines make up closed polygon. The convertional algorithms of the edges extraction may not perceive man-made structures and have problems that matching algorithms were too complex. This paper proposed sub-pixel boundaries extraction algorithm that fused split-and-merge and image improvement algorithms to overcome complexity. In matching procedure, feature-based algorithm that minimize the proposed cost function are used and the cost fuction considers movement of mid-points for left and right images to match structures. Because we could not obtain disparity of inner parts for the man-made structures, interpolation method was used. The experiment showed good results.