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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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The KIPS Transactions:PartB
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Korea Information Processing Society
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Volume & Issues
Volume 16B, Issue 6 - Dec 2009
Volume 16B, Issue 5 - Oct 2009
Volume 16B, Issue 4 - Aug 2009
Volume 16B, Issue 3 - Jun 2009
Volume 16B, Issue 2 - Apr 2009
Volume 16B, Issue 1 - Feb 2009
Selecting the target year
Speed Sign Recognition by Using Hierarchical Application of Color Segmentation and Normalized Template Matching
Lee, Kang-Ho ; Lee, Kyu-Won ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 257~262
DOI : 10.3745/KIPSTB.2009.16-B.4.257
A method of the region extraction and recognition of a speed sign in the real road environment is proposed. The region of speed sign is extracted by using color information and then numbers are segmented in the region. We improve the recognition rate by performing an incline compensation of the speed sign for directions clockwise and counterclockwise. In image sequences of the real road environment, a robust recognition results are achieved with speed signs at normal condition as well as inclined.
An Adaptive Histogram Redistribution Algorithm Based on Area Ratio of Sub-Histogram for Contrast Enhancement
Park, Dong-Min ; Choi, Myung-Ruyl ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 263~270
DOI : 10.3745/KIPSTB.2009.16-B.4.263
Histogram Equalization (HE) is a very popular technique for enhancing the contrast of an image. HE stretches the dynamic range of an image using the cumulative distribution function of a given input image, therefore improving its contrast. However, HE has a well-known problem : when HE is applied for the contrast enhancement, there is a significant change in brightness. To resolve this problem, we propose An Adaptive Contrast Enhancement Algorithm using Subhistogram Area-Ratioed Histogram Redistribution, a new method that helps reduce excessive contrast enhancement. This proposed algorithm redistributes the dynamic range of an input image using its mean luminance value and the ratio of sub-histogram area. Experimental results show that by this redistribution, the significant change in brightness is reduced effectively and the output image is able to preserve the naturalness of an original image even if it has a poor histogram distribution.
Smoke Detection using Region Growing Method
Kim, Dong-Keun ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 271~280
DOI : 10.3745/KIPSTB.2009.16-B.4.271
In this paper, we propose a smoke detection method using region growing method in outdoor video sequences. Our proposed method is composed of three steps; the initial change area detection step, the boundary finding and expanding step, and the smoke classification step. In the first step, we use a background subtraction to detect changed areas in the current input frame against the background image. In difference images of the background subtraction, we calculate a binary image using a threshold value and apply morphology operations to the binary image to remove noises. In the second step, we find boundaries of the changed areas using labeling algorithm and expand the boundaries to their neighbors using the region growing algorithm. In the final step, ellipses of the boundaries are estimated using moments. We classify whether the boundary is smoke by using the temporal information.
Medical Image Automatic Annotation Using Multi-class SVM and Annotation Code Array
Park, Ki-Hee ; Ko, Byoung-Chul ; Nam, Jae-Yeal ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 281~288
DOI : 10.3745/KIPSTB.2009.16-B.4.281
This paper proposes a novel algorithm for the efficient classification and annotation of medical images, especially X-ray images. Since X-ray images have a bright foreground against a dark background, we need to extract the different visual descriptors compare with general nature images. In this paper, a Color Structure Descriptor (CSD) based on Harris Corner Detector is only extracted from salient points, and an Edge Histogram Descriptor (EHD) used for a textual feature of image. These two feature vectors are then applied to a multi-class Support Vector Machine (SVM), respectively, to classify images into one of 20 categories. Finally, an image has the Annotation Code Array based on the pre-defined hierarchical relations of categories and priority code order, which is given the several optimal keywords by the Annotation Code Array. Our experiments show that our annotation results have better annotation performance when compared to other method.
Experimentation and Evaluation of Energy Corrected Snake(ECS) Algorithm for Detection and Tracking the Moving Object
Yang, Seong-Sil ; Yoon, Hee-Byung ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 289~298
DOI : 10.3745/KIPSTB.2009.16-B.4.289
Active Contour Model, that is, Snake algorithm is effective for detection and tracking the objects. However, this algorithm has some drawbacks; numerous parameters must be designed(weighting factors, iteration steps, etc.), a reasonable initialization must be available and moreover suffers from numerical instability. Therefore we propose a novel Energy Corrected Snake(ECS) algorithm which improved on external energy of Snake algorithm for detection and tracking the moving object more effectively. The proposed algorithm uses the difference image, getting when the object is moving. It copies four direction images from the difference image and performs the accumulating compute to erasing image noise, so that it gets external energy steadily. Then external energy united with contour that is computed by internal energy. Consequently we can detect and track the moving object more speedily and easily. To show the effectiveness of the proposed algorithm, we experiment on 3 situations. The experimental results showed that the proposed algorithm outperformed by 6
9% of detection rate and 6
11% of tracker detection rate compared with the Snake algorithm.
A Study on Enhancing the Performance of Detecting Lip Feature Points for Facial Expression Recognition Based on AAM
Han, Eun-Jung ; Kang, Byung-Jun ; Park, Kang-Ryoung ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 299~308
DOI : 10.3745/KIPSTB.2009.16-B.4.299
AAM(Active Appearance Model) is an algorithm to extract face feature points with statistical models of shape and texture information based on PCA(Principal Component Analysis). This method is widely used for face recognition, face modeling and expression recognition. However, the detection performance of AAM algorithm is sensitive to initial value and the AAM method has the problem that detection error is increased when an input image is quite different from training data. Especially, the algorithm shows high accuracy in case of closed lips but the detection error is increased in case of opened lips and deformed lips according to the facial expression of user. To solve these problems, we propose the improved AAM algorithm using lip feature points which is extracted based on a new lip detection algorithm. In this paper, we select a searching region based on the face feature points which are detected by AAM algorithm. And lip corner points are extracted by using Canny edge detection and histogram projection method in the selected searching region. Then, lip region is accurately detected by combining color and edge information of lip in the searching region which is adjusted based on the position of the detected lip corners. Based on that, the accuracy and processing speed of lip detection are improved. Experimental results showed that the RMS(Root Mean Square) error of the proposed method was reduced as much as 4.21 pixels compared to that only using AAM algorithm.
A Personal Memex System Using Uniform Representation of the Data from Various Devices
Min, Young-Kun ; Lee, Bog-Ju ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 309~318
DOI : 10.3745/KIPSTB.2009.16-B.4.309
The researches on the system that automatically records and retrieves one`s everyday life is relatively actively worked recently. These systems, called personal memex or life log, usually entail dedicated devices such as SenseCam in MyLifeBits project. This research paid attention to the digital devices such as mobile phones, credit cards, and digital camera that people use everyday. The system enables a person to store everyday life systematically that are saved in the devices or the deviced-related web pages (e.g., phone records in the cellular phone company) and to refer this quickly later. The data collection agent in the proposed system, called MyMemex, collects the personal life log "web data" using the web services that the web sites provide and stores the web data into the server. The "file data" stored in the off-line digital devices are also loaded into the server. Each of the file data or web data is viewed as a memex event that can be described by 4W1H form. The different types of data in different services are transformed into the memex event data in 4W1H form. The memex event ontology is used in this transform. Users can sign in to the web server of this service to view their life logs in the chronological manner. Users can also search the life logs using keywords. Moreover, the life logs can be viewed as a diary or story style by converting the memex events to sentences. The related memex events are grouped to be displayed as an "episode" by a heuristic identification method. A result with high accuracy has been obtained by the experiment for the episode identification using the real life log data of one of the authors.
The Strategies for Exploring Various Regions and Recognizing Local Minimum of Particle Swarm Optimization
Lee, Young-Ah ; Kim, Tack-Hun ; Yang, Sung-Bong ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 319~326
DOI : 10.3745/KIPSTB.2009.16-B.4.319
PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle`s movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle`s movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.
ε-AMDA Algorithm and Its Application to Decision Making
Choi, Dae-Young ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 327~331
DOI : 10.3745/KIPSTB.2009.16-B.4.327
In fuzzy logic, aggregating uncertainties is generally achieved by means of operators such as t-norms and t-conorms. However, existing aggregation operators have some disadvantages as follows : First, they are situation-independent. Thus, they may not be properly applied to dynamic aggregation process. Second, they do not give an intuitional sense to decision making process. To solve these problems, we propose a new
-AMDA (Aggregation based on the fuzzy Multidimensional Decision Analysis) algorithm to reflect degrees of strength for option i (i
An Automatic Korean Word Spacing System for Devices with Low Computing Power
Song, Yeong-Kil ; Kim, Hark-Soo ;
The KIPS Transactions:PartB, volume 16B, issue 4, 2009, Pages 333~340
DOI : 10.3745/KIPSTB.2009.16-B.4.333
Most of the previous automatic word spacing systems are not suitable to use for mobile devices with relatively low computing powers because they require many system resources. We propose an automatic word spacing system that requires reasonable memory usage and simple numerical computations for mobile devices with low computing powers. The proposed system is a two step model that consists of a statistical system and a rule-based system. To reduce the memory usage, the statistical system first corrects word spacing errors by using a modified hidden Markov model based on character unigrams. Then, to increase the accuracy, the rule-based system re-corrects miscorrected word spaces by using lexical rules based on character bigrams or more. In the experiments, the proposed system showed relatively high accuracy of 94.14% in spite of small memory usage of about 1MB.