• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.8
• /
• pp.2783-2795
• /
• 2014

Orthogonal frequency division multiplexing with offset quadrature amplitude modulation (OFDM-OQAM) technique has drawn significant interests in recent years. However, most of the existing OFDM peak-to-average ratio (PAPR) reduction schemes cannot be used in the OFDM-OQAM system directly. In this paper, a modified scheme called overlapped segmental clipping (OS-clipping) is proposed to deal with the high PAPR problem specifically in the OFDM-OQAM system. For the proposed OS-clipping scheme, the input signals are divided into a number of overlapped segments and then the clipping operation is processed on each segment. Simulation results show that the modified scheme used in the OFDM-OQAM system can provide better performance than conventional clipping scheme directly used in the OFDM-OQAM system, and even outperforms conventional clipping scheme applied in the OFDM system.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2006.03a
• /
• pp.78-85
• /
• 2006

The Ulsan Fault System continues from north of Gyeongju to Ulsan city, trending NNW-SSE and is about 50 km. Many Quaternary faults have been reported and investigated with outcrop observation. Lineaments have been extracted with aerial photograph interpretation and classified by their ranks. Trench excavations on the lineaments along Ulsan Fault System have been carried out to clarify the neotectonic movements and fault parameters such as the latest movement age, fault displacement, slip rate and recurrence interval. We have compiled data from previous studies on criteria of segment type such as lineament rank, seismicity, slip rate, and the latest fault movement. Based on these data, we tried to devide the Ulsan Fault System into several segments. The results of segmentation with each types of segment along the Ulsan Fault System did not show singular division point but overlapped or different length and location.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.3
• /
• pp.79-87
• /
• 1996

A new method for recognizing and locating partially occluded or overlapped two-dimensional objects regardless of their size, translation, and rotation, is presented. Dominant points approximating occuluding contoures of objects are generated by finding local maxima of smoothed k-cosine function, and then used to guide the contour segment matching procedure. Primitives between the dominant points are produced by projecting the local contours onto the line between the dominant points. Robust classification of primitives. Which is crucial for reliable partial shape matching, is performed using adaptive resonance theory (ART2). The matched primitives having similar scale factors and rotation angles are detected in the hough space to identify the presence of the given model in the object scene. Finally the translation vector is estimated by minimizing the mean squred error of the matched contur segment pairs. This model-based matching algorithm may be used in diveerse factory automation applications since models can be added or changed simply by training ART2 adaptively without modifying the matching algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.3
• /
• pp.74-83
• /
• 1997

In this paper, a new stereo matching algorithm is proposed which uses th econstrainted optimization technique and the matching measures between the segments extracted from zero-crossing edges. The initial matching measures and average disparities are calculated by the features of segments on the searching window of the left and right images. The matching measure is calculated by applying an exponential function using the differences of slope, overlapped length and intensity. The coherency constraint is that neighbouring image points corresponding to the same object should have nearly the same disparities. The matching measures are iteratively updated by applying the coherency constraint. Simulation results on various images show that the proposed algorithm more acculately extracts the segment disparity.

• The KIPS Transactions:PartB
• /
• v.16B no.2
• /
• pp.131-140
• /
• 2009

This paper presents a new algorithm to the segmentation of the FISH images. First, for segmentation of the cell nuclei from background, a threshold is estimated by using the gaussian mixture model and maximizing the likelihood function of gray value of cell images. After nuclei segmentation, overlapped nuclei and isolated nuclei need to be classified for exact nuclei analysis. For nuclei classification, this paper extracted the morphological features of the nuclei such as compactness, smoothness and moments from training data. Three probability density functions are generated from these features and they are applied to the proposed Bayesian networks as evidences. After nuclei classification, segmenting of overlapped nuclei into isolated nuclei is necessary. This paper first performs intensity gradient transform and watershed algorithm to segment overlapped nuclei. Then proposed stepwise merging strategy is applied to merge several fragments in major nucleus. The experimental results using FISH images show that our system can indeed improve segmentation performance compared to previous researches, since we performed nuclei classification before separating overlapped nuclei.

• Journal of Internet Computing and Services
• /
• v.25 no.2
• /
• pp.57-67
• /
• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.25 no.9
• /
• pp.1091-1101
• /
• 1988

In this paper, a method to segment each character and extract figure from Korean documents is proposed. At first, each character string is extracted by means of iterative horizontal propagation, shrink algorithm and run-length algorithm. Individual character region is extracted by iterative horizontal and vertical manipulation. Next, characters of right pitch are searched. Each character is segmented by the position information. Overlapped character is segmented on the ground of the width of already extracted character. The rest are extracted as special characters of half pitch. Using 9 data input in the form of 840 X 600 from Korean monthly magazine, experiment was simulated. Extraction rate of character is 100%, and that of individual character is 98%. Judging from these results, efficiency on extracting character region and segmenting individual character is proved.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.27 no.2
• /
• pp.146-159
• /
• 2023

This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering algorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.

• The KIPS Transactions:PartB
• /
• v.11B no.7 s.96
• /
• pp.769-778
• /
• 2004

Object segmentation is a challenging Problem when the background is cluttered and the objects are overlapped one another. Recent develop-ment using snake algorithms proposed to segment objects from a 2-D Image presents a higher possibilityfor getting better contours. However, the performance of those snake algorithms degrades rapidly when the background is cluttered and objects are overlapped one another, Moreover, the initial snake point placement is another difficulty to be resolved. Here, we propose a novel snake algorithm for object segmentation using disparity information taken from a set of stereo images. By applying our newly designed snake energy function defined in the disparity space, our algorithmeffectively circumvents the limitations found in the previous methods. The performance of the proposed algorithm has been verified by computer simulation using various stereo image sets. The experiment results have exhibited a better performance over the well-known snake algorithm in terms of segmentation accuracy.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.4
• /
• pp.444-450
• /
• 1999

This paper deals with several methods: the clustering method that uses k-means algorithm to abstract the area of characters on the image document and the distance function that suits for the HIS coordinate system to cluster the image. For the prepossessing step to recognize this, or the method of characters segmentate, the algorithm to abstract a discrete character is also proposed, using the linking picture element. This algorithm provides the feature that separates any character such as the touching or overlapped character. The methods of projecting and tracking the edge have so far been used to segment them. However, with the new method proposed here, the picture element extracts a discrete character with only one-time projection after abstracting the character string. it is possible to pull out it. dividing the area into the character and the rest (non-character). This has great significance in terms of processing color documents, not the simple binary image, and already received verification that it is more advanced than the previous document processing system.