• The Journal of the Korea Contents Association
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• v.6 no.9
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• pp.19-27
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• 2006

Mean shift procedure is applied for the data points in the joint spatial-range domain and achieves a high quality. However, a color image is segmented differently according to the inputted spatial parameter or range parameter and the demerit is that the image is broken into many small regions in case of the small parameter. In this paper, to improve this demerit, we propose the method that groups similar regions using region merging method for over-segmented images. The proposed method converts a over-segmented image in RGB color space into in HSI color space and merges similar regions by hue information. Here, to preserve edge information, the region merge constraints are used to decide whether regions are merged or not. After then, we merge the regions in RGB color space for non-processed regions in HSI color space. Experimental results show the superiority in region's segmentation results.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.415-420
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• 1989

This thesis deals with range finding using one camera and laser pointer. Range finding will be used further recognition of the image, that is, range image which allows further segmentation of the scene. In the first step, camera modeling is performed by camera calibration which executes least square fit. Least square fit uses the method of sigular value decomposition. And perspective transform of camera is obtained. Lastly range finding is performed by triangulation principle. The result of this algorithm are displayed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1121-1139
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• 2015

In texture-plus-depth format, depth map compression is an important task. Different from normal texture images, depth maps have less texture information, while contain many homogeneous regions separated by sharp edges. This feature will be employed to form an efficient depth map coding scheme in this paper. Firstly, the histogram of the depth map will be analyzed to find an appropriate threshold that segments the depth map into the foreground and background regions, allowing the edge between these two kinds of regions to be obtained. Secondly, the two regions will be encoded through rate distortion optimization with a shape adaptive wavelet transform, while the edges are lossless encoded with JBIG2. Finally, a depth-updating algorithm based on the threshold and the depth range is applied to enhance the quality of the decoded depth maps. Experimental results demonstrate the effective performance on both the depth map quality and the synthesized view quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.967-975
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• 2010

Image segmentation is one of the first steps leading to image analysis and interpretation, which is to distinguish objects from background. However, the active contour model can't exactly extract the desired objects because the phase only is 2. In this paper, we propose the method which can find the desired contours by composing the initial curve near the objects which have intensities of special range. The initial curve is calculated by the histogram equalization, the Gaussian equalization, and the threshold. The proposed method reduce the calculation speed and exactly detect the wanted objects because the initial curve set near by interested area. The proposed method also shows more efficient than the active contour model in the results applied the CT and MR images.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.103-111
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• 2016

Traditionally, in the hospital the doctors saw the TRUS images by their eyes and manually segmented the boundary between the prostate and nonprostate. But the manually segmenting process not only needed too much time but also had different boundaries according to the doctor. To cope the problems, some automatic segmentations of the prostate have been studied to generate the constant segmentation results and get the belief from patients. Besides, on detecting the boundary, the ones in the middle of all images are easy to find the boundary but the base and apex of the images are hard to do it since there are lots of uncertain boundary. Accurate detection of prostate boundaries is a challenging and difficult task due to weak prostate boundaries, speckle noises and the short range of gray levels. In this paper, we propose the method that extracts a prostate boundary using features of its directions on prostate image. As a result of our experiments, it shows that the boundary never falls short of the existing methods or human expert's segmentation. And also, its searching speed is too fast because the method searches a smaller area that other methods.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.6
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• pp.601-611
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• 2003

The main objective of this study was to investigate the relationship between benefits segmentation and knitwear purchasing behavior of college female students. A questionnaire was developed to measure benefits segmentation, knit wear purchasing behavior. The questionnaire was administered to 505 college female students in Chonbuk and Chonnam. The data was analyzed using percentage, frequency, mean, factor analysis, cluster analysis and ANOVA, Duncan multiple range test. The results of the study were as follows: The college female students were classified into four subdivisions by the cluster analysis: recreation pursuit group, fashion pursuit group, individuality pursuit group, self-improvement pursuit group on the basis of pursuit benefit factors. The knitwear purchasing motives of consumers were significantly different according to pursuit benefit subdivision. The individuality pursuit group was the highest user of mass media fashion information sources. The fashion pursuit group used purchasing experience and advice of others less than other groups. Consumers' evaluation criteria of knitwear products were significantly different depending on pursuit benefit subdivision in design and coordination, goods traits, practicality, individual expression, and external criterion. The other groups used purchasing experience and advice of others more than the fashion pursuit group.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4395-4412
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• 2020

The multisource image fusion has become an active topic in the last few years owing to its higher segmentation rate. To enhance the accuracy of multimodal pig-body feature segmentation, a multisource image fusion method was employed. Nevertheless, the conventional multisource image fusion methods can not extract superior contrast and abundant details of fused image. To superior segment shape feature and detect temperature feature, a new multisource image fusion method was presented and entitled as NSST-GF-IPCNN. Firstly, the multisource images were resolved into a range of multiscale and multidirectional subbands by Nonsubsampled Shearlet Transform (NSST). Then, to superior describe fine-scale texture and edge information, even-symmetrical Gabor filter and Improved Pulse Coupled Neural Network (IPCNN) were used to fuse low and high-frequency subbands, respectively. Next, the fused coefficients were reconstructed into a fusion image using inverse NSST. Finally, the shape feature was extracted using automatic threshold algorithm and optimized using morphological operation. Nevertheless, the highest temperature of pig-body was gained in view of segmentation results. Experiments revealed that the presented fusion algorithm was able to realize 2.102-4.066% higher average accuracy rate than the traditional algorithms and also enhanced efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2002.11a
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• pp.324-327
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• 2002

For the characterization of arbitrary shaped printed circuit board, lossy transmission line grid model based on SPICE netlist and analytical plane model based on the segmentation method are proposed in this paper. Two methods are compared with an arbitrary shaped power/ground plane. Furthermore, design considerations for the complete power distribution network structure are discussed to ensure the maximum value of the PDN impedance is low enough across the desired frequency range and to guide decoupling capacitor selection.