• Archives of Plastic Surgery
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• v.38 no.3
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• pp.273-278
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• 2011

Purpose: It has always been an aspiration for Asians to look more balanced and feminine, considering their facial features regarding relatively flat midface with marked prominences of the zygoma. Many studies have been dealt in this subject. However, the authors would like to emphasize the concept and introduce the technique of repositioning of the malar complex to a cosmetically beneficial point and stationing it on proper position by fixation on zygoma body and arch. Methods: From January 1998 to December 2007, this method was performed in 50 patients of mild to moderate prominence and malposition of the malar complex. A simplified technique of lateral orbital osteotomy and oblique osteotomy on zygomatic arch through intraoral and preauricular incision was developed. Then, liberal malar complex can be moved to a supero-posterior direction and repositioned to a more cosmetically beneficial point. To maintain the stationed position and to protect from vector affected by the attached masticating muscle to zygomatc bone, fixation was done on both zygoma body and arch. Results: We have obtained satisfactory results using this procedure without any observable complications. The advantages of this procedure are proper exposure, inconspicuous scar, safe, more natural contour, improved stability, and shorter healing time. Conclusion: The authors suggest that reduction malarplasty should be approached with underlying concept of repositioning and fixation. In mild moderate malar prominent cases, our technique will provide with maintenance of aesthetic concept, equal to the malar reduction performed under coronal approach and provide with more natural facial contour with stability even with less invasive surgical approach.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.96-107
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• 1996

A voice personality transformation algorithm using orthogonal vector space conversion is proposed in this paper. Voice personality transformation is the process of changing one person's acoustic features (source) to those of another person (target). In this paper, personality transformation is achieved by changing the LPC cepstrum coefficients, excitation spectrum and pitch contour. An orthogonal vector space conversion technique is proposed to transform the LPC cepstrum coefficients. The LPC cepstrum transformation is implemented by principle component decomposition by applying the Karhunen-Loeve transformation and minimum mean-square error coordinate transformation(MSECT). Additionally, we propose a pitch contour modification method to transform the prosodic characteristics of any speaker. To do this, reference pitch patterns for source and target speaker are firstly built up, and speaker's one. The experimental results show the effectiveness of the proposed algorithm in both subjective and objective evaluations.

• Journal of Biosystems Engineering
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• v.30 no.6 s.113
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• pp.340-346
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• 2005

Major quality features of the beef carcass in most countries including Korea are size, marbling state of the lean tissue, color of the fat and lean tissue, and thickness of back fat of the 13th rib. To evaluate the beef quality, extracting loin parts from the sectional image of the 13th beef rib is crucial and is the first step. However, because of the inhomogeneous distribution and fuzzy pattern of the fat and lean tissues on the beef cut, it is difficult to extract automatically the proper contour of the lean tissue. In this paper, a prototype mobile beef quality measurement system, which can be implemented practically at the beef processing site was developed. The developed system was composed of the hand held image acquisition unit and mobile processing unit mounted with touch-pad screen. Algorithms to extract the boundary of the lean tissue and a proper tool to evaluate the marbling status have been developed using color image processing. The boundary extraction algorithm showed successful results for the beef cuts with simple and moderate patterns of the lean tissue and fat. However, it had some difficulty in eliminating complex pattern of the extraneous tissues adhered to the lean tissue in the boundary extraction. The developed algorithms were implemented to the prototype mobile processing unit.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.57-66
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• 2011

In this paper, it is proposed for algorithm to detect human efficiently using a edge symmetry and gradient directional characteristics in realtime by the feature extraction in a single input image. Proposed algorithm is composed of three stages, preprocessing, region partition of human candidates, verification of candidate regions. Here, preprocessing stage is strong the image regardless of the intensity and brightness of surrounding environment, also detects a contour with characteristics of human as considering the shape features size and the condition of human for characteristic of human. And stage for region partition of human candidates has separated the region with edge symmetry for human and size in the detected contour, also divided 1st candidates region with applying the adaboost algorithm. Finally, the candidate region verification stage makes excellent the performance for the false detection by verifying the candidate region using feature vector of a gradient for divided local area and classifier. The results of the simulations, which is applying the proposed algorithm, the processing speed of the proposed algorithms is improved approximately 1.7 times, also, the FNR(False Negative Rate) is confirmed to be better 3% than the conventional algorithm which is a single structure algorithm.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.675-682
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• 2014

Prostate is only an organ of men. To diagnose the disease of the prostate, generally transrectal ultrasound(TRUS) images are used. Detecting its boundary is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS images using Support Vector Machine(SVM) is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing. Gabor filter bank for extraction of rotation-invariant texture features has been implemented. SVM for training step has been used to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted. A number of experiments are conducted to validate this method and results shows that the proposed algorithm extracted the prostate boundary with less than 10% relative to boundary provided manually by doctors.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3708-3728
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• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.846-855
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• 2007

Significant feature extraction in cancer cell image analysis is an important process for grading cell carcinoma. In this study, we propose a method for 3D quantitative analysis of cell nuclei based upon digital image cytometry. First, we acquired volumetric renal cell carcinoma data for each grade using confocal laser scanning microscopy and segmented cell nuclei employing color features based upon a supervised teaming scheme. For 3D visualization, we used a contour-based method for surface rendering and a 3D texture mapping method for volume rendering. We then defined and extracted the 3D morphological features of cell nuclei. To evaluate what quantitative features of 3D analysis could contribute to diagnostic information, we analyzed the statistical significance of the extracted 3D features in each grade using an analysis of variance (ANOVA). Finally, we compared the 2D with the 3D features of cell nuclei and analyzed the correlations between them. We found statistically significant correlations between nuclear grade and 3D morphological features. The proposed method has potential for use as fundamental research in developing a new nuclear grading system for accurate diagnosis and prediction of prognosis.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.25-36
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• 2008

In this paper, we present a robust hand recognition approach to sudden illumination changes. The proposed approach constructs a background model with respect to hue and hue gradient in HSI color space and extracts a foreground hand region from an input image using the background subtraction method. Eighteen features are defined for a hand pose and multi-class SVM(Support Vector Machine) approach is applied to learn and classify hand poses based on eighteen features. The proposed approach robustly extracts the contour of a hand with variations in illumination by applying the hue gradient into the background subtraction. A hand pose is defined by two Eigen values which are normalized by the size of OBB(Object-Oriented Bounding Box), and sixteen feature values which represent the number of hand contour points included in each subrange of OBB. We compared the RGB-based background subtraction, hue-based background subtraction and the proposed approach with sudden illumination changes and proved the robustness of the proposed approach. In the experiment, we built a hand pose training model from 2,700 sample hand images of six subjects which represent nine numerical numbers from one to nine. Our implementation result shows 92.6% of successful recognition rate for 1,620 hand images with various lighting condition using the training model.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.603-611
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• 2016

Generally, the doctors manually delineated the prostate boundary seeing the image by their eyes, but the manual method not only needed quite much time but also had different boundaries depending on doctors. To reduce the effort like them the automatic delineating methods are needed, but detecting the boundary is hard to do since there are lots of uncertain textures or speckle noises. There have been studied in SVM, SIFT, Gabor texture filter, snake-like contour, and average-shape model methods. Besides, there were lots of studies about 2 and 3 dimension images and CT and MRI. But no studies have been developed superior to human experts and they need additional studies. For this, this paper proposes a method that delineates the boundary predicting its texture features and its average distribution on the prostate image. As result, we got the similar boundary as the method of human experts.