• International Journal of Contents
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• v.2 no.3
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• pp.22-26
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• 2006

In this paper we introduce a new circle detection algorithm for occluded on/off pupil and iris boundary extraction. The proposed algorithm employs 7-step processing to detect a center and radius of occluded on/off eye images using the property of the chords. The algorithm deals with two types of occluded pupil and iris boundary information; one is composed of circle-shaped, incomplete objects, which is called occluded on iris images and the other type consists of arc objects in which circular information has partially disappeared, called occluded off iris images. This method shows that the center and radius of iris boundary can be detected from as little as one-third of the occluded on/off iris information image. It is also shown that the proposed algorithm computed the center and radius of the incomplete iris boundary information which has partially occluded and disappeared. Experimental results on RGB images and IR images show that the proposed method has encouraging performance of boundary detection for pupil and iris segmentation. The experimental results show satisfactorily the detection of circle from incomplete circle shape information which is occluded as well as the detection of pupil/iris boundary circle of the occluded on/off image.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.484-491
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• 2015

Aerial scene is greatly increased by the introduction and supply of the image due to the growth of digital optical imaging technology and development of the UAV. It has been used as the extraction of ground properties, classification, change detection, image fusion and mapping based on the aerial image. In particular, in the image analysis and utilization of deep learning algorithm it has shown a new paradigm to overcome the limitation of the field of pattern recognition. This paper presents the possibility to apply a more wide range and various fields through the segmentation and classification of aerial scene based on the Deep learning(ConvNet). We build 4-classes image database consists of Road, Building, Yard, Forest total 3000. Each of the classes has a certain pattern, the results with feature vector map come out differently. Our system consists of feature extraction, classification and training. Feature extraction is built up of two layers based on ConvNet. And then, it is classified by using the Multilayer perceptron and Logistic regression, the algorithm as a classification process.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.82-91
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• 2013

This paper proposes a building recognition algorithm using watershed image segmentation algorithm and integrated region matching (IRM). To recognize a building, a preprocessing algorithm which is using Gaussian filter to remove noise and using canny edge extraction algorithm to extract edges is applied to input building image. First, images are segmented by watershed algorithm. Next, a region adjacency graph (RAG) based on the information of segmented regions is created. And then similar and small regions are merged. Second, a color distribution feature of each region is extracted. Finally, similar building images are obtained and ranked. The building recognition algorithm was evaluated by experiment. It is verified that the result from the proposed method is superior to color histogram matching based results.

• Smart Media Journal
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• v.9 no.3
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• pp.59-70
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• 2020

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.14 no.1
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• pp.57-66
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• 2010

In this paper, we develop the Automatic Number Plate Recognition (ANPR) System. ANPR is generally composed of the following four steps: i) The acquisition of the image; ii) The extraction of the region of the number plate; iii) The partition of the number and iv) The recognition. The second and third steps incorporate image processing technique. We propose to resolve this by using Partial Differential Equation(PDE) based segmentation method. This method is computationally efficient and robust. Results indicate that our methods are capable to recognize the plate number on difficult situations.

• The Journal of the Korea Contents Association
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• v.6 no.4
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• pp.69-80
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• 2006

Interest about baby health increased into promise of all life health on society. Prevention disease or treatment about baby health is important, but it is important that first of grasping baby state through correct diagnosis. In this paper, diagnosis method for baby ocular inspection of Oriental medicine applying image processing technology is presented. For this, extracting baby frontal face image based on skin color, five sensory organs extraction and extraction of myeongdang based on position information is proposed. Finally, usefulness of method Proposed by an experiment world prove.

• ETRI Journal
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• v.29 no.3
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• pp.353-362
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• 2007

In this paper, we propose a novel unsupervised video object extraction algorithm for individual images or image sequences with low depth of field (DOF). Low DOF is a popular photographic technique which enables the representation of the photographer's intention by giving a clear focus only on an object of interest (OOI). We first describe a fast and efficient scheme for extracting OOIs from individual low-DOF images and then extend it to deal with image sequences with low DOF in the next part. The basic algorithm unfolds into three modules. In the first module, a higher-order statistics map, which represents the spatial distribution of the high-frequency components, is obtained from an input low-DOF image. The second module locates the block-based OOI for further processing. Using the block-based OOI, the final OOI is obtained with pixel-level accuracy. We also present an algorithm to extend the extraction scheme to image sequences with low DOF. The proposed system does not require any user assistance to determine the initial OOI. This is possible due to the use of low-DOF images. The experimental results indicate that the proposed algorithm can serve as an effective tool for applications, such as 2D to 3D and photo-realistic video scene generation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.30-36
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• 2006

In this paper, a new type of written image segmentation based on relative comparison of region areas is proposed. The original image is composed of two distinctive regions; information and background. Compared with this binary original image, the observed one is the gray scale which is represented with complex regions with speckles and noise due to degradation or contamination. For applying threshold or statistical approach, there occurs the region-deformation problem in the process of binarization. At first step, the efficient iterated conditional mode (ICM) which takes the lozenge type block is used for regions formation into the binary image. Secondly the information region is estimated through selecting action and restored its primary state. Not only decision of the attachment to a region but also the calculation of the magnitude of its area are carried on at each current pixel iteratively. All region areas are sorted into a set and selected through the decision parameter which is obtained statistically. Our experiments show that these approaches are effective on ink-rubbed copy image (拓本 'Takbon') and efficient at shape restoration. Experiments on gray scale image show promising shape extraction results, comparing with the threshold-segmentation and conventional ICM method.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.27-34
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• 2019

In this paper, a new type of lip feature is proposed as distance metric in CIELUV color system. The performance of the proposed feature was tested on face image database, Helen dataset from University of Illinois. The test processes consists of three steps. The first step is feature extraction and second step is principal component analysis for the optimal projection of a feature vector. The final step is Otsu's threshold for a two-class problem. The performance of the proposed feature was better than conventional features. Performance metrics for the evaluation are OverLap and Segmentation Error. Best performance for the proposed feature was OverLap of 65% and 59 % of segmentation error. Conventional methods shows 80~95% for OverLap and 5~15% of segmentation error usually. In conventional cases, the face database is well calibrated and adjusted with the same background and illumination for the scene. The Helen dataset used in this paper is not calibrated or adjusted at all. These images are gathered from internet and therefore, there are no calibration and adjustment.

• Journal of Advanced Navigation Technology
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• v.13 no.2
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• pp.257-261
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• 2009

This paper propose a new method using Gestalt theory to extract image contour. The proposed method use Gestalt theory based on proximity, similarity and continuation for grouping objects from image segmentation. It use downward feedback and perception to materialize one visual level in image from heterogeneity visual levels in image. The experimental result show that the proposed method achieves better performance than other methods.