• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.179-182
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• 2001

In this paper. a key frame extraction algorithm for browsing and searching the summary of a video is proposed. Toward this end, important frames representing a shot are selected according to the correlations among frames. by using the Fourier descriptor which is useful for the shot boundary detection. To quantitatively evaluate the importance of selected frames. a new measure based on correlation coefficients of frames is proposed. If there are several frames with a same importance. another criteria is introduced to break the tie. by computing the partial moment of subframes including each candidate key frame so that the distortion rate is minimized Since a key frame extraction algorithm can be evaluated subjectively. the performance of the proposed algorithm has been verified by a statistical test. Experiments show that more than 20% improvement has been obtained by the proposed algorithm compared to existing methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.193-198
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• 2001

최근 대용량 디지털 비디오의 효율적인 이용 및 관리를 위한 데이터 베이스 구축이 절실히 요구되고 있다. 이를 위해서 , 디지털 비디오의 내용 기반 요약이 필요하며, 선행 기술로서 비디오의 샷 경계 검출이 이루어져야 한다. 기존의 샷 경계 검출 방법들은 압축 파일로 저장된 비디오 데이터에 대해 샷 경계 검출을 수행한다. 이러한 기존 방법과 달리, 본 논문에서는 디지털 비디오 카메라로부터 비디오 데이터를 얻어 저장하면서, 실시간으로 샷 경계 검출을 수행하고자 한다. 그리고, 실시간 샷 경계 검출에 있어서 본 논문은 MPEG-7의 Homogeneous Texture와 Edge Histogram 기술자를 적용하여 추출된 각 비디오 프레임의 질감과 에지 정보를 이용한다 이 방법은 비디오의 샷 경계 검출에 있어서 시간적으로 기존 방법들보다 효율적이고, 내용 기반 검색에 유용하다.

• Journal of Biomedical Engineering Research
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• v.33 no.4
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• pp.169-176
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• 2012

Tumor cell morphology is closely related to its migratory behaviors. An active tumor cell has a highly irregular shape, whereas a spherical cell is inactive. Thus, quantitative analysis of cell features is crucial to determine tumor malignancy or to test the efficacy of anticancer treatment. We use 3D time-lapse phase-contrast microscopy to analyze single cell morphology because it enables to observe long-term activity of living cells without photobleaching and phototoxicity, which is common in other fluorescence-labeled microscopy. Despite this advantage, there are image-level drawbacks to phase-contrast microscopy, such as local light effect and contrast interference ring. Therefore, we first corrected for non-uniform illumination artifacts and then we use intensity distribution information to detect cell boundary. In phase contrast microscopy image, cell is normally appeared as dark region surrounded by bright halo ring. Due to halo artifact is minimal around the cell body and has non-symmetric diffusion pattern, we calculate cross sectional plane which intersects center of each cell and orthogonal to first principal axis. Then, we extract dark cell region by analyzing intensity profile curve considering local bright peak as halo area. Finally, we calculated the Fourier descriptor that morphological characteristics of cell to classify tumor cells into active and inactive groups. We validated classification accuracy by comparing our findings with manually obtained results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.1007-1016
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• 2005

This paper presents a feature extraction method for shape based retrieval of 3D models. Since the feature descriptor of 3D model should be invariant to translation, rotation and scaling, it is necessary to preprocess the 3D models to represent them in a canonical coordinate system. We use the PCA(Principal Component Analysis) method to preprocess the 3D models. Also, we apply that to make a MBR(Minimum Boundary Rectangle) and a circumsphere. The proposed algorithm is as follows. We generate a circumsphere around 3D models, where radius equals 1(r=1) and locate each model in the center of the circumsphere. We produce the concentric spheres with a different radius($r_i=i/n,\;i=1,2,{\ldots},n$). After looking for meshes intersected with the concentric spheres, we compute the curvature of the meshes. We use these curvatures as the model descriptor. Experimental results numerically show the performance improvement of proposed algorithm from min. 0.1 to max. 0.6 in comparison with conventional methods by ANMRR, although our method uses .relatively small bins. This paper uses $R{^*}-tree$ as the indexing.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.99-105
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• 2011

In this paper, we proposed a method for estimating an accurate gamut based on segment maxima method. According to the number of segments in the segment maxima, a local concavity is generated in the vicinity of lightness axis or a gamut is reduced in high chroma region. It induces artifacts or deterioration of the image quality. To remove these artifacts, the number of segment is determined according to the number of samples. and a local concavity is modified by extending a detected concave point to the line connecting two adjacent boundary points. Experimental results show that the contours in a uniform color region and speckle artifacts from the conventional segment maxima algorithm are removed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.170-178
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• 2008

A high performance object recognition algorithm using Fourier description of the 3D information of the objects is proposed. Object boundaries contain sufficient information for recognition in most of objects. However, it is not well utilized as the key solution of the object recognition since obtaining the accurate boundary information is not easy. Also, object boundaries vary highly depending on the size or orientation of object. The proposed object recognition algorithm is based on 1) the accurate object boundaries extracted from the 3D shape which is obtained by the laser scan device, and 2) reduction of the required database using the size and rotational invariant feature of the Fourier Descriptor. Such Fourier information is compared with the database and the recognition is done by selecting the best matching object. The experiments have been done on the rich database of MPEG 7 Part B.

• Journal of the Korean GEO-environmental Society
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• v.17 no.3
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• pp.21-26
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• 2016

Soil particle shape analysis was conducted with sands from Jumujun, Korea and Ras Al Khair, Saudi Arabia. Two hundred times enlarged digital images of the particles of those two sands were obtained with an optical microscope. The resolution of the digital images was $640{\times}320$. By conducting digital image processing, the coordinates of the soil particle boundary were extracted. After mapping those coordinates to the complex space, Fourier transformation was performed and the coefficients of each trigonometry term were computed. The coefficients reflect the shape characteristics of the sand grains and are invariant to translation. To evaluate the shape itself excluding the size of the soil particle, the coefficient was normalized by the equivalent radius of soil particle; this is called Fourier descriptor. After analyzing the Fourier descriptors, it was found that the major characteristics of Jumunjin and Ras Al Khair sands were elongation and asymmetry. Furthermore, it was found that the particle shapes reflect the self-similar, fractal nature of the textural features. The effects of resolution on soil particle shape analysis was also studied. Regarding this, it was found that the significant Fourier descriptors were not significantly affected by the image resolution investigated in this study, but the descriptors associated with textural features were affected.

• Journal of Internet Computing and Services
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• v.3 no.6
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• pp.13-24
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• 2002

This paper presents an automatic approach to detect face and facial feature from face images based on the color information and deformable model. Skin color information has been widely used for face and facial feature diction since it is effective for object recognition and has less computational burden, In this paper, we propose how to compensates varying light condition and utilize the transformed YCbCr color model to detect candidates region of face and facial feature from color images, Moreover, the detected face facial feature areas are subsequently assigned to a initial condition of active contour model to extract optimal boundaries of face and facial feature by resolving initial boundary problem when the active contour is used, The experimental results show the efficiency of the proposed method, The face and facial feature information will be used for face recognition and facial feature descriptor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.990-999
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• 2007

In this paper, we propose the region-based image retrieval method using JSEG which is a method for unsupervised segmentation of color-texture regions. JSEG is an algorithm that discretizes an image by color classification, makes the J-image by applying a region to window mask, and then segments the image by using a region growing and merging. The segmented image from JSEG is given to a user as the query image, and a user can select a few segmented regions as the query region. After finding the MBR of regions selected by user query and generating the multiple window masks based on the center point of MBR, we extract the feature vectors from selected regions. We use the accumulated histogram as the global descriptor for performance comparison of extracted feature vectors in each method. Our approach fast and accurately supplies the relevant images for the given query, as the feature vectors extracted from specific regions and global regions are simultaneously applied to image retrieval. Experimental evidence suggests that our algorithm outperforms the recent image-based methods for image indexing and retrieval.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.117-128
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• 2011

In this paper, we present a novel approach for contents-based medication image retrieval from a medication image database using the shape classification and color information of the medication. One major problem in developing a contents-based drug image retrieval system is there are too many similar images in shape and color and it makes difficult to identify any specific medication by a single feature of the drug image. To resolve such difficulty in identifying images, we propose a hybrid approach to retrieve a medication image based on shape and color features of the medication. In the first phase of the proposed method we classify the medications by shape of the images. In the second phase, we identify them by color matching between a query image and preclassified images in the first phase. For the shape classification, the shape signature, which is unique shape descriptor of the medication, is extracted from the boundary of the medication. Once images are classified by the shape signature, Hue and Saturation(HS) color model is used to retrieve a most similarly matched medication image from the classified database images with the query image. The proposed system is designed and developed especially for specific population- seniors to browse medication images by using visual information of the medication in a feasible fashion. The experiment shows the proposed automatic image retrieval system is reliable and convenient to identify the medication images.