• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.84-90
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• 2014

This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.236-245
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• 2008

Robust face recognition under various illumination environments is very difficult and needs to be accomplished for successful commercialization. In this paper, we propose an efficient illumination preprocessing method for face recognition. illumination preprocessing algorithm based on anisotropic smoothing is well known to be effective among illumination normalization methods but deteriorates the intensity contrast of the original image, and incurs less sharp edges. The proposed method in this paper improves the previous anisotropic smoothing based illumination normalization method so that it increases the intensity contrast and enhances the edges while diminishing effects of illumination. Due to the result of these improvements, face images preprocessed by the proposed illumination preprocessing method becomes to have more distinctive feature vectors(Gabor feature vectors). Through experiments of face recognition using Gabor jet similarity, the effectiveness of the proposed illumination preprocessing method is verified.

• Proceedings of the KSRS Conference
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• v.2
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• pp.736-739
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• 2006

While hyperspectral data are very rich in information, their processing poses several challenges such as computational requirements, noise removal and relevant information extraction. In this paper, the application of advanced scale-space filtering to selected hyperspectral bands was investigated. In particular, a pre-processing tool, consisting of anisotropic diffusion and morphological leveling filtering, has been developed, aiming to an edge-preserving smoothing and simplification of hyperspectral data, procedures which are of fundamental importance during feature extraction and object detection. Two scale space parameters define the extent of image smoothing (anisotropic diffusion iterations) and image simplification (scale of morphological levelings). Experimental results demonstrated the effectiveness of the developed scale space filtering for the enhancement and smoothing of hyperspectral remote sensing data and their advantage against watershed over-segmentation problems and edge detection.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 2003.11a
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• pp.87-94
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• 2003

Anisotropic diffusion is a selective smoothing technique that promotes smoothing within a region instead of smoothing across boundaries. In anisotropic diffusion, the rate of smoothing is controlled by the local value of the diffusion coefficient chosen to be a function of the local image gradient magnitude. El-Fallah and Gary E. Ford represented the image as a surface and proved that setting the inhomogeneous diffusion coefficient equal to the inverse of the magnitude of the surface normal results in surface evolving speed that is proportional to the mean curvature of the image surface. This model has the advantage of having the mean curvature diffusion (MCD) render invariant magnitude, thereby preserving structure and locality. In this paper, the proposed MCD model efficiently reduces diffusion coefficient at the thin edges using the smoothness of the surface.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.835-836
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• 2008

This paper presents a new method of segmenting characters in a car licence plate which is less influenced by illumination variation. It uses an anisotropic filter to reduce the lighting noise and a histogram specification scheme to obtain the binary image. Anisotropic smoothing filter process the input images, which are acquired under different lighting conditions, so that they may have similar image quality. The enhanced performance of the proposed algorithm has been proved by the experiment.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.9 s.186
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• pp.190-199
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• 2006

A new approach which combines implicit surface scheme and recursive subdivision method is suggested in order to fill the holes with complex shapes in the polygon model. In the method, a base surface is constructed by creating smooth implicit surface from the points selected in the neighborhood of holes. In order to assure C$^1$ continuity between the newly generated surface and the original polygon model, offset points of same number as the selected points are used as the augmented constraint conditions in the calculation of implicit surface. In this paper the well-known recursive subdivision method is used in order to generate the triangular net with good quality using the hole boundary curve and generated base implicit surface. An efficient anisotropic smoothing algorithm is introduced to eliminate the unwanted noise data and improve the quality of polygon model. The effectiveness and validity of the proposed method are demonstrated by performing numerical experiments for the various types of holes and polygon model.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.275-276
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• 2007

In this paper, we propose an efficient illumination preprocessing algorithm for face recognition. One of the best known illumination preprocessing method, based on anisotropic smoothing, enhances the edge information, but instead deteriorates the contrast of the original image. Our proposed method reduces the deterioration of the contrast while enhancing the edge information, and thus the preprocessed image does not lose features like Gabor features of the original images much.. The effectiveness of the proposed illumination preprocessing method is verified through experiments of face recognition.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.439-442
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• 2007

강인한 얼굴 인식 시스템을 만들기 위해서는 안경의 제거가 중요한 요소이다. 이를 위해서는 뛰어난 성능의 안경 검출 방법이 필수적이다. 본 논문에서는 안경의 유무 판단에 관한 새로운 방법을 제안한다. 영상은 조명 부분과 반사부분의 곱으로 이루어져 있다. 얼굴의 경우 안경 고유의 반사계수와 얼굴 고유의 반사계수가 다른 점에 착안하여 anisotropic smoothing 방법을 이용하여 입력 얼굴 영상에서의 반사 부분을 얻고, 이를 이용하여 안경의 반사 부분을 얼굴의 반사부분에서 검출한 뒤 이진화한다. 이후, 이진화 된 안경 픽셀 수를 이용하여 안경의 유무를 판단한다.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.503-513
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• 2020

This paper presents a method to evaluate denoising filters based on edge locations in their denoised images. Image quality assessment has often been performed by using structural similarity (SSIM). However, SSIM does not provide clearly the geometric accuracy of features in denoised images. Thus, in this paper, a method to localize edge locations with subpixel accuracy based on adaptive weighting of gradients is used for obtaining the subpixel locations of edges in ground truth image, noisy images, and denoised images. Then, this paper proposes a method to evaluate the geometric accuracy of edge locations based on root mean squares error (RMSE) and jaggedness with reference to ground truth locations. Jaggedness is a measure proposed in this study to measure the stability of the distribution of edge locations. Tested denoising filters are anisotropic diffusion (AF), bilateral filter, guided filter, weighted guided filter, weighted mean of patches filter, and smoothing filter (SF). SF is a simple filter that smooths images by applying a Gaussian blurring to a noisy image. Experiments were performed with a set of simulated images and natural images. The experimental results show that AF and SF recovered edge locations more accurately than the other tested filters in terms of SSIM, RMSE, and jaggedness and that SF produced better results than AF in terms of jaggedness.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.179-184
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• 2009

Robust face recognition under various illumination environments is difficult to achieve. For face recognition robust to illumination changes, usually face images are normalized with respect to illumination as a preprocessing step before face recognition. The anisotropic smoothing-based illumination normalization method, known to be one of the best illumination normalization methods, cannot handle casting shadows. In this paper, we present an efficient illumination normalization method for face recognition. The proposed illumination normalization method separates the effect of illumination from eigenfaces and constructs an illumination-separated eigenface subspace. Then, an incoming face image is projected into the subspace and the obtained projected face image is rendered so that illumination effects including casting shadows are reduced as much as possible. Application to real face images shows the proposed illumination normalization method.