• Journal of IKEEE
• /
• v.18 no.2
• /
• pp.234-239
• /
• 2014

This paper proposes a new automatic detection method to inspect mura defects on display film surface using morphological image processing and labeling. This automatic detection method for mura defects on display films comprises 3 phases of preprocessing with morphological image processing, Gabor filtering, and labeling. Since distorted results could be obtained with the presence of non-uniform illumination, preprocessing step reduces illumination components using morphological image processing. In Gabor filtering, mura images are created with binary coded mura components using Gabor filters. Subsequently, labeling is a final phase of finding the mura defect area using the difference between large mura defects and values in the periphery. To evaluate the accuracy of the proposed detection method, detection rate was assessed by applying the method in 200 display film samples. As a result, the detection rate was high at about 95.5%. Moreover, the study was able to acquire reliable results using the Semu index for luminance mura in image quality inspection.

• Journal of the Korean Institute of Navigation
• /
• v.19 no.3
• /
• pp.21-28
• /
• 1995

Mathematical morphology skeleton image processing makes many partial skeleton image planes from an original binary image. And the original binary image can be reconstructed without any distortion by summing the first partial skeleton image plane and each dilated partial skeleton image planes using the same structuring element. Especially compression effects of Elias coding to the morphological globally minimal skeleton(GMS) image, is better than that of PCX and Huffman coding. And then this paper proposes mathematical morphological GMS image processing which can be applied to a binary image transmitting for facimile and big size(bigger than $64{\times}64$ size) bitmap fonts storing in a memory.

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.1025-1028
• /
• 1999

After detecting the edge which is applying the morphological operators to the hybrid FCNN, we could analyze and compare. The hybrid FCNN is completely removed to the noise in the image, and worked in order to obtain the result image which is closest to the original image. Also, the morphological operator is applied to the image as the method in order to detect more good the edge than the conventional edge. FCNN which is the pipeline type is completely suitable to detecting the image processing as well as the hardware size. In this paper. we would make the structure elements of the morphological operator the variable template and the static template, and compare with the edge enhancement of two images. After being the result which is applying the variable template morphological operator and the static template morphological operator to the image, we could know that the edge images applying the variable template is superior in a edge enhancement side.

• Journal of Biosystems Engineering
• /
• v.21 no.3
• /
• pp.315-324
• /
• 1996

This study was one of a series of studies on application of machine vision and image processing to extract the geometrical features of plants and to analyze plant growth. Several algorithms were developed to measure morphological properties of plants and describing the growth development of in-situ lettuce(Lactuca sativa L.). Canopy, centroid, leaf density and fractal dimension of plant were measured from a top viewed binary image. It was capable of identifying plants by a thinning top viewed image. Overlapping the thinning side viewed image with a side viewed binary image of plant was very effective to auto-detect meaningful nodes associated with canopy components such as stem, branch, petiole and leaf. And, plant height, stem diameter, number and angle of branches, and internode length and so on were analyzed by using meaningful nodes extracted from overlapped side viewed images. Canopy, leaf density and fractal dimension showed high relation with fresh weight or growth pattern of in-situ lettuces. It was concluded that machine vision system and image processing techniques are very useful in extracting geometrical features and monitoring plant growth, although interactive methods, for some applications, were required.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.17 no.10
• /
• pp.1109-1116
• /
• 1992

Mathematical morphology, theoretical foundation for morphological filter, is very efficient for the analysis of the geometrical characteristics of signals and systems and is used as a predominant tool for smoothing the data with noise. In this study, H/W design of morphological filter is implemented to process the gray scale dilation and the erosion in the same circuit and to choose the maximum and minimum value by a selector, resulting in their education of the complexity of the circuit and an architecture for parallel processing. The structure of morphological filter consists of the structuring-element block, the image data block, the control block, the ADD block, the MIN/MAX block, etc, and is designed on an one-chip for real time operation.

• 전자공학회논문지 IE
• /
• v.45 no.2
• /
• pp.20-27
• /
• 2008

In this paper, we propose a new image processing technique that reliably detects the various defects of TFT-LCD polarizing films. The image of polarizing film is acquisited from reflected laser beam First, we apply the morphological image processing technique to remove the background noise. Next, we use the 2-dimensional LMS adaptive filtering and statistical characteristics to detect the white and black defects. Performance of the proposed method is evaluated on real TFT-LCD polarizing film samples.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.10
• /
• pp.2707-2715
• /
• 1999

Recently, there have been researches to automate processing and analysing images in the medical field using image processing technique, a fast communication network, and high performance hardware. In this paper, we propose a system to be able to analyze morphological abnormality of red-blood cells for peripheral blood image using image processing techniques. To do this, we segment red-blood cells in the blood image acquired from microscope with CCD camera and then extract UNL fourier features to classify them into 15 classes. We reduce the number of multi-variate features using PCA to construct a more efficient classifier. Our system has the best performance in recognition rate, compared with two other algorithms, LVQ3 and k-NN. So, we show that it can be applied to a pathological guided system.

• Annual Conference of KIPS
• /
• 2017.11a
• /
• pp.799-802
• /
• 2017

We attempt to combines concepts of Morphological Operator(MO) and Convolutional Neural Networks(CNN) to improve image-to-image translation. To do this, we propose an operation that approximates morphological operations. Also we propose S-Convolution, an operation that extends the operation to use multiple filters like CNN. The experiment result shows that it can learn MO with big filter using multiple S-convolution layer of small filter. To validate effectiveness of the proposed layer in image-to-image translation we experiment with GAN with S-convolution applied. The result showed that GAN with S-convolution can achieve distinct result from that of GAN with CNN.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.2
• /
• pp.91-95
• /
• 2011

In this paper, we present an improved multi-scale gradient algorithm. The proposed algorithm works the effectively handling of both step and blurred edges. In the proposed algorithm, the image sharpening operator is sharpening the edges and contours of the objects. This operation gives an opportunity to get noise reduced image and step edged image. After that, multi-scale gradient operator works on noise reduced image in order to get a gradient image. The gradient image is segmented by watershed transform. The approach of region merging is used after watershed transform. The region merging is carried out according to the region area and region homogeneity. The region number of the proposed algorithm is 36% shorter than that of the existing algorithm because the proposed algorithm produces a few irrelevant regions. Moreover, the computational time of the proposed algorithm is relatively fast in comparison with the existing one.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.5
• /
• pp.789-795
• /
• 2002

Color image is formed of combination of three color channels. Therefore its architecture is very complicated and it requires complicated image Processing for effective image segmentation. In this paper. we propose architecture of universalized Color Morphological Pyramids(CMP) which is able to give effective image segmentation. Image Pyramid architecture is a successive Image sequence whose area ratio $2^{\int}({\int}=1,2,....,N)$ after filtering and subsampling of input image. In this technique, noise removed by sequential filtering and resolution is degraded by downsampling using CMP in various color spaces. After that, new level images are constructed that apply formula using distance of neighbor vectors in close level images and segments its image. The feasibility of proposed method is examined by comparing with the results obtained from the existing method.