• 전자공학회논문지S
• /
• 제35S권1호
• /
• pp.96-104
• /
• 1998

A new gray-scale thinning algorithm using local min/max operations is proposed. In this method, erosion and dilation properties of local min/max operations are using for generating new rides and detecting ridges in gray scale image, and gray-scale skeletons are gradually obtained by accumulating the detected ridges. This method can be applicable to the unsegmented image in which object are not specified, and the obtained skeletons correspond to the ridges (high gray values) of an input image.

• 한국정보디스플레이학회:학술대회논문집
• /
• 한국정보디스플레이학회 2007년도 7th International Meeting on Information Display 제7권1호
• /
• pp.520-523
• /
• 2007

In this paper, we present a novel method to minimize flicker and gray scale errors automatically across the entire panel by using a compensation of the gray levels of image. It was realized by image simulation with feedback structure. As a result of simulation, we observed flickers from the simulated image. And we compensated the gray scale levels for original image. The compensated gray scale levels correspond to flickers which are generated by difference of pixel voltage in odd and even frame. And we simulated repetitively the compensated image by our block diagram for reduction flicker. Consequently, we confirmed flickers have been decreased more than 87%. Furthermore, our method provides visualization and valid prediction for improvement of TFT-LCD panel

• Journal of the Optical Society of Korea
• /
• 제14권4호
• /
• pp.388-394
• /
• 2010

In this paper the object recognition performance of a photon counting integral imaging system is quantitatively compared with that of a conventional gray scale imaging system. For 3D imaging of objects with a small number of photons, the elemental image set of a 3D scene is obtained using the integral imaging set up. We assume that the elemental image detection follows a Poisson distribution. Computational geometrical ray back propagation algorithm and parametric maximum likelihood estimator are applied to the photon counting elemental image set in order to reconstruct the original 3D scene. To evaluate the photon counting object recognition performance, the normalized correlation peaks between the reconstructed 3D scenes are calculated for the varied and fixed total number of photons in the reconstructed sectional image changing the total number of image channels in the integral imaging system. It is quantitatively illustrated that the recognition performance of the photon counting integral imaging system can be similar to that of a conventional gray scale imaging system as the number of image viewing channels in the photon counting integral imaging (PCII) system is increased up to the threshold point. Also, we present experiments to find the threshold point on the total number of image channels in the PCII system which can guarantee a comparable recognition performance with a gray scale imaging system. To the best of our knowledge, this is the first report on comparisons of object recognition performance with 3D photon counting & gray scale images.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1999년도 하계종합학술대회 논문집
• /
• pp.974-977
• /
• 1999

In this paper, we have proposed the hardware architecture which implements the algorithm for retaining the connectivity which prevents disconnecting in the gray-scale image thinning To perform the image thinning in a real time which find a skeleton in image, it is necessary to examine the connectivity of the skeleton in a real time. The proposed architecture finds the connectivity number in the 4-clock period. The architecture is consists of three blocks, PS(Parallel to Serial) Converter and State Generator and Ridge Checker. The PS Converter changes the 3$\times$3 gray level image to four sets of image pixels. The State Generator examine the connectivity of the central pixel by searching the data from the PS Converter. the 3$\times$3 gray level image determines. The Ridge Checker determines whether the central pixel is on the skeleton or not The proposed architecture finds the connectivity of the central pixel in a 3$\times$3 gray level image in the 4-clocks. The total circuits are verified by the design tools and operate correctly.

• 대한임상전기생리학회지
• /
• 제8권2호
• /
• pp.39-43
• /
• 2010

Purpose : The purpose of this study was to analyze skin reactions induced by ultraviolet irradiation using digital imagery. Methods : We recruited 15 women and ultraviolet irradiation was applied to their lumbar area. (The degree of inflammatory reaction was set on the basis of the third erythema dose. Image analysis was divided by Photoshop CS (8 bit RGB scale and gray scale). Then, images were processes using Image Pro Plus 4.5 program analyzing R, G, B, chromatic red value, luminance value and gray value. Results : As a result of analyzing changes in RGB scale, there were statistically significant differences in R, G, and chromatic red values. As a result of analyzing changes in gray scale, there were statistically significant differences in gray value. Analysis of changes in B and luminance values showed that there was no statistically significant difference. Conclusion : This study found that ultraviolet irradiation had influence on RGB and gray scale. These results suggest that changes to digital images on skin reaction by ultraviolet irradiation are related to erythema. In particular, these changes are related to R and gray values.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
• /
• pp.283-286
• /
• 2001

Based on a fuzzy system representation of gray scale images, we derive an edge detection algorithm whose convolution kernel is different from the known kernels such as those of Roberts', Prewitt's or Sobel's gradient. Our fuzzy system representation is an exact representation of the bicubic spline function which represents the gray scale image approximately. Hence the fuzzy system is a continuous function and it provides a natural way to define the gradient and the Laplacian operator. We show that the gradient at grid points can be evaluated by taking the convolution of the image with a 3 3 kernel. We also show that our gradient coupled with the approximate value of the continuous function generates an edge detection method which creates edge images clearer than those by other methods. A few examples of applying our methods are included.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.2485-2489
• /
• 2005

This paper introduces a new preprocessing scheme to replace original data of gray scale images with particular ordered data so that performance of lossless compression can be improved more efficiently. As a kind of preprocessing technique to maximize performance of entropy encoder, the proposed method converts the input image data into more compressible form. Before encoding a stream of the input image, the proposed preprocessor counts co-occurrence frequencies for neighboring pixel pairs. Then, it replaces each pair of adjacent gray values with particular ordered numbers based on the investigated co-occurrence frequencies. When compressing ordered image using entropy encoder, we can expect to raise compression rate more highly because of enhanced statistical feature of the input image. In this paper, we show that lossless compression rate increased by up to 37.85% when comparing results from compressing preprocessed and non-preprocessed image data using entropy encoder such as Huffman, Arithmetic encoder.

• 산업경영시스템학회지
• /
• 제43권1호
• /
• pp.16-25
• /
• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• Radiation Oncology Journal
• /
• 제18권4호
• /
• pp.345-354
• /
• 2000

목적 : 방사선조사야를 확인하는 보편적인 방법인 linacgram은 저대조도(low contrast)의 영상을 보여주고 있어 정확한 영상을 확인하는데 문제점이 있다. 따라서 본 연구는 linacgram의 대조도를 높이는 저가형 확인방법을 모색하여 영상판독과 조사야 확인에 도움이 되고자 한다. 대상 및 방법: 인체모형을 사용하여 얻어진 필름 영상을 필름전용 스캐너(Diagnostic Pro)를 통해 Optical Density Scan, Histogram Equalized, Linear Histogram Based (HB), Linear Histogram Independent, Linear Optical Density (OD) Logarithmic 및 Power, Square Root scan 방식으로 디지털화 하였다. 각기 다른 방식으로 전산 입력된 영상의 신호분포도를 얻어 signal intensity를 비교한 후 pailette fitting 방식을 통해 영상을 재구성하였고 재구성된 영상을 비교 분석하였다. 실제 치료에서 얻어진 각 인체 부위별 linacgram도 동일한 방법으로 처리한 후 화질 개선도를 알아 보았다. 결과 : 인체모형을 통해 얻어진 영상의 신호 분포영역은 Logarlthmic 방식을 선택했을 때 최소값인 3192가 나왔고 Square Root방식을 사용했을 때 최대값인 21940가 나왔다. 이러한 값들을 모니터 상에서 구현할 수 있는 256 gray scale로 바꾸어 보았을 때 7$\~$30$\%$ 만 사용되어지고 있음을 알수 있었다. Pallette fitting 방식을 통하여 모니터의 최대표현 값인 256 계조도로 Gray Scale Expansion (GSE) 함으로써 모니터가 지원하는 8bit gray scale pallette의 전범위를 사용하여 대조도가 개선되었다. 임상에서 얻어진 각 인체 부위별 무릎관절, 두경부, 폐, 골반영상에서도 GSE 처리하여 얻어진 영상이 해부학적 구조를 판독하는데 도움이 죄었다. 결론 : GSE 영상의 재구성은 대조도를 증가 시킬뿐 아니라 인체내 관심부위의 농도분포를 별도로 재구성할 수 있으므로 이중방사선조사(double exposure)에 의해 발생되는 화질의 저하를 보정함으로써 화질 개선을 가능하게 하였다. Linacgram 화질 개선은 simulation image 및 치료계획에서 발생한 DRR과 multi-layer 중첩영상 분석에 사용할 수 있으며 영상 비교 시 치료부위의 신속하고 정밀한 확인을 가능하게 하였다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2017년도 학술발표회
• /
• pp.493-493
• /
• 2017

부유사 수리실험에서 부유사의 농도를 측정하는 것은 불확실성이 매우 크다. Einstein(1950)은 유사의 pickup function 결정에서 이러한 불확실성 때문에 유사입자의 거동을 발생시키는 양력의 확률을 적용하기도 하였다. 일반적으로 부유사의 측정은 부유사 채집기를 통해 수행하지만, 시간적으로 비효율 적이며, 채집 시 채집기의 부피로 인한 난류 발생으로 채집 후 흐름 변화가 발생할 수 있다. 수리실험의 규모라면 이 문제는 더욱 부각될 수 있다. 연속적인 부유사의 농도 측정을 위해 이러한 점은 개선되어야 하는 문제이다. 본 연구에서는 유사 실험의 이러한 단점을 극복하고자 image processing 기법을 적용하였다. Image processing은 부유사의 농도가 증가할수록 탁도가 증가하는 특성을 이용하여, 부유사 농도를 추정하는 방법이다. 이 과정에서 RGB(Red-Green-Blue)로 색을 표시하는 방식에서 image를 변환하여 gray scale로 전환해야 하며, 파(wave)의 전파에 의한 image 결과의 변형은 없다고 가정하였다. Gray scale과 탁도와의 관계를 도출하기 위해 하상에 유사를 포설하고, 단파(surge)를 발생 시켰다. 실험은 길이 12.0m, 폭 0.8m, 높이 0.75m의 개수로에서 수행하였으며, 수로 상류에 sluice형 gate를 급격하게 개방하는 것으로 단파를 재현하였다. 탁도 측정을 위해 유사 채집기를 이용하였으며, 상기에서 제시한 흐름 교란문제로, 1지점에서 1개의 시간동안만 채집을 수행하였으며, image의 촬영을 병행하였다. 또한 data의 정확도를 높이기 위해 3번의 반복실험을 수행하였다. 실험결과 gray scale과 탁도와는 일정한 관계가 나타났으며, 이를 토대로 gray scale-SSC(suspended sediment concentration)와의 관계를 도출하였다. Bayesian 분석을 이용하여 image processing의 보정(확률적 보정)을 추가적으로 수행하였다. 최종적으로 실측한 값과 image processing을 통한 값을 1:1 curve를 통해 비교하였으며, 약 9%의 평균 오차가 발생하여, image processing과 bayesian 적용을 통한 부유사 농도 측정은 신뢰할 만한 결과를 도출하는 것으로 판단된다.