• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.53-62
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• 2006

Object recognition techniques using principal component analysis are disposed to be decreased recognition rate when lighting change of image happens. The purpose of this thesis is to propose an object recognition technique using new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. And the object recognition algorithm proposed here represents more enhanced recognition rate using improved k-Nearest Neighbor. In this thesis, we proposed an object recognition algorithm which creates object space by pre-processing and being learned image using histogram equalization and median filter. By spreading histogram of test image using histogram equalization, the effect to change of illumination is reduced. This method is stronger to change of illumination than basic PCA method and normalization, and almost removes effect of illumination, therefore almost maintains constant good recognition rate. And, it compares ingredient projected test image into object space with distance of representative value and recognizes after representative value of each object in model image is made. Each model images is used in recognition unit about some continual input image using improved k-Nearest Neighbor in this thesis because existing method have many errors about distance calculation.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.459-460
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• 2007

얼굴인식에서 조명에 의한 얼굴영상의 왜곡은 인식률에 큰 영향을 미친다. 본 논문에서는 이를 해결하기 위해 다양한 조명환경에서도 인식률의 변화가 거의 없는 방법을 제안하였다. 얼굴인식에 사용하는 영상의 전처리 방법으로 대부분 히스토그램 이퀄라이제이션(Histogram Equalization) 과정을 거친다. 그러나 이 방법은 영상 전체에 적용되는 것이기 때문에 어두운 영역에 숨어있는 얼굴특징을 부각시키는 데에 한계가 있다. 따라서 얼굴영상이 가지고 있는 성질에 따라 임계값을 정하고 이를 기준으로 밝은 부분과 어두운 부분을 분할한다. 여기에 얼굴의 특징들이 더욱 선명해지도록 화질을 향상시켰다. 이 전처리 과정을 거쳐 PCA를 사용하여 얼굴인식을 수행한 결과 평균 99.6%라는 높은 인식률을 얻을 수 있었다.

• Journal of radiological science and technology
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• v.40 no.2
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• pp.197-203
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• 2017

The purpose of this study was to measure signal to noise ratio (SNR) according to change of equalization from region of interest (ROI) of heart shadow in chest image. We examined images of chest image of 87 patients in a University-affiliated hospital, Seoul, Korea. Chest images of each patient were calculated by using ImageJ. We have analysis socio-demographical variables, SNR according to images, 95% confidence according to SNR of difference in a mean of SNR. Differences of SNR among change of equalization were tested by SPSS Statistics21 ANOVA test for there was statistical significance 95%(p < 0.05). In SNR results, with the quality of distributions in the order of original chest image, original chest image heart shadow and equalization chest image, equalization chest image heart shadow(p < 0.001). In conclusion, this study would be that quantitative evaluation of heart shadow on chest image can be used as an adjunct to the histogram equalization chest image.