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.

A multi-disciplinary approach is used to analyze current trends in environmental and technology color research to provide better understanding of how color plays a crucial role in engineering, medical science, law, design, arts, marketing, and business. The study shows that understanding the complexities of color perception can contribute to better product and building design and the use of color therapy in medicine.

멀티미디어의 개발에 따라 가상 영상에 대한 실사적인 색 표현이 가능해졌다. 이로 인해 영상에서 물체를 표현하기 위해 빛과 표면의 특성을 기반으로 색을 표현하는 렌더링 기법들이 개발되고 있다. 빛과 표면의 물리적인 상호작용을 모델링하는 과정에서 각각에 대한 분광 특성에 대한 정보가 정확하다면 결과영상은 정확하게 표현할 수 있다. 그러나 기존의 렌더링 응용은 단순한 RGB 기반 3차원을 이용하여 벡터의 성분별 합과 곱으로 빛과 표면의 분광 특성을 표현하고 상호작용을 모델링하기 때문에 실제적인빛-표면 상호 작용과 수학적인 연산이 일치하지 않는다. 이를 해결하기 위해 본 논문에서는 새로운 특성함수 기반의 기저함수를 이용하여 단순한 연산으로 빛-표면 상호작용을 계산할 수 있는 표면 반사율 모 델 기법을 제안한다.

We have started multi-color imaging program of nearby galaxies since last year and present preliminary result here. We selected 12 nearby galaxies classfied from E to Sab type and observed in BVRIJHK' bands. Photomtric parameters such as isophotal diameter, axial ratio, isophotal magnitude were measured and observed colors were compared with theoritical model. We find a standard evolution model agrees well with observed results.