![]() I just want to specify the color of each pixel by assigning the RGB values one by one. Foveon X3 direct image sensor is the most advanced color image sensor. If it helps the index of each image is equal. In order to construct an RGB picture, we calculate Green and Blue values for each Red pixel, Blue and Red values for each Green pixel and Red and Green values for each Blue pixel through interpolation or color demosaicing algorithm (For more details See here). FINALLY my question is how do I take these values for Red Green and Blue, and make a third picture that uses these values as the Red Green and Blue values at each pixel? For instance if my matrix is Combination = I want the first pixel of my image to have an RGB value of RGB: 0.54,0.23. ![]() On the other hand, an RGB image consists of third dimension in addition to the 2 dimensions in gray image, which differs the RGB image and gray image. Thereby, you'll get a 2-D image i.e two spatial coordinates only. Then I will combine these matrices into one matrix with three columns where the first column is Red and the second is Green and the third column is Blue (Combination = NOTE: the second column is zero because there are no Green values). A Gray image is a combination of all three planes (R,G,B) in a ratio stated as below. Next I determine the RGB values for each image, and I create two matrices one with variations in the Red and the other with variations in Blue (Picture1 = (Variations for Red) and Picture2 = (Variations for Blue)). There is no Green value for either picture. should ocr model keeping those images as the color image (rgb) learning or converting them into the gray image. However, when ocr recognizes the text images, which are low-resolution images and unclear text texture. I change the colormap for each image so that one image goes from Black to Blue, and the other image goes from Black to Red. In general, using the gray image for ocr is good. The more complex the colour channels are, the more complex the dataset is and the longer it will take to train the model. Most colour (RGB) images are composed of three colour channels, while grayscale images have just one channel. ![]() I'm given two different grayscale images that I convert to RGB images. Colour channels reflect the dimensionality of your image arrays. ![]()
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