In this activity, basic image and advance image types have been studied. Examples of the four basic types of image such as binary, grayscale, true color and indexed images have been collected.
Figure 1.a. shows an example of a binary image, which is composed of either black or white image. The pixel values of this type of image is either ones (1’s) or zeros (0’s) or BITS. Other files such as document text, fingerprints, lineart, particle tracks, or signatures are saved as binary in order to get the information needed that may be found in line shapes.
Figure 1.a. Binary Image
The black and white image shown in Figure 1.b. is an example of a grayscale image. In this type, each pixel is given a value from 0 (black) to 255 (white), resulting to a byte-size per pixel. If the information needed are embedded in graytones, images are then saved as grayscale. Examples of these are medical or biological images and faces for face recognition.
Figure 1.b. Grayscale Image
Figure 1.c. shows an image with three channels or bands, wherein each channel is the intensity of a red. Green, and blue primary light. In a truecolor image, there are 2563 or about 1.6 million possible number of colors.
Figure 1.c. Truecolor Image. (from Personal File)
An indexed image whose colors are represented by numbers which denote the index of the colors in a color map is shown in Figure 1.d. In an indexed image, the image and its color map are the two sets of data stored.
Figure 1.d. Indexed Image
http://upload.wikimedia.org/wikipedia/en/7/7c/Adaptative_8bits_palette_sample_image.png
http://upload.wikimedia.org/wikipedia/en/7/7c/Adaptative_8bits_palette_sample_image.png
In the following, advanced types of images are shown. In Figure 2.a., a high dynamic range (HDR) image is shown. This type of image can be stored in 10- to 16-bit grayscales.
Figure 2.a. High Dynamic Range Image
Figure 2.b. shows image with more than 3 bands (Red, Green and Blue).Iimages with bands in the order of 10’s are multispectral image, while those in order of 100’s are hyperspectral images.
Figure 2.b. Hyperspectral Image.
In 3D images, the spatial 3d information can be stored. The 3D surfaces may be saved as point clouds (x,y,z), image stacks, stereopairs. Figure 2.c shows an example of 3D image.
Figure 2.c. 3D Image (from Personal File)
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The scanned image in activity 1 was converted to grayscale and to black and white image. Thresholding was done to remove the background of the graph. Figure 4 shows how the value of the thresholding affects the conversion of the image. Among the following images, Figure 4.c. shows the image with best quality. If the thresholding is above 0.90, the coversion shows a noisy image. If the thresholding below 0.50, the lines in the graph becomes thinner.
Figure 3. Scanned Image from a Journal
thresholding = 0.40
thresholding = 0.50
thresholding = 0.60
thresholding = 0.65
thresholding = 0.70
thresholding = 0.75
thresholding = 0.90
Figure 4. Conversion to Black and White image with different thresholding.
I give myself only 8/10 points for this activity since I feel that I wasn't able to discuss some details thoroughly.
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