Image processing
Download as PPTX, PDF5 likes5,222 views
The document discusses the relationship between linear algebra and image processing, highlighting that linear algebra is essential for understanding vector spaces and transformations. It explains that digital image processing utilizes mathematical principles to manipulate visual information and has become integral in various industrial applications since the 1990s. The paper also describes different types of images, including binary, grayscale, and color images, and how they can be represented as matrices based on pixel values.
![How to processing Images ?
In the binary image f (x, y) each pixel is 0 or 1, i.e., the matrix that defines the image is a matrix with
entries 0 or 1. In this case the image is set to white (1) and black (0). A grayscale image also called
monochrome only measures the light intensity (brightness).
In this case, the value of f (x, y) for each pixel is an integer that measures the brightness of that pixel.
The grayscale images vary in a range between [0, NC-1], where NC is the number of gray levels or
intensity levels. The most common grayscale image using 8 or 16-bit per pixel, resulting in NC = 28
= 256 or NC = 216 = 65536 distinct gray levels. In a color image the value of for each pixel measures
the intensity of the color at each pixel and it is represented
by a vector with color components.
The most common color spaces are RGB (Red, Green and Blue),
HSV Hue, Saturation and Value) and CMYK (Cyan, Magenta,
Yellow and black). In this paper the color images are defined in
RGB space. The three parameters of the RGB, where represent
the intensities of the three primary colors of the model, define a three-dimensional space with three
orthogonal directions (R, G, and B). Traditionally, the implementations of the model in the RGB
graphics systems use integer values between 0 and 255 (8-bit).](https://image.slidesharecdn.com/imageprocessing-160113114420/85/Image-processing-3-320.jpg)
Image processing
- 1. Image Processing : Linear Algebra Created by- Mydul Islam
- 2. About Linear Algebra & Image Processing: Linear algebra is the branch of mathematics concerning vector spaces and linear mappings between such spaces. It includes the study of lines, planes, and subspaces, but is also concerned with properties common to all vector spaces. There are much more application in Linear Algebra. Image processing in the one of Team. Since 1990, the Digital Image Processing has been integrated into all sectors of industrial production. The image processing facilitates the manipulation of visual information and revolutionizes the way of perception, analysis, communication, storage and use of such data. The Image Processing combines high technology with theory and cognitive information. The digital image processing is a highly relevant area, extremely rich in mathematical ideas and potential activities for learning Linear Algebra in a way completely different from the standard form. A digital image is a two-dimensional discrete function, f (x, y) with (x, y) Î Z 2 , where each pair of coordinates is called a pixel. The word pixel derived from English "picture element" determines the smallest logical unit of visual information used to construct an image. Without loss of generality one image can be represented by a matrix where each element aij corresponds to the value of the pixel image position (i, j) .
- 3. How to processing Images ? In the binary image f (x, y) each pixel is 0 or 1, i.e., the matrix that defines the image is a matrix with entries 0 or 1. In this case the image is set to white (1) and black (0). A grayscale image also called monochrome only measures the light intensity (brightness). In this case, the value of f (x, y) for each pixel is an integer that measures the brightness of that pixel. The grayscale images vary in a range between [0, NC-1], where NC is the number of gray levels or intensity levels. The most common grayscale image using 8 or 16-bit per pixel, resulting in NC = 28 = 256 or NC = 216 = 65536 distinct gray levels. In a color image the value of for each pixel measures the intensity of the color at each pixel and it is represented by a vector with color components. The most common color spaces are RGB (Red, Green and Blue), HSV Hue, Saturation and Value) and CMYK (Cyan, Magenta, Yellow and black). In this paper the color images are defined in RGB space. The three parameters of the RGB, where represent the intensities of the three primary colors of the model, define a three-dimensional space with three orthogonal directions (R, G, and B). Traditionally, the implementations of the model in the RGB graphics systems use integer values between 0 and 255 (8-bit).
- 5. Matrices (Images) Addition, Subtraction or Transpose!