TAILIEUCHUNG - Lecture Digital image processing - Lecture 4: Pixels

After studying this chapter you will be able to understand: By representing the image in matrix with different points called “pixels”, there are some important relationships exist among those pixels, neighborhood of the pixel and there types, connectivity in an image, connected component labeling algorithm, adjacency and different types of adjacency relationships, distance measures method, different image operations. | Digital Image Processing CCS331 Relationships of Pixel 1 Summery of previous lecture Sampling: instead of taking the possible intensity values at every possible location in the image; we take the pixels values or intensity values at some discrete locations in the 2 dimensional space Quantization: The sample values are quantized to one of the discrete levels and depending upon the number of levels that we choose, the number of bits needed to represent each and every sample value. 2 Summery of previous lecture Normally 8 bits for quantization of every sample value is used. if it is a black and white image; then the set of values for black and white will be 16 if it is a color image, then there are 3 different planes - the red plane, green plane and blue plane. For each of these different planes, every point is represented by 8 bits. for a color image, normally we have 24 bits for every sample which we call it pixel; an image is represented in the form of a matrix or a 2 dimensional matrix which is a digital image and each of the matrix elements are is now called a pixel. 3 Todays lecture By representing the image in matrix with different points called “pixels”, There are some important relationships exist among those pixels. Neighborhood of the pixel and there types. Connectivity in an image. Connected component labeling algorithm Adjacency and different types of adjacency relationships. Distance measures method Different image operations 4 Pixel neighborhood An image is represented as a function f(x , y) Each element f(x , y) at location of x and y in 2D matrix is called pixel. So the location is sampling and the values are quantization as it’s a matrix so there will be some pixels around one x, y location 5 Pixel neighborhood Consider a pixel at location a pixel p at location x y matrix will have a number of rows and columns row which is just before x that is row x minus 1, and the row just after the row x is, row x plus 1. Similarly, there will be a column .

• Đồ họa - Thiết kế - Flash
• Digital image processing
• Lecture Digital image processing
• Digital images
• Digital computers
• Image processing
• Pixel neighborhood
• Image segmentation
• Local processing
• Global processing
• Morphological image processing
• Autonomous machine applications
• Region growing
• Image digitization
• Theory of sampling
• Image transformation
• Image enhancement
• Image restoration
• Image transform
• Image compression
• Dark image