Basics of pixel neighbor.
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Neighbor of pixels prepared by students of institute of management sciences peshawar and course instructor is Dr imran Mughal
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1. There are different relationships between pixels including neighborhood, adjacency, connectivity, and paths.
2. Neighborhood refers to the pixels surrounding a given pixel. Adjacency means two pixels are connected based on a similarity criterion.
3. Connectivity determines if pixels are adjacent in a certain sense like 4-connected or 8-connected based on their neighborhoods.
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1) Unconstrained and constrained restoration, with unconstrained having no knowledge of noise and constrained using knowledge of noise.
2) Inverse filtering which is a direct method that minimizes error between degraded and original images using matrix operations, but can be unstable due to noise or near-zero filter values.
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This document discusses pixel relationships and neighborhood concepts in digital images. It defines a pixel and pixel connectivity. There are different types of pixel neighborhoods, including 4-neighbor, 8-neighbor, and diagonal neighbors. Connected components are sets of pixels that are connected based on pixel adjacency. Algorithms can label connected components and identify distinct image regions. Various distance measures quantify how close pixels are, such as Euclidean, Manhattan, and chessboard distances. Arithmetic and logical operators can combine pixel values from different images. Neighborhood operations apply functions to pixels based on their values and those of nearby pixels.
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This document discusses different types of error free compression techniques including variable-length coding, Huffman coding, and arithmetic coding. It then describes lossy compression techniques such as lossy predictive coding, delta modulation, and transform coding. Lossy compression allows for increased compression by compromising accuracy through the use of quantization. Transform coding performs four steps: decomposition, transformation, quantization, and coding to compress image data.
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The document discusses various techniques for image compression including:
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- Difference coding which encodes the differences between pixel values.
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Reversible compression allows exact reconstruction while lossy compression sacrifices some information for higher compression but images remain visually similar. Combining techniques can achieve even higher compression ratios.
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- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
- Spatial operations include single-pixel, neighborhood, and geometric transformations of pixel locations and intensities.
- Images can be represented as vectors and transformed using matrix operations.
- Common transforms like Fourier use separable, symmetric kernels to decompose images into frequency domains.
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Prepared by
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- Difference coding which encodes the differences between pixel values.
- Block truncation coding which divides images into blocks and assigns codewords.
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Reversible compression allows exact reconstruction while lossy compression sacrifices some information for higher compression but images remain visually similar. Combining techniques can achieve even higher compression ratios.
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This document discusses various mathematical tools used in digital image processing (DIP), including array versus matrix operations, linear versus nonlinear operations, arithmetic operations, set and logical operations, spatial operations, vector and matrix operations, and image transforms. Key points include:
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- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
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The document discusses basic relationships between pixels in digital images. It defines that a pixel has 4 horizontal and vertical neighbors, called 4-neighbors. It also has 4 diagonal neighbors, and together with the 4-neighbors they form the 8-neighbors of a pixel. Adjacency between pixels is defined based on 4, 8 or m-connectivity depending on pixel intensity values. Connectivity and paths between pixels are also described. Regions in an image are defined as connected subsets of pixels, and region boundaries are pixels adjacent to the complement of the region.
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its very useful for students.
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The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
Discrete cosine transform
Transform coding is used in image and video processing to exploit correlations between neighboring pixels. The discrete cosine transform (DCT) is commonly used as it provides good energy compaction and de-correlation. DCT represents data as a sum of variable frequency cosine waves in the frequency domain. It has properties like separability and orthogonality that make it efficient for computation. DCT exhibits excellent energy compaction and removal of redundancy between pixels, making it useful for image and video compression.
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This document provides information about an image processing course. The key details are:
- The course number is CSC 447 and is taught over 3 lecture hours and 2 lab hours. It is worth 65 marks and has a 3 hour exam.
- The course covers topics like image processing applications, enhancement techniques, restoration, segmentation, and scene analysis. It also covers specific techniques like using neural networks and parallel algorithms for image processing.
- The textbook for the course is "Digital Image Processing Using Matlab" by Rafael Gonzalez and Richard Woods. There are 11 lab assignments focused on topics like image display, filtering, transforms, and color conversion using Matlab.
- The course is taught by
Digital Image Fundamentals - II
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
Digital_Image_Processing with examples.pdf
This provides detailed explanation about the DIP methods.
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Digital Image Fundamentals
This document provides an overview of digital image fundamentals including:
- The electromagnetic spectrum and how light is sensed and sampled by sensor arrays to create digital images.
- Common sensor technologies like CCD and CMOS sensors and how they work.
- How digital images are represented through spatial and intensity discretization via sampling and quantization.
- Factors that affect image quality like spatial and intensity resolution.
- Concepts like aliasing, moire patterns, and their relationship to sampling rates.
- Basic image processing techniques like zooming, shrinking, and relationships between pixels.
12-Image enhancement and filtering.ppt
This document discusses various techniques for image enhancement, which aims to improve the visual interpretability of images. It describes point operations and local operations that modify pixel brightness values. Common enhancement techniques mentioned include contrast manipulation through thresholding, stretching, and slicing. Spatial feature manipulation techniques like filtering and edge enhancement are also summarized. The document provides examples and explanations of contrast stretching, spatial filtering, and ratio images. It concludes with a brief overview of topographic correction to account for slope and aspect effects.
Lecture 4
Local neighborhood processing is a common technique in spatial domain image filtering. It involves defining a neighborhood around each pixel and applying an operation to the pixel values within the neighborhood. Common examples are mean and weighted mean filters, which average pixel values to reduce noise. Mean filters replace each pixel value with the average of neighboring pixels. Weighted mean filters assign more importance to central pixels and horizontally/vertically adjacent pixels compared to diagonal neighbors. Neighborhood processing is implemented by defining a filter kernel that specifies the operation and applying it to each pixel location.
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Basics of pixel neighbor.
- 1. Digital Image Processing. COURSE INSTRUCTOR DR IMRAN MUGHAL. PREPARED BY RAJA OSAMA RAJA RAHEEL
- 2. Content • Basic Relationship between pixels. • Pixel adjacency • Pixel connectivity • Pixel region • Region adjacency • Region boundary.
- 3. Neighbors of pixels. • A pixel p at location (x , y) has two horizontal and two vertical neighbors. • This set of four pixels (vertical & horizontal) is called 4 neighbors of pixel. p=N4(p) • If the p is boundary pixel then it will have less number of neighbor pixels. p(x , y-1) p(x -1 , y) p(x , y) p(x+1 , y) P(x , y+1)
- 4. Neighbors of pixel • A pixel p at location ( x ,y ) has 4 diagonal neighbors. P=ND(p) • If p is neighbor pixel then it will have less number of neighbor pixels. p(x-1 , y+1) p(x+1 , y-1) P(x , y) p(x-1 , y-1) P(x+1 , y+1)
- 5. Neighbors of pixels • Union of all 4 (vertical & horizontal ) and 4 ( diagonal ) neighbors is called 8 neighbors. • N8(p) =N4(p) U PD(p) • If p is boundary pixel then it will have less number of neighbor pixels. p(x-1 , y+1) p(x , y-1) p(x+1 , y-1) p(x-1 , y) p(x , y) p(x+1 , y) p(x-1 , y-1) p(x , y+1) P(x+1 , y+1)
- 6. Adjacency 4-Adjacency:- • Two pixels are adjacent if they have same intensity value and they are neighbors in set of p = N4(p). • In binary image two pixels are adjacent if they are neighbors and have same intensity either 0 or 1. 1 0 0 0 0 1 1 1 1
- 7. Adjacency Two pixels are adjacent if they have same intensity value and they are neighbors in set of p = N8(p). 1 0 0 0 0 1 1 1 1
- 8. Connectivity • Two pixels are said to be connected if a path exist between them. • Pixel connectivity based on 4 connectivity
- 9. Pixel Region & Region adjacency • Region can be a subset image made by pixels. • Region can be grow according to your need. • impixelregion is function to set pixel region in an image.
- 10. Pixel Region and Region Adjacency
- 11. Pixel Region and Region Adjacency Region adjacency • R1 and R2 will said to be Region adjacent if their union form a connected set. • Regions that are not adjacent are called disjoint. • There can be two type of region adjacency 4&8 adjacency.
- 12. Pixel Region and Region Adjacency EXAMPLE: R1 R2 Above regions are adjacent using only 8-adjacency. 1 1 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0
- 13. Boundary of Region • The boundary of the region R is the set of pixels in the region that have one or more neighbors that are not in R. 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 1 0 1 0
- 14. Thank you!