tailieunhanh - Lecture Digital image processing - Lecture 26: Image segmentation

This chapter presents the following content: Link the edge points, local processing, global processing, hough transformation, image analysis, image segmentation, segmentation algorithms, discontinuities intensity, gradient operators linking the edge points. | Digital Image Processing CSC331 Image Segmentation 1 Summery of previous lecture Image analysis Image segmentation Segmentation Algorithms Discontinuities intensity: points, lines and edges Gradient Operators Linking the edge points 2 Todays lecture Link the edge points Local processing Global processing Hough transformation 3 Link the edge points When you apply the sobel operator to an image, the edges are not always connected Can be because of the noise Or non uniform illumination of the scene We need to link the edge points to get some meaningful edges to extract some meaningful edge information. There are 2 approaches by which linking can be done Local processing Global processing 4 Edge Linking and Boundary Detection Local Processing We need to take similarity measure of 2 points to link it Two properties of edge points are useful for edge linking: the strength (or magnitude) of the detected edge points their directions (determined from gradient directions) This is usually done in local neighborhoods. Adjacent edge points with similar magnitude and direction are linked. Edge Linking and Boundary Detection Local Processing: Example In this example, we can find the license plate candidate after edge linking process. Sometime we need the linking of the edge points that are discontinues and normally the neighborhood size that is taken is a small neighborhood. The local processing technique does not help to link the edge points. We need the global processing technique. Hough transformation 7 Hough transformation 8 9 10 11 12 13 Now, the problem comes when this straight line tries to be vertical that is parallel to x axis. slope of the straight line that is the value of m tends to be infinity and in this formulation, The value of m which becomes very large or which tends to become infinite can not be handled To solve this problem, instead of considering the slope intercept form, use the normal representation of a straight line. 14 15 16 17 18 19 Edge Linking and . | Digital Image Processing CSC331 Image Segmentation 1 Summery of previous lecture Image analysis Image segmentation Segmentation Algorithms Discontinuities intensity: points, lines and edges Gradient Operators Linking the edge points 2 Todays lecture Link the edge points Local processing Global processing Hough transformation 3 Link the edge points When you apply the sobel operator to an image, the edges are not always connected Can be because of the noise Or non uniform illumination of the scene We need to link the edge points to get some meaningful edges to extract some meaningful edge information. There are 2 approaches by which linking can be done Local processing Global processing 4 Edge Linking and Boundary Detection Local Processing We need to take similarity measure of 2 points to link it Two properties of edge points are useful for edge linking: the strength (or magnitude) of the detected edge points their directions (determined from gradient directions) This is usually done .

• Đồ họa - Thiết kế - Flash
• Image segmentation
• Digital image processing
• Lecture Digital image processing
• Digital images
• Digital computers
• Image processing
• Local processing
• Global processing
• Region growing
• International Journal of Computer Science and Communication Networks
• Robust watershed segmentation of noisy image using wavelet
• Robust watershed segmentation
• Noisy image using wavelet
• Few energy transform values
• Marker controlled Watershed Segmentation
• The additional function
• Dynamic semi supervised fuzzy clustering
• Dental X ray image segmentation
• Additional function
• Fuzzy C Means
• Supervised Texture Classification
• Medical Image Segmentation
• Automatic Segmentation
• Color Fundus Photographs
• Pancreatic Tumors
• Computed Tomography
• Intravascular
• Retinal blood vessel segmentation
• Tool to detect diabetic retinopathy
• Detect diabetic retinopathy
• Blood vessel segmentation software system
• Improving image quality
• Retinal blood vessels
• Three image processing stages
• BMC Bioinformatics
• Cell segmentation
• Batch processing
• Quantitative cellular analysis
• Brain tumor segmentation
• U net with image driven level set loss
• Cross entropy loss
• Mumford shah loss
• Level set method
• Activate contour model
• Deep learning
• Nuclei segmentation
• The fully convolutional neural network
• U Net model
• Defect detection
• K means and graph
• Original image
• Human operators
• Image classification
• Biological imaging
• Image features
• Segmentation free analysis
• High dimensional classification
• International Journal of Computer Science & Communication Networks
• Image enhancement for face recognition
• Color segmentation and edge detection algorithm
• Edge detection algorithm
• Color segmentation algorithm
• Muscle image segmentation
• Cloud computing
• Multi core programming
• Muscle cell segmentation
• Vertebra Detection
• Mean Shift
• Color Image
• Natural Scenes
• Texture Segmentation
• Statistical Pattern
• Integration of spatial fuzzy clustering
• Level set for efficient image segmentation
• Spatial fuzzy clustering
• Locally regularized evolution
• Curvelet transform
• Image denoise
• Histogram segmentation
• Wrapping based transform
• Unequally Spaced Fast Fourier Transform
• Shortest distance
• Dijkstra’s algorithm
• Label correcting
• Label correcting methods
• Image reconstruction
• Methods comparison
• Differential contrast image
• Quantitative phase imaging
• Laplacian of Gaussians