Module 2.2 : Isolated Point Detection | Image Segmentation | Digital Image Processing скачать в хорошем качестве

Module 2.2 : Isolated Point Detection | Image Segmentation | Digital Image Processing

ISOLATED POINT DETECTION Isolated point detection in image segmentation refers to the process of identifying single pixels that differ significantly from their neighboring pixels, often representing unique or anomalous points in an image. This technique is crucial for detecting discontinuities such as isolated noise points or features that need to be distinguished from the background. Isolated Point Detection in Image Segmentation Isolated points are pixels that stand out distinctly compared to their surrounding pixels, often surrounded by a nearly constant gray level neighborhood. This detection forms one of the three fundamental types of discontinuities in digital images, alongside line and edge detection. The Laplacian operator, which uses the second derivative of the image intensity function, is commonly employed to detect isolated points because it highlights regions where there is a rapid change in intensity. The Laplacian operator is isotropic and does not provide directional information, making it suitable for detecting isolated points regardless of orientation. Mathematically, the Laplacian operator is implemented through convolution with a specific mask, analyzing the intensity variation around each pixel to determine if it qualifies as an isolated point based on a threshold response magnitude. In practice, isolated point detection can assist in highlighting features such as defects, noise, or small objects in an image, facilitating effective segmentation and further image analysis. Applications include detecting isolated porosity points in X-ray images of materials, which is useful in quality control and medical imaging. Isolated point detection thus plays a foundational role in image segmentation by focusing on pinpointing singular characteristic pixels, building a basis for more complex segmentation tasks. Digital image size refers to the dimensions and file size of an image, which are crucial for both display and printing purposes. Understanding these concepts enables effective use of images in various digital contexts. You may refer the following books to practice more numerical questions: 1. R.C.Gonzalez and R.E.Woods, “Digital Image Processing”, Prentice Hall, 3rd Edition,2011. 2. S. Sridhar , “Digital Image Processing”, Oxford University Press,2011 If you have any suggestion/feedback or if you want videos on any topic related to digital image processing , please do comments in my video or write email to me: ask2ajaysir@gmail.com #DigitalImageProcessing #ImageProcessing #ComputerVision #MachineLearning #AIImageProcessing #ImageEnhancement #ImageSegmentation #ImageAnalysis #DataScience #DeepLearning #ImageRecognition #ImageFilters #OpenCV #ComputerGraphics #ImageProcessingTutorials #MorphologicalOperations #ImageMorphology #DigitalImageProcessing #digitalimageprocessing #btechexams #ImageProcessingNumericals #UniversityExams #BTechPreparation #MidTermExams #EndTermExams #EngineeringExams #NumericalProblems #ImageProcessingTutorials #BTechStudyGuide #DigitalSignalProcessing #ExamPreparation #EngineeringNumericals #IndiaBTechStudents #ImageSegmentation, #DigitalImageProcessing, #ComputerVision, #MachineLearning, #DeepLearning, #AI, #ArtificialIntelligence, #PythonProgramming, #OpenCV, #MATLAB. #ImageProcessingTutorial, #DataScience, #DataAnalysis, #TechTutorial, #ScienceTutorial, #EngineeringTutorial, #LearnWithMe, #TechTips, #CodingTutorial, #TutorialSunday