image processing powerpoint presentation for 6th sem

This power point presentation explains the topic of fundamental steps in image processing for the 6th semester students of btech.

1. SUBMITTED TO—MISS MITHU MAL SUBMITTED BY-SHYAM KUMAR SAHU STREAM—COMPUTER SCIENCE AND ENGINNERING SECTION - ‘B’ UNIVERSITY ROLL NO-12500121080 SUBJECT—IMAGE PROCESSING SUBJECT CODE—PEC-IT601D PRESENTATION ON FUNDAMENTAL STEPS IN IMAGE PROCESSING

2. CONTENTS INTRODUCTION TO IMAGE PROCESSING IMAGE ACQUISITION IMAGE PREPROCESSING IMAGE ENHANCEMENT IMAGE RESTORATION IMAGE SEGMENTATION FEATURE EXTRACTION IMAGE CLASSIFICATION

3. Introduction to Image Processing Image processing is a method to perform operations on an image to extract information or enhance features, leading to improved interpretation or pattern recognition. It involves multiple fundamental steps.

4. Image Acquisition Digital Cameras Digital cameras capture images and convert them into electronic data. They are widely used for image acquisition in photography and various applications. Scanners Scanners are used to digitize photographs, documents, and other 2D objects into digital images, which can then be processed using image processing techniques. Remote Sensing Remote sensing technologies, such as satellites or drones, are used to capture images for environmental monitoring and geographical mapping.

5. Image Preprocessing Noise Reduction Removal of noise or unwanted artifacts from the image to improve its quality and clarity. Normalization Adjusting the range of pixel values to make the image more uniform or consistent for further processing. Image Scaling Resizing the image to meet specific requirements, such as standardizing dimensions or file size.

6. Image Enhancement Contrast Adjustment Modifying the distribution of intensity levels to enhance the visual features of the image, making it more clear and vibrant. Sharpening Improving the clarity and focus of the image by enhancing the edges and details. Color Correction Adjusting the color balance and tones to achieve the desired visual appearance of the image.

7. Image Restoration Noise Reduction Eliminate noise artifacts caused by the capture process. Deblurring Recover details and sharpness in blurred images. Image Inpainting Reconstruct missing or damaged parts of the image.

8. Image Segmentation 1 Thresholding Divides the image into binary regions based on pixel intensity. 2 Edge Detection Finds boundaries within the image. 3 Region Growing Identifies regions based on pixel similarities.

9. Feature Extraction 1 Corner Detection Identifies key points in the image. 2 Texture Analysis Evaluates spatial patterns in the image. 3 Color Histogram Quantifies the distribution of colors in the image.

10. Image Classification Supervised Learning Classifies images based on labeled training data. Unsupervised Learning Finds patterns and clusters in images without pre-existing labels. Deep Learning Utilizes neural networks for advanced image recognition and classification.