github-actions[bot]
commented
5 months ago Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊
Note: This repo is for beginners to learn and start with Opensource we won't accept more than 10 issues from a single person, This restriction applies to Gssoc project which has a similar kind of adding folder files, Points will be reduced when we find Spam.
I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue.
You can also check our CONTRIBUTING.md for guidelines on contributing to this project.
anushkasaxena07
SHRAVANIc01
sanjay-kv
Image Resize Description: Adjusting the dimensions of an image. Benefit: Standardizes input size for machine learning models, reduces computation, and can help speed up processing. Application: Preparing images for neural networks, reducing image storage size.
Image Crop Description: Extracting a specific region from an image. Benefit: Focuses on the region of interest, removes unwanted parts, and can help in improving model accuracy by providing only relevant information. Application: Object detection, facial recognition.
Edge Detection Sobel, Prewitt, Canny Description: Techniques to highlight the edges within an image. Benefit: Essential for feature extraction, helps in identifying object boundaries, and is useful in various image analysis tasks. Application: Object detection, image segmentation, pattern recognition.
Noise Removal Description: Reducing or eliminating noise from an image. Benefit: Enhances image quality, improves feature extraction, and can lead to better model performance. Application: Medical imaging, photography, and any application where image clarity is crucial.
Image Conversion (Grayscale, Binary) Description: Converting an image to grayscale (single channel) or binary (black and white). Benefit: Simplifies the image, reduces computational complexity, and is often required for certain algorithms. Application: OCR (Optical Character Recognition), thresholding, and preparatory steps for edge detection.
Histogram Equalization Description: Adjusting the contrast of an image using its histogram. Benefit: Enhances the contrast, improves the visibility of features, and can make details in dark or bright regions more discernible. Application: Enhancing medical images, satellite imagery, and improving the visual quality of photographs.
Use Case Preprocessing on single and multiple images using python. this will help users understanding pre processing techniques and increase accuracy of the model Benefits and Applications Summary Image Resize: Standardizes size, reduces computation. Image Crop: Focuses on important regions. Edge Detection: Extracts structural information. Noise Removal: Enhances image quality. Image Conversion: Simplifies processing, reduces complexity. Histogram Equalization: Improves contrast and visibility of features.