Traditional Computer Vision Techniques
Traditional computer vision techniques rely on manual feature extraction and classical algorithms to interpret images and videos. These methods have been used for decades and involve a sequence of steps to process and analyze visual data.
Key Components of Traditional Computer Vision Techniques
- Image Preprocessing:
- Filtering: Techniques like Gaussian blur, median filtering, and edge detection (e.g., Sobel, Canny) are used to enhance image features.
- Transformation: Operations such as scaling, rotation, and affine transformations adjust the image to a standard form.
- Feature Extraction:
- Descriptors: Methods like Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Histogram of Oriented Gradients (HOG) extract distinctive features from images.
- Keypoints: Algorithms detect points of interest in the image, which are used to describe the content.
- Feature Matching:
- Algorithms: Techniques such as brute-force matching, FLANN-based matcher, and RANSAC are employed to match features between images for tasks like object recognition and image stitching.
- Classification:
- Machine Learning Models: Algorithms like Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Random Forests classify the extracted features.
Difference between Traditional Computer Vision Techniques and Deep Learning-based Approaches
Computer vision enables machines to interpret and understand the visual world. Over the years, two main approaches have dominated the field: traditional computer vision techniques and deep learning-based approaches.
This article delves into the fundamental differences between these two methodologies and how can be answered in the interview.
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