Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images (1710.02726v1)

Abstract: Fast and robust image matching is a very important task with various applications in computer vision and robotics. In this paper, we compare the performance of three different image matching techniques, i.e., SIFT, SURF, and ORB, against different kinds of transformations and deformations such as scaling, rotation, noise, fish eye distortion, and shearing. For this purpose, we manually apply different types of transformations on original images and compute the matching evaluation parameters such as the number of key points in images, the matching rate, and the execution time required for each algorithm and we will show that which algorithm is the best more robust against each kind of distortion. Index Terms-Image matching, scale invariant feature transform (SIFT), speed up robust feature (SURF), robust independent elementary features (BRIEF), oriented FAST, rotated BRIEF (ORB).

• The paper demonstrates that SIFT achieves high matching accuracy and robustness, while SURF balances speed and ORB excels in real-time efficiency.
• It reveals that SIFT consistently outperforms in intensity and fisheye distortions, whereas ORB and SURF are optimized for faster processing under rotations and scaling.
• The study suggests that integrating these algorithms or hybridizing their strengths could further enhance image matching in computer vision applications.

Performance Comparison of Image Matching Techniques: SIFT, SURF, and ORB

The paper presents a comparative analysis of prominent image matching algorithms—SIFT, SURF, and ORB—evaluating their performance under various image distortions. This paper is critical for applications in fields like computer vision and robotics, particularly where real-time processing and robustness to image transformations are imperative.

Overview of Image Matching Techniques

The paper concentrates on three image matching algorithms, each with unique characteristics:

1. Scale Invariant Feature Transform (SIFT): Developed by Lowe, SIFT is known for its robustness to image transformations such as scale, rotation, and affine transformations. It involves a multi-step process that includes scale-space extrema detection using Difference of Gaussian (DoG), keypoint localization, orientation assignment, and distinctive descriptor generation. Despite its accuracy, SIFT's computational complexity can be a limitation in real-time applications.
2. Speeded Up Robust Features (SURF): SURF is an evolution of SIFT that approximates the DoG with box filters, resulting in faster processing. It employs a Hessian-based detector and a wavelet-based descriptor, focusing on efficient feature matching without sacrificing significant accuracy. SURF is particularly effective due to its reliance on integral images for rapid convolution operations.
3. Oriented FAST and Rotated BRIEF (ORB): ORB, an alternative to SIFT and SURF, combines the FAST keypoint detector with the BRIEF descriptor, enhanced by introducing rotations to handle in-plane rotations more effectively. It is noted for its speed, making it suitable for applications where computational efficiency is a priority.

Comparative Analysis and Simulation Results

The research investigates the robustness and computational performance of these algorithms against varying intensities, rotations, scaling, shearing, fisheye distortions, and noise. Key evaluation metrics include the number of keypoints, the matching rate, and execution time.

• Intensity Variations: SIFT demonstrates superior performance with the highest matching rate, though ORB offers faster execution.
• Rotational Distortions: SIFT remains effective across most angles, although ORB and SURF outperform it at rotative angles proportional to 90 degrees.
• Scaling Effects: ORB achieves the highest matching rate, showcasing its superior scalability handling.
• Shearing Displacements: SIFT provides the most robust performance in terms of matching rate.
• Fisheye Distortions and Noisy Images: SIFT maintains a consistently high matching rate, but ORB's performance is competitive, especially in noise scenarios, highlighting its potential in real-time environments where noise is prevalent.

Implications and Prospects

The paper delineates clear distinctions among the image matching algorithms with significant implications for both theoretical and practical applications. SIFT's robustness is evident across various transformations, making it suitable for scenarios where accuracy is paramount. SURF's balance between speed and performance positions it as a versatile choice, whereas ORB's computational efficiency lends it significant promise in applications constrained by processing time.

Future advancements may focus on hybrid approaches that leverage the strengths of these algorithms, enhancing adaptability and performance under newly emerging image distortions. Moreover, integrating these techniques with machine learning frameworks could further optimize feature extraction and matching processes, propelling developments in image recognition and autonomous systems.

The paper provides an essential benchmark in understanding current strengths and limitations of image matching techniques, offering a foundation for subsequent research intended to optimize and innovate within this domain.