Robust Corner Detector Based on Local Maximum and Minimum Differences

In computer vision applications, corners are often regarded as desirable features due to their simplicity and low coordination requirements. Traditional intensity-based algorithms identify corners by examining the intensity relationship between neighboring and local regions, as well as the derivative information. Most detectors that solely utilize intensity information were developed before 2000, with FAST being an exception. Our approach is a new intensity-based corner detector that stands out by relying solely on pixel intensity for corner detection. We accomplish this by employing an innovative corner response function. Our method identifies corner locations by solely considering intensity values within a 3×3 neighborhood. By sorting pixels based on intensity and calculating the difference between one-third of the largest and smallest values, we generate a highly effective corner response map with strong discriminatory capabilities. Experimental evaluation on benchmark images demonstrates the superiority of our detector compared to seven established methods. Our method achieves better accuracy in corner localization and reduces both missed corner detections and false positives. Also, it requires only one parameter for adjustment, making it computationally efficient and allows for real-time processing potential. Furthermore, the generated corner response map holds promise for integration with deep learning architectures, opening possibilities for further exploration.