Efficient invariant interest point detector using Bilateral-Harris corner detector for object recognition application

Interest point detection plays a significant role in computer vision applications. The most commonly used interest point detector algorithm is scale invariant feature transform (SIFT). The use of Gaussian filter in the SIFT algorithm fails to match interest points on the edge and it also causes blur annoyance in the rescaling process. To overcome this failure Bilateral-Harris Corner Detector (BHCD) has been proposed in this paper. In the proposed BHCD, a Bilateral filter preserves edges by smoothening and removing noise in an image. Accuracy in localization of interest points are improved by using the proposed dynamic blur metric calculation. The Harris corner has been added to get stable and reliable interest point detection. The proposed BHCD has been simulated for the evaluation criteria such as repeatability and matching score. Extensive experimental results show that the proposed method is more robust to illumination, scaling, rotation, compression and viewpoint changes. The experimental evaluation for BHCD has been carried for the object recognition benchmark datasets COIL-100, ZuBud, Caltech-101. The proposed BHCD achieves highest recognition rate compared to the other state-of-the-art methods.