Abstract
In this paper, we present a new image patch descriptor for object detection and image matching. The descriptor is based on the standard HoG pipeline. The descriptor is generated in a novel way, by embedding the response of an oriented anisotropic derivative half Gaussian kernel in the Histogram of Orientation Gradient (HoG) framework. By doing so, we are able to bin more curvature information. As a result, our descriptor performs better than the state of art descriptors such as SIFT, GLOH and DAISY. In addition to this, we repeat the same procedure by replacing the anisotropic derivative half Gaussian kernel with a computationally less complex anisotropic derivative half exponential kernel and achieve similar results. The proposed image descriptors using both the kernels are very robust and shows promising results for variations in brightness, scale, rotation, view point, blur and compression. We have extensively evaluated the effectiveness of the devised method with various challenging image pairs acquired under varying circumstances.
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Acknowledgments
This work is funded by L’institut mediterraneen des metiers de la longevite (I2ML), Nimes, France.
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Venkatrayappa, D., Montesinos, P., Diep, D., Magnier, B. (2015). A Novel Image Descriptor Based on Anisotropic Filtering. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_14
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DOI: https://doi.org/10.1007/978-3-319-23192-1_14
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