Object Class Detection and Classification using Multi Scale Gradient and Corner Point based Shape Descriptors
This work addresses object recognition in computer vision, but it appears incremental as it builds on existing descriptors and classifiers.
The paper tackles object class detection and classification by introducing multi-scale gradient and corner point shape descriptors, achieving robustness to various deformations and higher accuracy with faster detection.
This paper presents a novel multi scale gradient and a corner point based shape descriptors. The novel multi scale gradient based shape descriptor is combined with generic Fourier descriptors to extract contour and region based shape information. Shape information based object class detection and classification technique with a random forest classifier has been optimized. Proposed integrated descriptor in this paper is robust to rotation, scale, translation, affine deformations, noisy contours and noisy shapes. The new corner point based interpolated shape descriptor has been exploited for fast object detection and classification with higher accuracy.