Real-time-based Object Recognition using SIFT algorithm

In this paper, we propose a real-time object recognition system that makes use of local image features using Scale Invariant Feature Transform (SIFT). The features are partial changes in illumination and affine, and they are invariant to rotation, image scaling, and translation. Similar characteristics are shared by these features and the inferior temporal cortex neurons involved in object recognition. Features can be efficiently detected using a phased filtering technique in scale space. Image keys generally support the local geometric information representing the blurred image gradient at different scales and orientation planes. An indexing technique called nearest-neighbor search (NSS) uses the keys as input to determine the object matches. Based on the number of descriptors we categorize the object is recognized or not, and the final output is displayed in the microcontroller-based display unit.