Local Features in Object Recognition
Computer vision algorithms that use local feature-based objects recognition algorithms to recognize objects in an image are one type of local feature-based object recognition algorithm. These algorithms make use of distinct features such as lines and curves in an image to identify and compare objects. You can use techniques like Scale Invariant Binary Pattern (LBP), or Scale Invariant FFeature Transforms (SIFT). The features are extracted using metrics such as scale, orientation and local brightness patterns (Varma & Zisserman, 2015). The features are then compared with a database of pre-stored object features. The object is detected when there is a sufficient match between the extracted features from the image and those stored in the database (Rahman & Rahman, 2021). Local feature recognition algorithms can be computationally efficient. They are able to identify the exact location where the object is located and provide a precise representation with very little processing time. These local feature recognition algorithms can be used for real-time video processing. Because of their accuracy and low computational cost, local feature-based object recognition algorithms can be used for computer vision applications. References Rahman, M. S., & Rahman, S. (2021). Object Recognition using Discrete Cosine Transform (DCT). Journal of Engineering and Applied Sciences (16(7), 4259-4259. Varma, M., & Zisserman, A. (2015). An statistical method for texture classification using single images. International Journal of Computer Vision 62(1): 61-81.
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