Conference Papers

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    PCVOS: Principal component variances based off-line signature verification
    (Institute of Electrical and Electronics Engineers Inc., 2015) Arunalatha, J.S.; Prashanth, C.R.; Tejaswi, V.; Shaila, K.; Raja, K.B.; Anvekar, D.; Venugopal, K.R.; Iyengar, S.S.; Patnaik, L.M.
    Offline signature verification system is widely used as a behavioral biometric for identifying a person. This behavioral biometric trait is a challenge in designing the system that has to counter intrapersonal and interpersonal variations. In this paper, we propose a novel technique PCVOS: Principal Component Variances based Off-line Signature Verification on two critical parameters viz., the Pixel Density (PD) and the Centre of Gravity (CoG) distance. It consists of two parallel processes, namely Signature training which involves extraction of features from the samples of database and Test signature analysis which performs extraction of features from the test samples. The trained values from the database are compared with the features of the test signature using Principal Component Analysis (PCA). The PCVOS algorithm shows a notable improvement over the algorithms in [21], [22] and [23]. © 2015 IEEE.
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    Enhanced Video Mosaic Generation: Efficient ORB Feature Extraction and Hamming Distance Matching
    (Institute of Electrical and Electronics Engineers Inc., 2024) Shridhar, H.; Harakannanavar, S.S.; Chetan, R.; Kanabur, V.; Jayalaxmi, H.; Prashanth, C.R.
    Video mosaicing is a technique for creating expansive panoramic views by seamlessly combining multiple video frames. This paper introduces an innovative algorithm for video mosaic generation, emphasizing the alignment and blending of non-overlapping frames. The proposed algorithm utilizes the ORB feature extraction technique to effectively address challenges related to camera motion and content variations across frames, ensuring a smooth and continuous mosaic output. Key aspects of the algorithm include optimizing the number of feature matches for frame stitching, which critically impacts the final mosaic's quality. Experimental results demonstrate the algorithm's ability to produce visually coherent and high-quality video mosaics. The model has been tested on real-time video frames, showing improved performance of 95% of accuracy with lower RMSE and contributing to the advancement of video mosaicing techniques for applications in surveillance and other fields requiring comprehensive scene views. © 2024 IEEE.