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A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision., , , and . CoRR, (2019)A Low Power, Fully Event-Based Gesture Recognition System., , , , , , , , , and 6 other author(s). CVPR, page 7388-7397. IEEE Computer Society, (2017)Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI., and . Medical Image Anal., 12 (3): 335-357 (2008)LATTE: Low-power Audio Transform with TrueNorth Ecosystem., , , , , , , , , and . IJCNN, page 4270-4277. IEEE, (2016)On Sensor Bias in Experimental Methods for Comparing Interest-Point, Saliency, and Recognition Algorithms., and . IEEE Trans. Pattern Anal. Mach. Intell., 34 (1): 110-126 (2012)Information Fusion for Multi-camera and Multi-body Structure and Motion., and . ACCV (1), volume 4843 of Lecture Notes in Computer Science, page 385-396. Springer, (2007)50 Years of object recognition: Directions forward., and . Comput. Vis. Image Underst., 117 (8): 827-891 (2013)A Low Power, High Throughput, Fully Event-Based Stereo System., , , , and . CVPR, page 7532-7542. Computer Vision Foundation / IEEE Computer Society, (2018)11.4 IBM NorthPole: An Architecture for Neural Network Inference with a 12nm Chip., , , , , , , , , and 22 other author(s). ISSCC, page 214-215. IEEE, (2024)A low-power neurosynaptic implementation of Local Binary Patterns for texture analysis., , , and . IJCNN, page 4308-4316. IEEE, (2016)