Abstract
Image recognition and detection play an important role in many fields, especially in night-vision technology. Traditional methods of image recognition are largely based on information extracted from an image to classify. This requires users to select appropriate features to set feature representations for original images per the specific circumstances. Manual selection of features is a laborious and heuristic work, and the features acquired in this way usually have poor robustness. To automatically obtain more robust and stronger generalisation image features, many scholars have applied machine-learning methods. Through training with large volumes of data samples, the performance of recognition and detection is improved with better accuracy and robustness.
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Bai, L., Han, J., Yue, J. (2019). Learning-Based Night-Vision Image Recognition and Object Detection. In: Night Vision Processing and Understanding. Springer, Singapore. https://doi.org/10.1007/978-981-13-1669-2_6
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DOI: https://doi.org/10.1007/978-981-13-1669-2_6
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