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• W2982243647 abstract "Very low resolution (VLR) image recognition corresponds to classifying images with resolution 16×16 or less. Though it has widespread applicability when objects are captured at a very large stand-off distance (e.g. surveillance scenario) or from wide angle mobile cameras, it has received limited attention. This research presents a novel Dual Directed Capsule Network model, termed as DirectCapsNet, for addressing VLR digit and face recognition. The proposed architecture utilizes a combination of capsule and convolutional layers for learning an effective VLR recognition model. The architecture also incorporates two novel loss functions: (i) the proposed HR-anchor loss and (ii) the proposed targeted reconstruction loss, in order to overcome the challenges of limited information content in VLR images. The proposed losses use high resolution images as auxiliary data during training to direct discriminative feature learning. Multiple experiments for VLR digit classification and VLR face recognition are performed along with comparisons with state-of-the-art algorithms. The proposed DirectCapsNet consistently showcases state-of-the-art results; for example, on the UCCS face database, it shows over 95% face recognition accuracy when 16×16 images are matched with 80×80 images." @default.
• W2982243647 created "2019-11-01" @default.
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• W2982243647 date "2019-10-01" @default.
• W2982243647 modified "2023-10-01" @default.
• W2982243647 title "Dual Directed Capsule Network for Very Low Resolution Image Recognition" @default.
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• W2982243647 doi "https://doi.org/10.1109/iccv.2019.00043" @default.
• W2982243647 hasPublicationYear "2019" @default.
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