Abstract: Fine-grained flower image classification (FGFIC) is challenging due to high similarities among species and variations within species, especially with limited training data. Existing genetic ...
Abstract: Street view (SV) images provide valuable supplementary data for characterizing the functional attributes of land use types, improving urban land use classification based on ...
Abstract: Image steganography conceals secret data within a cover image to generate a new image (stego image) in a manner that makes the secret data undetectable. The main problem in image ...
This project implements a state-of-the-art CNN architecture for CIFAR-10 image classification, achieving 88.82% accuracy through systematic hyperparameter optimization. The implementation includes GPU ...
Abstract: As a crucial non-invasive imaging modality in clinical diagnosis, ultrasound interpretation faces challenges of subjectivity and inefficiency. To address the limitations of traditional ...
Hyperspectral imaging (HSI) is a next generation remote sensing technology which collects images in the hundreds of narrow, contiguous spectral bands [1]. Contrary to typical RGB or multispectral ...
Abstract: In this paper, we propose a lightweight privacy-preserving convolutional neural network framework for military vehicle images classification (LPP-CNN). Existing target classification methods ...
Abstract: Brain tumors are among the deadliest diseases worldwide and require early and accurate diagnosis via Magnetic Resonance Imaging (MRI). Deep learning techniques, particularly convolutional ...
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