Recently, more and more new iris acquisition devices appear on the market. In practical situation, it is highly possible that the iris images for training and testing are acquired by different iris image sensors. In that case, the recognition rate will decrease a lot and become much worse than the one when both sets of images are acquired by the same image sensors. Such issue is called “cross-s...

This paper proposes a generic formulation that significantly expedites the training and deployment of image classification models, particularly under the scenarios of many image categories and high feature dimensions. As a defining property, our method represents both the images and learned classifiers using binary hash codes, which are simultaneously learned from the training data. Classifying...

Abstract Convolutional neural networks (CNNs) offer an alternative to the image cross-correlation methods used in particle velocimetry (PIV) reconstruct fluid velocity field from experimental recording. Despite flexibility of CNNs, accuracy and robustness standard processing remains unsurpassed for general PIV data. As CNNs are non-linear typically entail up millions trainable parameters, they ...

Hyperspectral image classification has gained great progress in recent years based on deep learning model and massive training data. However, it is expensive unpractical to label hyperspectral data implement constrained environment. To address this problem, paper proposes an effective ghost module spectral network for classification. First, Ghost3D adopted reduce the size of parameter dramatica...

Most image segmentation algorithms are trained on binary masks formulated as a classification task per pixel. However, in applications such medical imaging, this black-and-white approach is too constraining because the contrast between two tissues often ill-defined, i.e., voxels located objects' edges contain mixture of (a partial volume effect). Consequently, assigning single hard label can re...