Abstract The Extreme Learning Machine is a single-hidden-layer feedforward learning
algorithm, which has been successfully applied in regression and classification problems in …

Well-known compressed sensing (CS) is widely used in image acquisition and
reconstruction. However, accurately reconstructing images from measurements at low …

This paper proposes a multi-threshold image segmentation method based on improved
emperor penguin optimization (EPO). The calculation complexity of multi-thresholds …

This paper introduces sparsity in the traditional fuzzy clustering framework and presents two
novel clustering methods. The first one is called deviation-sparse fuzzy c-means (DSFCM) …

Traditional fuzzy clustering algorithms suffer from two problems in image segmentations.
One is that these algorithms are sensitive to outliers due to the non-sparsity of fuzzy …

Since a noisy image has inferior characteristics, the direct use of Fuzzy-Means (FCM) to
segment it often produces poor image segmentation results. Intuitively, using its ideal value …

Purpose To evaluate the feasibility of using compressed sensing (CS) to accelerate 3D‐T1ρ
mapping of cartilage and to reduce total scan times without degrading the estimation of T1ρ …

An improvement of the monotone fast iterative shrinkage-thresholding algorithm (MFISTA)
for faster convergence is proposed in this paper. Our motivation is to reduce the …

Electrical impedance tomography (EIT) is non-destructive monitoring technology that can
visualize the conductivity distribution in the observed area. The inverse problem for imaging …

Moving object detection is a fundamental task in many video processing applications, such
as video surveillance. The robustness and efficiency of background subtraction make it one …