Adaptive restoration of multispectral datasets used for SVM classification

Removing noise from images while keeping its important details unchanged is a challenging issue in image restoration. In this paper, we propose a novel approach based on partial differential equations (PDE) in order to mitigate three well-known types of noises from remote sensing data while important features such as edges are preserved. In the presented method, after performing the Watershed-based segmentation as a preprocessing step, optimum values of PDE parameters are adaptively found based on the noise type and the image texture. In order to evaluate the performance of the proposed algorithm, Peak Signal-to-Noise Ratio (PSNR) criterion is applied. Moreover, feeding the original/noisy/denoised images into SVM classifier and exploring the classification ratios are suggested as an application-based assessment. The gained results prove a considerable enhancement both in quantitative metrics (PSNR and MSE) and SVM classification ratios (from 71.71% to 95.07%)

Gender classification based on fuzzy clustering and principal component analysis

Gender classification is one of the most challenging problems in computer vision. Facial gender detection of neonates and children is also known as a highly demanding issue for human observers. This study proposes a novel gender classification method using frontal facial images of people. The proposed approach employs principal component analysis (PCA) and fuzzy clustering technique, respectively, for feature extraction and classification steps. In other words, PCA is applied to extract the most appropriate features from images as well as reducing the dimensionality of data. The extracted features are then used to assign the new images to appropriate classes – male or female – based on fuzzy clustering. The computational time and accuracy of the proposed method are examined together and the prominence of the proposed approach compared to most of the other well‐known competing methods is proved, especially for younger faces. Experimental results indicate the considerable classification accuracies which have been acquired for FG‐Net, Stanford and FERET databases. Meanwhile, since the proposed algorithm is relatively straightforward, its computational time is reasonable and often less than the other state‐of‐the‐art gender classification methods

Live-cell imaging of circadian clock protein dynamics in CRISPR-generated knock-in cells

Live-cell recordings have been an important tool for studying circadian rhythms. Here the authors use CRISPR gene editing mediated knock-in to fluorescently tag Per2 and Cry1, and study cellular circadian dynamics of these two clock proteins