Chaotic fitness-dependent quasi-reflected Aquila optimizer for superpixel based white blood cell segmentation
The crisp partitional clustering techniques like K-Means (KM) are an efficient image
segmentation algorithm. However, the foremost concern with crisp partitional clustering …
segmentation algorithm. However, the foremost concern with crisp partitional clustering …
[PDF][PDF] Biomedical Image Segmentation with Modified U-Net.
U Tatli, C Budak - Traitement du Signal, 2023 - researchgate.net
In recent years, image segmentation has become a widely researched topic. It serves as an
essential element in numerous visual applications. Image Segmentation is the process of …
essential element in numerous visual applications. Image Segmentation is the process of …
Hybrid 3D convolution and 2D depthwise separable convolution neural network for hyperspectral image classification
Convolutional neural networks (CNNs) are one of the popular deep learning methods used
to solve the hyperspectral image classification (HSIC) problem. CNN has a strong feature …
to solve the hyperspectral image classification (HSIC) problem. CNN has a strong feature …
Membership Adjusted Superpixel Based Fuzzy C-Means for White Blood Cell Segmentation
Fuzzy C-means (FCM) is a well-known clustering technique that is efficiently used for image
segmentation. However, the performance of the FCM degrades for noisy images and slow …
segmentation. However, the performance of the FCM degrades for noisy images and slow …