Clustering algorithms have successfully been applied as a digital image segmentation technique in various fields and applications. However, those clustering algorithms are only applicable for specific images such as medical images, microscopic images etc. In this paper, we present a new clustering algorithm called Adaptive Fuzzy-K-means (AFKM) clustering for image segmentation which could be applied on general images and/or specific images (i.e., medical and microscopic images), captured using different consumer electronic products namely, for example, the common digital cameras and CCD cameras. The algorithm employs the concepts of fuzziness and belongingness to provide a better and more adaptive clustering process as compared to several conventional clustering algorithms. Both qualitative and quantitative analyses favour the proposed AFKM algorithm in terms of providing a better segmentation performance for various types of images and various number of segmented regions. Based on the results obtained, the proposed algorithm gives better visual quality as compared to several other clustering methods.

This study optimized the latest YOLOv5 framework, including its subset models, with training on different datasets that differed in image contrast and cloudiness to assess model performances based on quantitative metrics and image processing speed. The hyperparameter in the feature-extraction phase was configured based on the learning rate and momentum and further improved based on the adaptive moment estimation (ADAM) optimizer and the function reducing-learning-rate-on-plateau to optimize the model’s training scheme. The optimized YOLOv5s achieved a better performance, with a mean average precision of 98.6% and a high inference speed of 106 frames per second. The ADAM optimizer with a detailed learning rate (0.0001) and momentum (0.99) fine-tuning yielded a sufficient convergence rate (0.69% at 55th epoch) to assist YOLOv5s in attaining a more precise detection for underwater objects.

In Magnetic Resonance (MR) images, noise is a common issue which limits the image accuracy of any quantitative measurements. Noise elimination in MRI image pre-processing is an important step to eliminate the noise and to make the image fit for further steps involved in the process of analyzing. However, different types of noises produces ranges of significant impact on image quality, and thus tend to affect human interpretation and performance of computer-aided diagnosis systems. Another issue is about filtering strategies to eliminate noise and preserve high quality image depending on filter reconstruction ability and noise model. In this work three different filtering algorithms such as Median filter (MF), Adaptive filter (ADF) and Average filter (AVF) are used to remove the additive noises present in the MRI images i.e. Gaussian, Salt and pepper and speckle noise. The noise density was gradually added to MRI image up to 90% to compare performance of the filters by qualitative and quantitative evaluation. The performance of these filters are compared using the statistical parameters such as Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The study shows that Median filter reconstructs a high quality image than other filters in Gaussian and Salt and pepper denoising with 38.3dB PSNR at 10% noise variance. While for speckle noise removal, Average filter is perform better than others which result of 56.2dB PSNR at 10% noise variance. A comparison with other well-established methods, this study shows that the Median and Average filter produces better denoising results, preserving the main structures and details