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M esh filtering of surfaces is crucial for noise reduction, feature preservation, and mesh simplification in graphics, visualization, and computer vision. In this paper, the detail preservation capacities of 3 frequently used filters, i.e., Bilateral, Laplacian, and Taubin mesh filters, in mesh filtering have been thoroughly examined by experiments conducted on 4 different test meshes. While the Bilateral filter excels in preserving sharp features due to its integration of geometric proximity with intensity similarity, the Laplacian filter prioritizes smoothness by averaging neighboring vertex positions, and the Taubin filter offers a balanced approach by merging attributes of both Laplacian and high-pass filters. The Bilateral filter's primary strength lies in its ability to maintain sharp features on a mesh, ensuring that intricate details are preserved by considering both the spatial closeness and intensity similarity of vertices. The Laplacian filter, although effective in achieving mesh smoothness, has the propensity to excessively smooth out sharp and defining features, potentially causing a loss of critical details in the processed mesh. The Taubin filter integrates the best of both worlds, ensuring smoothness without excessive mesh shrinkage; however, it might not excel in feature preservation as effectively as the Bilateral filter or smooth as uniformly as the Laplacian filter, making it a middle-ground option for certain applications. The statistical analysis of the experimental results has shown that the Taubin method is statistically a more successful mesh filtering method for the test sets used in this paper. [ABSTRACT FROM AUTHOR]

Digital image processing heavily relies on the connectivity of pixels, as it is a vital component for accurate object identification and analysis within an image. Grouping together pixels with similar features such as colour and intensity, allows for the formation of meaningful patterns or objects, which is essential for object recognition and segmentation. This approach is particularly valuable in photogrammetric imaging, video surveillance, deep learning as it facilitates the isolation of regions of interest and object tracking. Image smoothing is also a crucial aspect in enhancing visual quality by reducing noise and enhancing details, especially in applications such as aerial mapping, medical imaging, video compression, image resizing and computer vision. The absence of connected pixels and image smoothing would make image processing tasks more challenging and less reliable, making them fundamental to digital image processing and critical to various applications in diverse fields. This paper introduces a novel image smoothing filter called Connected Pixels Based Image Smoothing Filter (CPF), which is based on gray connected pixels. The success of the CPF was compared to that of the Non-Local Means Filter (NLMF) in terms of Structural Similarity Index (SSIM) for the same Mean Squared Error (MSE). The experimental results showed that CPF has a better ability to preserve image details compared to NLMF. [ABSTRACT FROM AUTHOR]

Hyperspectral images are widely used for land use/cover analysis in remote sensing due to their rich spectral information. However, these data often suffer from noise caused by various factors such as random and systematic errors, making them less useful for end-users. In this study, denoising methods (i.e., DnCNN, NGM, CSF, BM3D, and Wiener) for hyperspectral images were compared using the Pavia University hyperspectral dataset with four different noise types: Gaussian, Salt & Pepper, Poisson, and Speckle. After denoising, the k-nearest neighbor method was used to classify the image, and statistical and visual performance comparisons were performed on the classified data. Six performance metrics -Accuracy, Sensitivity, Specificity, Precision, F-Score, and G-Mean- were employed to compare the outcomes qualitatively. The findings demonstrate that DnCNN and BM3D have the best outcome performance for all four noise types. Due to their lack of sensitivity and specificity, the CSF and Wiener approaches had low performance for particular noise sources. For all noise types, the NGM approach had the worst results. The validated instruments not provide effective results when it came to denoising Salt & Pepper noise, but they managed to produce outstanding results when it came to denoising Poisson noise. In order to enhance the quality and usability of hyperspectral images for land use/cover analysis, this study emphasizes the significance of choosing an effective denoising technique. [ABSTRACT FROM AUTHOR]

In this paper, a new and effective unsupervised change detection technique is presented for Synthetic Aperture Radar Images (SAR) images. Unsupervised change detection is one of the important research topics in many application areas such as agricultural applications, monitoring of forests and urbanization, damage assessment after any disaster. SAR images are often used in change detection applications because they can provide images in all weather conditions unlike optical images. In this paper, a new change detection approach based on PCA blocking and Gaussian Mixture Models (GMM) clustering for SAR images was proposed. The approach firstly computes logarithmic difference image. Then, wiener and median filter are applied to reduce the effect of speckle noise in difference image and to preserve edge information. After that, the approach calculates eigenvector space from kxk blocks with PCA. Lastly, change detection map is computed using GMM clustering. Bern and Ottawa datasets were used to evaluate the proposed approach. Proposed method was compared with PCADS, PCA-FCM and PCA-Kmeans methods. Quantitative and qualitative analysis was conducted based on the Ground Truth (GT) images belong same geographical regions. According to results, proposed approach is quite successful. [ABSTRACT FROM AUTHOR]

This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. The training performance of the ABC algorithm is compared with the Genetic algorithm, Kalman filtering algorithm and gradient descent algorithm. In the experiments, not only well known classification problems from the UCI repository such as the Iris, Wine and Glass datasets have been used, but also an experimental setup is designed and inertial sensor based terrain classification for autonomous ground vehicles was also achieved. Experimental results show that the use of the ABC algorithm results in better learning than those of others. [ABSTRACT FROM AUTHOR]