Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.

This paper proposes a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA to solve problems with continuous and binary decision variables. The SCMWOA algorithm is first tested on nineteen datasets from the UCI Machine Learning Repository with different numbers of attributes, instances, and classes for feature selection. It is then employed to solve several benchmark functions and classical engineering case studies. The SCMWOA algorithm is applied for solving constrained optimization problems. The two tested examples are the welded beam design and the tension/compression spring design. The results emphasize that the SCMWOA algorithm outperforms several comparative optimization algorithms and provides better accuracy compared to other algorithms. The statistical analysis tests, including one-way analysis of variance (ANOVA) and Wilcoxon’s rank-sum, confirm that the SCMWOA algorithm performs better.

Blood pressure (BP) changes with age are widespread, and systemic high blood pressure (HBP) is a serious factor in developing strokes and cognitive impairment. A non-invasive methodology to detect changes in human brain’s vasculature using Magnetic Resonance Angiography (MRA) data and correlation of cerebrovascular changes to mean arterial pressure (MAP) is presented. MRA data and systemic blood pressure measurements were gathered from patients (n = 15, M = 8, F = 7, Age = 49.2 ± 7.3 years) over 700 days (an initial visit and then a follow-up period of 2 years with a final visit.). A novel segmentation algorithm was developed to delineate brain blood vessels from surrounding tissue. Vascular probability distribution function (PDF) was calculated from segmentation data to correlate the temporal changes in cerebral vasculature to MAP calculated from systemic BP measurements. A 3D reconstruction of the cerebral vasculature was performed using a growing tree model. Segmentation results recorded 99.9% specificity and 99.7% sensitivity in identifying and delineating the brain’s vascular tree. The PDFs had a statistically significant correlation to MAP changes below the circle of Willis (p-value = 0.0007). This non-invasive methodology could be used to detect alterations in the cerebrovascular system by analyzing MRA images, which would assist clinicians in optimizing medical treatment plans of HBP.

This paper presents SfM ToAEval, a framework for evaluating different feature extraction algorithms in Structure from Motion (SfM) pipelines. SfM ToAEval allows automatic evaluation of the effect of using different feature detectors and descriptors combinations on the quality of the 3D reconstruction of stationary objects or scenes from a given collection of image sequences. In addition, SfM ToAEval evaluates the 3D reconstruction without the need for ground truth. Moreover, SfM ToAEval is aware of the reconstruction density-accuracy trade-off, and it supports visualizing it to enable deciding the “best” reconstruction transparently. Furthermore, SfM ToAEval allows quantifying the quality of each 3D reconstruction compared to others. SfM ToAEval was used to evaluate 98 feature detectors and descriptors combinations on six image sequences, and it was able to identify four promising combinations. Experimental results comparing the proposed combinations with related work are presented in this research. The complete source code of the proposed framework as well as a minimal Jupyter Notebook demonstrating how different functionalities can be used are released under the MIT license.

A content-based image retrieval system is proposed using an optimized weighted feature voting technique with multifeatures and multidistance measures. The proposed system consists of two phases: enrollment phase and querying phase. In enrollment phase, six features have been extracted from each enrollment image. These features are color histogram, edge histogram, edge direction histogram, hierarchical annular histogram, Gabor filter, and co-occurrence matrix. The extracted features are weighted, and feature weights are optimized using the four optimization techniques. Particle swarm optimization, ant lion optimizer, bird swarm algorithm, and whale optimization algorithm are applied and compared to decide the suitable technique. In querying phase, the same features are extracted from the query image and comparing these features to the extracted features from the database images through the matching measures. Three distance measures have been used and compared for the matching process: histogram intersection, Euclidean distance, and cosine distance. Based on these distances and the feature weights, the class of the query image is identified using the weighted feature voting mechanism. Finally, the related images are retrieved based on the feature that returned the maximum number of images in the identified class. For the validation and performance evaluations of the proposed system; Wang, Caltech101, and UW datasets are used. The experimental results show that the proposed method achieves improved precision in comparison with existing methods with 93% for Wang database, 92% for UW database, and 94% for Caltech101 database.