Color Image Segmentation Using Fuzzy C-Regression Model

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms

A Forensic Scheme for Revealing Post-processed Region Duplication Forgery in Suspected Images

Recent researches have demonstrated that local interest points alone can be employed to detect region duplication forgery in image forensics. Authentic images may be abused by copy-move tool in Adobe Photoshop to fully contained duplicated regions such as objects with high primitives such as corners and edges. Corners and edges represent the internal structure of an object in the image which makes them have a discriminating property under geometric transformations such as scale and rotation operation. They can be localised using scale-invariant features transform (SIFT) algorithm. In this paper, we provide an image forgery detection technique by using local interest points. Local interest points can be exposed by extracting adaptive non-maximal suppression (ANMS) keypoints from dividing blocks in the segmented image to detect such corners of objects. We also demonstrate that ANMS keypoints can be effectively utilised to detect blurred and scaled forged regions. The ANMS features of the image are shown to exhibit the internal structure of copy moved region. We provide a new texture descriptor called local phase quantisation (LPQ) that is robust to image blurring and also to eliminate the false positives of duplicated regions. Experimental results show that our scheme has the ability to reveal region duplication forgeries under scaling, rotation and blur manipulation of JPEG images on MICC-F220 and CASIA v2 image datasets

COVID-19 Detection from Chest X-Ray images using Feature Fusion and Deep learning

Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient’s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%)

Characterising Land Cover Changes in the Niger Delta Caused by Oil Production

In recent decades, oil extraction activities have particularly affected land cover in the Niger Delta region, subsequently increasing or reducing the extent of certain land cover types. Where complete change has not occurred, the quality of the land cover may have still been affected and degraded. However, the extent to which oil activities have affected the landscape is not fully understood. This thesis presents an integrated multiscale land cover change characterisation using geospatial analyses to determine the impact of oil extraction activities on the land cover. Firstly, a spatiotemporal hotspot analysis of oil spills from 2007-2019 and oil facilities shows that the area around Omuko-Ahoada in the north-eastern and around Ijaw-South in the southern part of the study area are the most impacted by the oil extraction activities. Secondly, from analysis of the impact of soil hydrocarbon parameters (SHP) on the health of different types of vegetation at the leaf scale from field spectrometer data, the mangrove is the most impacted by total petroleum hydrocarbon (TPH) and soil toxicity by showing a decrease in chlorophyll content and low spectral reflectance. At the same time, the mango shows the most tolerance to TPH, while oil palm is the most tolerant to toxicity (EC50). Thirdly, from the analysis of the impact of the oil spill volume and time gap after the occurrence of oil spills on the health of dense, sparse and mangrove vegetation even many years after the occurrence of spills by way of normalised difference vegetation index (NDVI) show that the dense vegetation is only impacted at volumes 1000 barrels and sparse vegetation between 400 and 1000 barrels. However, the mangrove vegetation is not impacted at any volume. Additionally, the impact of oil spills was more visible within 90 days of the spill for sparse and mangrove vegetation than for dense vegetation, which can withstand the oil spill due to its size. Also, the result shows that the health condition of vegetation on spill sites is impacted by oil spills when compared with those on none spill sites for all vegetation types. Finally, land cover change detection at the landscape scale was performed using a Bayesian classifier from 1987-2016 and NDVI map. The results show that the oil extraction activities have affected the land cover, especially the vegetation, with many conversions from vegetation to non-vegetation and degradation occurring near oil extraction activities. The results from this thesis could help address the environmental problems in the Niger Delta, such as land pollution, degradation and land cover change, by prioritising programs such as oil spill cleans up or remediation and the restoration of the vegetation using some plants that have shown some resistance to the impact oil spills to ensure the sustainability of the natural environment in the Niger Delta

Characterising Land Cover Changes in the Niger Delta Caused by Oil Production

In recent decades, oil extraction activities have particularly affected land cover in the Niger Delta region, subsequently increasing or reducing the extent of certain land cover types. Where complete change has not occurred, the quality of the land cover may have still been affected and degraded. However, the extent to which oil activities have affected the landscape is not fully understood. This thesis presents an integrated multiscale land cover change characterisation using geospatial analyses to determine the impact of oil extraction activities on the land cover. Firstly, a spatiotemporal hotspot analysis of oil spills from 2007-2019 and oil facilities shows that the area around Omuko-Ahoada in the north-eastern and around Ijaw-South in the southern part of the study area are the most impacted by the oil extraction activities. Secondly, from analysis of the impact of soil hydrocarbon parameters (SHP) on the health of different types of vegetation at the leaf scale from field spectrometer data, the mangrove is the most impacted by total petroleum hydrocarbon (TPH) and soil toxicity by showing a decrease in chlorophyll content and low spectral reflectance. At the same time, the mango shows the most tolerance to TPH, while oil palm is the most tolerant to toxicity (EC50). Thirdly, from the analysis of the impact of the oil spill volume and time gap after the occurrence of oil spills on the health of dense, sparse and mangrove vegetation even many years after the occurrence of spills by way of normalised difference vegetation index (NDVI) show that the dense vegetation is only impacted at volumes 1000 barrels and sparse vegetation between 400 and 1000 barrels. However, the mangrove vegetation is not impacted at any volume. Additionally, the impact of oil spills was more visible within 90 days of the spill for sparse and mangrove vegetation than for dense vegetation, which can withstand the oil spill due to its size. Also, the result shows that the health condition of vegetation on spill sites is impacted by oil spills when compared with those on none spill sites for all vegetation types. Finally, land cover change detection at the landscape scale was performed using a Bayesian classifier from 1987-2016 and NDVI map. The results show that the oil extraction activities have affected the land cover, especially the vegetation, with many conversions from vegetation to non-vegetation and degradation occurring near oil extraction activities. The results from this thesis could help address the environmental problems in the Niger Delta, such as land pollution, degradation and land cover change, by prioritising programs such as oil spill cleans up or remediation and the restoration of the vegetation using some plants that have shown some resistance to the impact oil spills to ensure the sustainability of the natural environment in the Niger Delta