Controlling the traffic in the metropolitan cities and the identification of the owner of a particular vehicle becomes a difficult task throughout the world. Though traffic police, interceptor units and CCTV surveillance system etc. are in place for implementation of road safety rules, if the vehicle violates the traffic rules then the system must identify the vehicle details as well as the owner of the vehicle but it is not easy, if the vehicle displacement is very high. In all these cases getting a glimpse of vehicle number plate of the miscreant helps in reducing the effort and time to track him down. Automatic License Plate Recognition (ALPR) is one of the solutions to this problem. In this paper the outdoor localization for crime investigation ALPR with Pressure-based localization is proposed to find out the vehicles. Camera in place will capture the image of the Number plate automatically using the Raspberry-Pi 4.The Pressure-based localization is implemented along with the Automatic license Plate Recognition to improve the accuracy and tracing the vehicles.
Early diagnosis of a brain tumor may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. This manuscript presented the performance analysis of clustered based and fusion-based segmentation techniques intended to detect the tumor from human brain MRI images in an efficient manner. Four primary steps are involved in this work such as pre-processing, clustering, segmentation, and fusion techniques. The main clustering methods such as K-means and fuzzy c-means (FCM) were first applied to the pre-processed MRI images, and then, the clustered images were segmented directly using the active contour segmentation techniques such as chan-vese (C-V) and level set method (LSM). Then in the next step, the clustered images were fused by using the non-sub sampled contour transform (NSCT) and convolution neural network (CNN) fusion methods, and then, the fused images were segmented by using the C-V and LSM segmentation methods again. The results of both clustered based and fusion-based segmentation in terms of structural similarity index measure (SSIM), dice coefficient (DC), computational time, sensitivity, precision, and segmentation accuracy revealed that CNN fusion-based C-V segmentation performs better than without fusion (clustered based or direct segmentation) to detect the tumors from the MRI images. The results indicate that C-V performs better with CNN as compared with the LSM. Finally, the fusion-based segmentation is an efficient approach to detect the tumor from the MRI images with minimal information loss and high segmentation accuracy over the clustered based segmentation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.