Scale Space Based Object-Oriented Shadow Detection and Removal from Urban High-Resolution Remote Sensing Images

This task mostly center to get the high resolution color remote sensing image, and furthermore attempted to eliminate the concealed district in the both metropolitan and country region. A portion of the current activities are included to recognize the concealed district and afterward dispense with that area, yet it has a few disadvantages. The discovery of the edges will be influenced generally by the utilization of the outside boundaries. The edge location cycle can be more useful in the recognition of the articles with the goal that the items can be utilized for additional handling. In this cycle we have execute the Scale Space algorithm is utilized to identify the shadow area and concentrate the component from the shadow district. Scale Space is least complex in area base image segmentation strategies. The idea of Scale Space algorithm is check the neighboring pixels of the underlying seed focuses. At that point decide if those neighboring pixels are added to the seed focuses or not. In the Scale Space limit algorithm Pixels are set in the area dependent on their properties or the properties of the close by pixel esteems. At that point the pixel containing the comparable properties is gathered and afterward the enormous quantities of pixels are circulated all through the image

MANET Congestion Control Mechanism - Challenges and Survey

The transport layer plays a crucial role in the Mobile Adhoc Network (MANET) protocol stack by controlling traffic flow, managing congestion, and enabling end-to-end delivery. With the help of congestion control mechanisms, numerous protocols are formed to enhance MANET performance. This paper focuses on a thorough analysis of the challenges the MANET protocol stack is facing as a result of congestion control issues such high overload, long delays, and increased packet loss. Finally, note that in order to increase MANET performance, research needs to concentrate on specific congestion control mechanisms

Detect and Evaluate Visual Pollution on Street Imagery Taken of a Moving Vehicle: Evaluating Street Imagery from Moving Vehicles to Identify Visual Pollution

Visual pollution is a growing problem in urban areas. It is important for environmental management to identify, formalize, measure and evaluate visual pollution. This paper presents a study on the development of an automated system for visual pollution classification using street images taken from a moving vehicle. The proposed system uses convolutional neural networks to classify different types of visual pollutants such as graffiti, faded signage, potholes, litter, construction zones, broken signage, poor street lighting, poor billboards, road sand, sidewalk clutter, and unmaintained facades.In this study, we utilized a large dataset of raw sensor camera inputs gathered from a fleet of multiple vehicles in a specific geographical area. Our aim was to develop convolutional neural networks that simulate human learning to classify visual pollutants from these images. The successful implementation of this system would be a significant contribution to the development of urban planning and the strengthening of communities worldwide. Additionally, it could lead to the creation of a "visual pollution score/index" for urban areas, which could serve as a new metric for urban environmental management. Our findings, which we present in this paper, will be a valuable addition to the academic community and the field of computer vision for environmental management applications

Shadow Detection and Removal using Artificial Neural Network

Shadow detection and removal is an important task when dealing with colour images. Shadows are generated by a local and relative absence of light or a shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a preprocessing task in computer vision. This thesis work proposes a simple method to detect and remove shadow from a single RGB image using artificial neural network. A shadow detection method is selected based on the phenomena of back propagation algorithm. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. The shadow removal is done by multiplying the shadow region by a constant

Shadow Detection and Removal in Single-Image Using Paired Regions

A shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. The shadows are sometimes helpful for providing useful information about objects, and sometimes it degrade the quality of images or it may affect the information provide by them. Thus for the correct image interpretation it is important to detect shadow and restore the information. However, shadow causes problems in computer vision applications, such as segmentation, object detection and object counting. That’s why shadow detection and removal is a pre-processing task in many computer vision applications. So we propose a simple method to detect and remove shadows from a single image. The proposed method begins by selecting shadow image and by pre-processing method we focus only on shadow part. In image classification we distinguish between shadow and non shadow pixels. So that we able to label shadow and non shadow regions of the image. Once shadow is detected that detection results are later refined by image matting, and the shadow- free image is recovered by removing shadow region by non shadow region. Examination of a number of examples indicates that this method yields a significant improvement over previous methods

Cross-Layer Optimization on Different Data Rates for Efficient Performance in Wireless Sensor Network

The traditional protocols used in wireless sensor networks adhere to stringent layering approaches, which decreases the performance of the quality of service (Quality of Service) metrics. As per specifications 802.15.4, wireless sensor networks are inexpensive and energy efficient. It is essential for evaluating the performance of WSNs. Researchers have looked into the fundamental aspects of a single physical layer and the medium access control (MAC) layer protocol using methodologies calculated using several mathematical models or experimental approaches, respectively. In this research, we offer an improved cross-layer analytical model that utilises a thorough combining and interacting of a Markov chain model of the MAC layer's propagation with a model of the PHY layer's propagation. This combination and interaction are described in detail. Various Quality of Service (quality of service) statistics are presented and evaluated, and a cross-layer effectiveness degradation study is conducted under different inputs of multi-parameter vectors. Other parameters, such as Average Wait Time, Reliability, Failure Probability, and Throughput, have been estimated from the simulation results and contrasted with standardised models. The cross-layer model provides a more thorough performance study with various cross-layer parameter sets, some of which comprise distance, power transmission, and offered loads, among other things

Integrating Cloud Technology and Artificial Intelligence for Enhanced Real Estate Investment Trusts (REITS) and Crowdfunding Synergy

The integration of Cloud technology (CT) and Artificial Intelligence (AI) presents significant potential benefits for the real estate industry, particularly in the areas of REITs and crowdfunding. By providing greater transparency, reducing costs, and increasing liquidity, the cloud can make real estate investments more accessible and appealing to individuals. Moreover, AI can assist with decision-making processes and data analysis, leading to better investment strategies and higher returns. To measure individual perspectives on this integration in India, a survey questionnaire involving 236 individuals will be designed using a Likert scale to measure attitudes and opinions on this topic. This will be followed by data collection through a convenient sampling method and analysis using the SPSS tool. The survey aims to identify the level of awareness and adoption of integrating cloud and AI among real estate professionals and investors, intending to enhance REITs and Crowdfunding Synergy (CS). The findings of the study revealed that the use of CT can enhance transparency in real estate investments, making it easier for investors to understand how their money is being used and what returns they can expect