Author
G. Harshitha
Bio: G. Harshitha is an academic researcher from Sathyabama University. The author has contributed to research in topics: Impulse noise & Aerodynamic force. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
Papers
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01 Mar 2017TL;DR: Different algorithms for exclusion of different impulse noise and their performance over different noise circumstances are discussed, finding most proficient algorithms in terms of edge preservation and noise suppression to restore the original image to the finest potential level.
Abstract: Image is a potential standard to convey information. Impulse noise degrades the image during image transmission and acquisition. The most important task of image processing is noise filtering and image enhancement. De-noising the degraded image is an important research region in Image Processing. Many de-noising proposals established which used standard median filter or its alterations to suppress noise. In this analysis paper, different algorithms for exclusion of different impulse noise and their performance over different noise circumstances are discussed. Each and every scheme has its benefits and drawbacks. The relative study assist in detection of most proficient algorithms in terms of edge preservation and noise suppression to restore the original image to the finest potential level. Most of the systems are suitable for some particular noise models, and does not perform effectively on other kind of noise models.
1 citations
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14 Jun 2023
TL;DR: In this article , the authors proposed a self-driving car detection system, which uses a combination of artificial intelligence, algorithms, and sensors to navigate roads and make decisions without human intervention.
Abstract: Vehicle detection systems play a crucial role in preventing accidents by providing real-time information about the location and movement of vehicles on the road. Monitoring speed is also essential for safety purposes as it enables the identification of vehicles that are exceeding speed limits or driving recklessly under current road conditions. The emergence of self-driving cars, which use a combination of artificial intelligence, algorithms, and sensors to navigate roads and make decisions without human intervention, is a rapidly advancing technology. Despite its complexity, this technology offers numerous advantages such as increased safety and reduced traffic congestion. One of the most significant benefits of self-driving cars is the potential to reduce accidents caused by human error, which is the leading cause of traffic accidents. By eliminating the need for human drivers, the risk of accidents can be significantly reduced. The frequency of car accidents is alarming, with one occurring every minute according to the National Highway Traffic Safety Administration (NHTSA). Auto insurance industry statistics indicate that every driver is likely to encounter at least four car accidents in their lifetime. Inexperienced drivers, particularly those aged 16 to 20, are at a higher risk of being involved in accidents. Annually, approximately 37,000 people die in car accidents, with one fatal accident occurring every 16 minutes. Among these, nearly 8,000 deaths involve drivers aged between 16 and 20 years old. Shockingly, more than 1,600 children under the age of 15 lose their lives in car accidents each year. To combat this alarming trend, measures are being taken to promote safe driving and reduce the frequency of accidents. Additionally, self-driving cars have the potential to improve traffic flow and minimize travel time as the vehicles can communicate with each other to optimize traffic flow and avoid congestion.
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TL;DR: In this article , the authors present a computational study on the rocket payload fairing using CATIA V5 software for modeling and analysis, and analyze the structural and aerodynamic performance of the fairing under specific boundary conditions.
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TL;DR: A novel idea of using median deviation parameter in estimating the noise in the images is proposed and successfully applied to gray level images and is found to be better than earlier methods and also robust in terms of preserving the contrast and fine details of the image even at high noise densities.
Abstract: A novel idea of using median deviation parameter in estimating the noise in the images is proposed and successfully applied to gray level images. The median filter which is very popular in removing the salt and pepper noise from the images has undergone many changes in recent past. To this modified median filter the concept of mean deviation is added and used in estimating and removing the noise. The proposed method is implemented by developing a Graphical User Interface in MATLAB and also implemented using the Spartan 3E Filed Programmable Device. The results are found to be better than earlier methods and also robust in terms of preserving the contrast and fine details of the image even at high noise densities.
2 citations