Shashikant Lokhande

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In this paper, a technique for the identification of the unwanted lane departure of a traveling vehicle on a road is proposed. A piecewise linear stretching function (PLSF) is used to improve the contrast level of the region of interest (ROI). Lane markings on the road are detected by dividing the ROI into two subregions and applying the Hough transform in(More)
Due to progress in the vehicular technology, vehicles have gradually become a popular form of transportation in people's daily life. The stability and the performance of the vehicles has been the subject of much attraction. Road vehicle engines are controlled by engine management system (EMS) in which fault identification & diagnosis is the vital part.(More)
The gigantic growth of the internet communication technology has illustrated its value and benefits to private businesses, government organizations, worldwide professionals, academic institutes and individuals over the past few years. The size and range of computing devices connected to the internet, substantially increased because of IPv6 and offers the(More)
In this paper, we propose a novel framework for pedestrian detection based on edgelet features and k-means classifier. Initially, edges of the pedestrian objects are extracted and edge map is prepared. Edgelet features are then used for detecting the pedestrians with diverse positions and appearances based on template matching technique. Classification is(More)
In recent years, pedestrian detection (PD) plays a vital role in a variety of applications such as security cameras, automotive control and so forth. These applications require two essential features, i.e. high speed performance and high accuracy. Firstly, the accuracy is determined by how the image features are described. The image feature description must(More)
Real time internet traffic classification is imperative for service discrimination, network security and network monitoring. Classification of traffic depends on initial first few network packets of full flows of captured IP traffic. Practically, the real world framework situation expects correct conclusion of classification well before a flow has ended(More)
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