Study Of Various Vehicle Detection Techniques –A Review
ABSTRACT- The Vehicle detection is method to detect the vehicles in the image or video data. The vehicle detection is the branch of the object detection, where the vehicle is the primary object. The vehicle detection can be performed on various kinds of the vehicle data obtained from the horizontal, aerial, parking or road surveillance cameras. In this paper, the vehicle detection and classification method has been proposed by using the hybrid deep neural network over the image data and video obtained from the aerial and satellite images to determine the vehicle density. The non-negative matrix factorization (NMF) will be utilized for the feature extraction and compression for the purpose of vehicle detection and classification. The 2nd level feature compression will be performed to create the quick response vehicle detection and classification system. The model will be programmed to detect the maximum vehicles visible as full or partial object in the image. The vehicle density reporting, vehicle movement reporting and upside & downside reporting for highways will be performed to achieve the goal of the vehicle detection and classification. The aim of this review is to produce the robust algorithm to detect and analyze the vehicle features like whether the vehicle is heavy or light in the images and videos with higher accuracy and precision.
Published by: Geetika Garg, Amardeep Kaur
Author: Geetika Garg
Paper ID: V2I3-1174
Paper Status: published
Published: June 15, 2016
A Novel Hybrid Classification Technique for Blur Detection
Image, audio and video are the popular entertainment and communication services of internet. Sometime they suffer from many problems, Blur is one of them. Blur is a factor that breakdown the status of image. In this paper, we are going to perform comparison of four different Blur Detection classifiers. This paper introduces our proposed technique (Hybrid Classifier). To verify the accuracy of hybrid Classifier we collect 1000 images from internet and hence results are predicted. From result and discussion, it is clear that Proposed Classifier give 96% accuracy which is 10% more than existing Classifier (SVM).
Published by: Indu Kharb, Abhishek Sharma
Author: Indu Kharb
Paper ID: V2I3-1173
Paper Status: published
Published: June 14, 2016
Document Image Binarization Technique for Degraded Document Images by using Morphological Operators
Segmentation of badly degraded document images is done for discriminating a text from background images but it is a very challenging task. So, to make a robust document images, till now many binarization techniques are used. But in existing binarization techniques thresholding and filtering is an unsolved problem. In the existing method, edge based segmentation can be done and Canny edge detector used. In our proposed technique, Image Binarzation for degraded document images has being use Region based segmentation. Firstly, an RGB image covert into gray image then image filtering can be done on the basis of Wiener Filtering and Gaussian filter. Secondly, morphological operators use to discriminate foreground from background. Then Otsu and Sauvola’s thresholding did for better results. Finally, proposed method results compare with the method used in DIBCO 2011 dataset. The evaluation based on few parameters like F-measure, PSNR, DRD and MPM. Keywords: Filtering, Morphological operators, and thresholding.
Published by: Divya Jyoti, Bodh Raj, Kapil Kapoor, Arun Sharma
Author: Divya Jyoti
Paper ID: V2I3-1172
Paper Status: published
Published: June 14, 2016
Implementation of Value Stream Mapping Methodology in Bearing Industry
Lean manufacturing is best way for the reduction of non value added cost, lean word defined by Crafcik in the book of “the machine change the world” in 1988. Lean means thinner and thinner as well as possible to reduce the cost. Lean manufacturing gives the benefit without investment with some modification. In this paper study of the bearing industry at Ahmadabad, Gujarat, for reduction of product lead- time and fulfill the customer demand. This industry is not fulfilling the customer demand and that cause increase the production lead time. In this study to selecting the medium size spherical roller bearing and apply the value stream mapping techniques for detecting the flow of non value added cost with the help of current state map and after analyze it and then prepare a future state map for the proposed implementation. And after apply the entire proposed lean tool and derive the benefits and fulfill the customer demand with reducing lead time.
Published by: Mehul Mayatra, Mr. N.D. Chauhan, Mr. Parthiv Trivedi, Mr. M.N. Qureshi
Author: Mehul Mayatra
Paper ID: V2I3-1171
Paper Status: published
Published: June 13, 2016
Optimized Healthcare Data Management and Critical Handling using Smart Data Categorization Method
ABSTRACT—The cloud based healthcare models are coming to the emergence very quickly and growing their roots across the globe for the empowering of the active healthcare services. The wearable body sensors are utilized to track the health of the patient when they are out of the healthcare premises. Also the telemedicine and remote healthcare monitoring applications has empowered the healthcare systems to grow their roots into the remote areas of the countries, where it becomes the very tough task to provide t he healthcare services or setup the hospitals, dispensaries, etc. The telemedicine practices empower the doctors to remotely monitor the health of the patients and prescribe the best medicines or the precautionary practices. But such healthcare applications suffers from the many performance based issues such as critical data handling, slow data delivery, etc. The healthcare specific network data classification and flow prioritization methods can be utilized to mitigate the healthcare network problems by decongesting the healthcare networks from the heavy loads by smartly optimizing the data outcome on the dominating controller nodes to optimize the healthcare data inflow volumes. The proposed model is expected to solve the problems associated with the existing systems designed for healthcare data management.
Published by: Divisha Poonia, Satvir Bajwa
Author: Divisha Poonia
Paper ID: V2I3-1169
Paper Status: published
Published: June 9, 2016
Bandwidth-Aware Stochastic Uplink Scheduling in WIMAX
Abstract—The bandwidth allocation in the WiMAX networking is the primary process which is utilized for the maximum accommodation of the wireless users. The WiMAX link scheduling plays the significant role in managing the resources efficiently and in serving the internet services to the maximum number of users. In this study, the study of various scheduling strategies in wireless mesh network will be carried out. We will focus mainly on the rtps class as it is the most challenging one. A detailed study of HAS and SADP algorithm will be carried out, analyzing its performance for all the service classes. We will then modify the existing algorithm in order to overcome its disadvantages and further improve its performance. The modified algorithm will be implemented in NS2 and simulations will be done. The results obtained from the research will be studied and compared with the previous results and conclusions will be drawn.
Published by: Komaljot Kaur, Amitabh Sharma
Author: Komaljot Kaur
Paper ID: V2I3-1167
Paper Status: published
Published: June 9, 2016
