Preventing Cyber Attacks using Artificial Intelligence
Exploring Advantages and Challenges of Chatbot Integration in Human-Robot Interaction
Counterfeit Currency Detection using Machine Learning
Artificial Intelligence use in Lung Cancer Screening to Improve Patient Recovery
An Analysis of Various Crypto Coins and their Suitability for Real-Time Applications
Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
The internet technology is increasing rapidly in the society and industry. In information technology, the people are searching information based on text and images. There are many techniques to extract the required information from the raw data which is in the form of text and images. There are many information searching engines such as Google which mostly use the text-based retrieval techniques. The text based retrieval is used for getting the text information only. But if you want the information in text and image form, then only text based information is not efficient. In the recent years, content based image retrieval techniques have been proposed to search the text and image collectively. Due to its importance in information technology, we have discussed all aspects regarding Content Based Image Retrieval in detail. The objective of this paper is to study the different existing Content Based Image Retrieval techniques and their applications. Our findings are based on reviews of the relevant literature survey which will be very useful for a researcher who is new in Content Based Image Retrieval techniques. We also have presented the content based image retrieval system which is developed at our site. The system is based on similarity measurement of color histograms of image. We have used our own image database of college annual gathering and Engineering Today 2011 technical event for testing the system. We found that the system works properly and gives excellent results. The algorithm is suggested by extending the same experiment which can help for making album.
In this paper to improving driver assistance systems, but it is difficult in natural driving environments due to non uniform and highly variable illumination and large head movements [1]. Various face detection techniques have been proposed over the past decade. Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper we propose to enlarge the features search space by enriching it with more types of features. With an additional seven new feature types, we show how Genetic Algorithm (GA) can be used, within the Adaboost framework, to find sets of features which can provide better classifiers with a shorter training time. The technique is referred as GABoost for our face detection system. The GA carries out an evolutionary search over possible features search space which results in a higher number of feature types and sets selected in lesser time. Experiments on a set of images from BioID database proved that by using GA to search on large number of feature types and sets, GA Boost is able to obtain cascade of boosted classifiers for a face detection system that can give higher detection rates, lower false positive rates and less training time but gives higher detection rates in natural Deriving environment.
This paper proposes Automatic region detection of facial features in an image that can be important stage for various facial image manipulation works, such as face recognition, facial expression recognition, 3D face modeling and facial features tracking. Region detection of facial features like eye, pupil, mouth, nose, nostrils, lip corners, eye corners etc., with different facial image with neutral region selection and illumination is a challenging task. In this paper, we presented different methods for fully automatic region detection of facial features. Object detector is used along with haar-like cascaded features in order to detect face, eyes and nose. Novel techniques using the basic concepts of facial geometry are proposed to locate the mouth position, nose position and eyes position. The estimation of detection region for features like eye, nose and mouth enhanced the detection accuracy effectively. An algorithm, using the H-plane of the HSV color space is proposed for detecting eye pupil from the eye detected region. Proposed algorithm is tested over 100 frontal face images with two different facial expressions (neutral face and smiling face).
With an unfortunate hardship in preserving the significant image content of interest, from an often contamination of noise due to evincing facts like internal element imperfections, scarce of illumination and digitization intrinsic to sensors (CCD Cameras),in addition to the environmental conditions and alignment which are extrinsic in wide variety of applications including satellite television, magnetic resonance imaging, computer tomography as well as in areas of research and technology such as geographical information systems and astronomy have lead its wings to be opened towards an evergreen application of essence in image processing ,i.e., image denoising. Image denoising is primary task prior to any high level image processing operation, with an underlying goal to remove noise while preserving edges, is still hard striking problem, in a solution to which several algorithms with their specific assumptions, advantages and limitations have been published and due to their inherent averaging leading to the loss of significant image features of interest in high frequency image denoising. In this paper we discuss the importance of nearly shift invariant, directional selective, dyadic decomposition tree based dual tree complex wavelet transform (DT-CWT) and an intelligent filter module (IFM) which can make the decision of selecting the filter type to denoise the image, to remove the resulting blur based on noise type and produces enthusiastic results in terms of psycho visual quality and performance metrics than those produced by previous tools and techniques.
Dynamic slicing technique is a proposed technique for slicing the architectural model. The presence of related information in diverse model parts makes dynamic slicing of unified modeling language (UML). In most cases UML model need to be converted into intermediate representation. These intermediate representation forms a data structure to be manipulated by the algorithm of specific goals. Various intermediate representation and associated algorithms produce results of slicing with its salient effectiveness. Slicing technique is also used to produce the impact analysis among the various model elements in different architecture diagram. Specifying the slicing criteria is another aspect which can be observable through the previous works. This paper summarizes all the previous works with their results and methodology.
