Gesture Recognition Based Mouse Events Rachit Puri,Samsung Research India, India
ABSTRACT
This paper presents the maneuver of mouse pointer and performs various mouse operations such as left click, right click, double click, and drag etc using gestures recognition technique. Recognizing gestures is a complex task which involves many aspects such as motionmodeling, motion analysis, pattern recognition and machine learning. Keeping all the essential factors in mind a system has been created which recognizes the movementof fingers and various patterns formed by them to provoke an event. Color caps have been used for fingers to distinguish it from the background color such as skincolor. Thus recognizing the particular gestures, mouse event is performed accordingly. The application has beencreated on MATLAB environment with operating system as windows 7.
Automatic theft security system (smart surveillance camera) Veena G.S,Chandrika Prasad and Khaleel, M.S.R.I.T, India
ABSTRACT
The proposed work aims to create a smart application camera,with the intention of eliminating the need for a human presence to detect any unwanted sinister activites,such as theft in this case.spread among the campus,are certain valuable biometric identification systems at arbitrary locations.The application monitor these system(hereafter referred to as "object") using our smart camera system based on an OpenCV platform. By using OpenCV Hear Training,employing the Viola-Jones algorithm implementation in OpenCV,we teach the machine to identify the object in environmental conditions.An added feature of face recongnition is based on Principal Component Analysis(PCA) to generate eligen faces and test image are verified by using distance based algorithm against the eigenfaces,like euclidean distance algorithm or Mahalanobis algorithm.If the object is misplaced,or an unauthorized user is in the extreme vicinity of the object,an alarm signal is raised.
Video Frame Summarization for Object Mining Swati Jagtap,Institute of Computer Technology Pune, India
ABSTRACT
With advances in techniques, technologies and devices, image mining has become one of the challenging areas with numerous applications in security and surveillance. Video object is the container format in a video media. Mining of these objects can reveal the useful information. This information is useful to detect security related threats. The system proposes video object content mining to evaluate the security related threats. The proposed system is based on summarization of the video frame. Video frame summarization aims at reducing the data that will be examined in order to extract useful information in a video. Summarized information can be used to detect the object in a video.
Variational Monte-Carlo Approach for Articulated Object Tracking Kartik Dwivedi1, Harish Bhaskar2,1 Indian Institute of Technology (IIT),India,2 Khalifa University, U.A.E.
ABSTRACT
In this paper, we describe a novel variational MonteCarlo approach for modelling and tracking body parts of articulated objects. An articulated object (human target) is represented as a dynamic Markov network of the different constituent parts. The proposed approach combines local information of individual body parts and other spatial constraints influenced by neighbouring parts. The movement of the relative parts of the articulated body is modelled with local information of displacements from the Markov network and the global information from other neighbouring parts. We explore the effect of certain model parameters (including the number of parts tracked; number of Monte-Carlo cycles, etc.) on system accuracy and show that our variational Monte Carlo approach achieves better efficiency and effectiveness compared to other methods on a number of realtime video datasets containing single targets.
Detection of Power-Lines in Complex Natural Surroundings Rajeev M Bhujade, Adithya , Hrishikesh , Balamurali, Tata Consultancy Services,India
ABSTRACT
Power transmission line inspection using Unmanned Aerial Vehicles (UAV) is taking off as an exciting solution due to advances in sensors and flight technology. Extracting power-lines from aerial images, taken from the UAV, having complex natural surroundings is a critical task in the above problem. In this paper we propose an approach for suppressing natural surrounding that leads to power line detection. The results of applying our method on real-life video frames taken from a UAV demonstrate that our approach is very effective. We believe that our approach can be easily used for line detection in any other real outdoor video as well.
Hand Gesture Recognition in Complex Background Richa Sharma, Dk Vishwakarma, Delhi Technological University,India
ABSTRACT
Hand Gesture recognition system is a process involving classifying the given gesture of the hand portion. This paper presents a technique for the recognition of hand gesture from the 11 different static gestures taken from NUS hand posture dataset. Hand gesture detection in complex background is seen as a challenging task. The purpose of this paper is to study and develop a method for the efficient detection and classification of hand gestures in the complex background. Skin similarity measure is used to detect the hand in complex background hand gesture image. The whole of the image is divided into two classes one is hand and other is background. Subsequently shape and texture features are extracted from the gestures which form the basis of recognition of the hand gesture.
Multi-Level Graphical Authentication Technique to Strengthen The Use of Mobile Banking Mrs.Hemangi Kulkarni1, SonalAhuja2, 1Computer Department IOIT Station Road, India
2Institute of Management Studies (CD&R),India
ABSTRACT
Mobile Application Industry is drastically growing industry which provides on-demand software that a user can readily use as a service. These applications are used worldwide by intended customer, and thus it is necessary to have strong password authentication, specially for applications which carry sensitive-data for example Banking Applications. Current Mobile banking login requires you to enter PIN authentication, however results from various early research studies have found that there are usability concerns while using PINs. Thus this paper describes the design of Multi-Level mobile authentication technique combination of Text Password and Graphical Password along with architecture, sequence diagrams, algorithms ,typical user interfaces and results.
A Digital Color Image Watermarking System Using Blind Source Separation Sangeeta D. Jadhav, Army Institute of Technology,India
ABSTRACT
An attempt is made to implement a digital color image-adaptive watermarking scheme in spatial domain and hybrid domain i.e host image in wavelet domain and watermark in spatial domain. Blind Source Separation (BSS) is used to extract the watermark The novelty of the presented scheme lies in determining the mixing matrix for BSS model using BFGS (Broyden-Fletcher-Goldfarb-Shanno) optimization technique. This method is based on the smooth and textured portions of the image. Texture analysis is carried based on energy content of the image (using GLCM) which makes the method image adaptive to embed color watermark. The performance evaluation is carried for hybrid domain of various color spaces like YIQ, HSI and YCbCr and the feasibility of optimization algorithm for finding mixing matrix is also checked for these color spaces. Three ICA (Independent Component Analysis)/BSS algorithms are used in extraction procedure ,through which the watermark can be retrieved efficiently . An effort is taken to find out the best suited color space to embed the watermark which satisfies the condition of imperceptibility and robustness against various attacks.
Low Power Reconfigurable FIR Filter Based on Window Techniques for On Chip Network Rainy Chaplot, Anurag Paliwal, Geetanjali Institute of Technical Studies,India
ABSTRACT
In this brief, we have designed a Reconfigurable Digital Low Pass FIR Filter System On Chip design. Analysis of performance of various filter orders 10, 20 to 120 are demonstrated for different window techniques namely Rectangular, Hanning, Hamming, Bartlett and Kaiser Window Function, with sampling frequency 48 KHz and with cut off frequency 10.8 KHz. It is shown that filter design by using Kaiser window function and Spartan 6 family of FPGA is best because resource utilization are less and consequently the power consumption is minimum for this combination. Xilinx Spartan 6 family synthesis result of the designed filter using Kaiser Window Function shows 5-10% power reduction over other window function. We have concluded the calculated parameters i.e. Power Consumption (Static and Dynamic), Delay, Resources Utilized by taking different window function as platform on the various FPGA families of Xilinx, so as to exploit respective family according to application, specification as well as constraints what is the best chosen parameter and family according to the analysis made. The coefficient of FIR filter is generated using MatLab FDA (Filter Design Analysis) tool box. Based on the coefficients, FIR filter is being programmed in VHDL, and synthesized and simulated on Xilinx design suite 14.1 ISE.
MORTAL: Multiple Object Realtime Tracking And Learning Praveen Palanisamy, VIT university,India
ABSTRACT
This paper proposes a real-time system that can track and learn multiple independent objects. The Multiple Objects Tracking And Learning (MORTAL) system uses a shared memory multiprocessing to parallelize object tracking. MORTAL takes less than 40 milliseconds to track up to 3 arbitrary objects simultaneously at a resolution of 320x240 on Intel's dual core ivy-bridge processor. It achieves over 11 Frames per second in simultaneously tracking four independent objects of various size, shape and appearance. The results show the ability of the proposed system to track multiple independent objects efficiently at different scales, poses, lighting conditions, occlusions and also when the objects of interest move at a fast pace.
Human beings are prone to error in herb species recognition.Thereby in case of preparation of herbal products by using these herbs as key components,there should be an utmost concern in correctly recognizing the species of herb.This paper shows image processing techniques for extraction of shape,vein and texture feature value for two species of herb leaves using beagle board XM and these different feature value extracted can be used as inputs for further classification stage.
Evaluation of BPNN and KNN classifiers for lip reading Sunil Sudam Morade, Suprava Patnaik,Xavier Institute of Engineering,India
ABSTRACT
In lip reading, selection of features and classifier plays crucial roles. Goal of this work is to compare the common feature extraction modules and classifiers. Two well-known image transformed models, namely Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are studied. A competent feature extraction module cascaded with a robust classifier can result a novel automatic lip reading system. We have compared performance of Back Propagation Neural Network (BPNN) algorithm with that of K-Nearest Neighborhood (KNN) algorithm. Both being from class of artificial intelligence needs training. Hence we have also examined the computational complexity associated with the training phase of both classifiers. The CUAVE database is used for experimentation and performance comparison. It is observed that BPNN is a better classifier than KNN.
Analysis of Various Vedic Techniques for Multiplication Poonam Undre, Harjeet kaur, Indira College of Engineering and Management,India
ABSTRACT
This paper proposed the analysis of various vedic techniques typically used for multiplication .By using these techniques different types of multiplications can be done quickly . In other words these calculations can be done mentally within fraction of seconds if user practices it. As a result performance of system also improved. Vedic Mathematics is the ancient system of mathematics which has a unique technique of calculations based on 16 Sutras and 13 Up-sutras .Four different types of techniques are described in this paper.All these techniques are based on vedic sutras and up sutras. These techniques are Urdhva Triyagbhyam, Nikhilam Sutra, Ekanyunena purvena, Antyayor Dasakepi.
Speed-Up Improvement using Parallel Approach in Image Steganography Jyothi Upadhya K, U Dinesh Acharya ,Hemalatha S, MIT, India
ABSTRACT
This paper presents a parallel approach to improve the time complexity problem associated with sequential algorithms. An image steganography algorithm in transform domain is considered for implementation. Image steganography is a technique to hide secret message in an image. With the parallel implementation, large message can be hidden in large image since it does not take much processing time. It is implemented on GPU systems. Parallel programming is done using OpenCL in CUDA cores from NVIDIA. The speed-up improvement obtained is very good with reasonably good output signal quality, when large amount of data is processed.
Data Hiding in Audio Signals using Wavelet Transform with Enhanced Security Deepthi S, Renuka A, Hemalatha S, Manipal Institute of Technology,India
ABSTRACT
Rapid increase in data transmission over internet results in emphasis on information security. Audio steganography is used for secure transmission of secret data with audio signal as the carrier. In the proposed method, cover audio file is transformed from space domain to wavelet domain using lifting scheme, leading to secure data hiding. Text message is encrypted using dynamic encryption algorithm. Cipher text is then hidden in wavelet coefficients of cover audio signal. Signal to Noise Ratio (SNR) and Squared Pearson Correlation Coefficient (SPCC) values are computed to judge the quality of the stego audio signal. Results show that stego audio signal is perceptually indistinguishable from the cover audio signal. Stego audio signal is robust even in presence of external noise. Proposed method provides secure and least error data extraction.
Face recognition using Principal Component Analysis with Median for Normalization on a Heterogeneous data set G. Shree Devi ,M. Munir Ahamed Rabbani,A. Jaya, B.S Abdur Rahman University,India
ABSTRACT
Recognizing Faces helps to name the various subjects present in the image. This work focuses on labeling faces on an image which includes faces of humans being of various age group (heterogeneous set ). Principal component analysis concentrates on finds the mean of the data set and subtracts the mean value from the data set with an intention to normalize that data. Normalization with respect to image is the removal of common features from the data set. This work brings in the novel idea of deploying the median another measure of central tendency for normalization rather than mean. The above work was implemented using matlab. Results show that Median is the best measure for normalization for a heterogeneous data set which gives raise to outliers.
Detecting of Occluded Boundaries by Optimal Curve Segment Method N. Nithya, Thiagarajar college of engineering,India
ABSTRACT
Occluded parts of an object, which result in the changes of object shapes, may greatly decreases the detectors accuracy of contour based objectdetection techniques. In this paper we tend to propose a shape recovery method toimprove the detection of target object contour with occlusion contours by utilizingobject shape models. Given an incomplete shape as instance, boosted by contourreconstruction method called Optimal curve segment method will give improveddetection results of classifier. By labeling segmented curve portion we get a requiredmissing curve which is not in continues curves. To acquire best matched curveportion to complete the shape with missing parts as well as possible.
Face Recognition Using Back Propagation Neural Networks V. Ramya, Saranathan College of Engineering,India
ABSTRACT
Face recognition from the images is challenging due to the wide variability of face appearances and the complexity of the image background. This paper proposes a novel approach for recognizing the human faces. The recognition is done by comparing the characteristics of the new face to that of known individuals. It has Face localization part, where mouth end point and eyeballs will be obtained. In feature Extraction, Distance between eyeballs and mouth end point will be calculated. The recognition is performed by Neural Network (NN) using Back Propagation networks. The recognition performance of the proposed method is tabulated based on the experiments performed on a number of images.
Video Segmentation for Moving Object Detection Using Local Change & Entropy based adaptive window Thresholding Anuradha.S.G1, K.Karibasappa2 and B.Eswar Reddy3, 1RYMEC, India, 2DSCE, India and 3JNTUA, India
ABSTRACT
Motion detection and object segmentation are an important research area of image-video processing and computer vision. The technique and mathematical modeling used to detect and segment region of interest (ROI) objects comprise the algorithmic modules of various high-level techniques in video analysis, object extraction, classification, and recognition. The detection of moving object is significant in many tasks, such as video surveillance & moving object tracking. The design of a video surveillance system is directed on involuntary identification of events of interest, especially on tracking and on classification of moving objects. An entropy based real-time adaptive non-parametric window thresholding algorithm for change detection is anticipated in this research. Based on the approximation of the value of scatter of sections of change in a difference image, a threshold of every image block is calculated discriminatively using entropy structure, then the global threshold is attained by averaging all thresholds for image blocks of the frame. The block threshold is calculated contrarily for regions of change and background. Investigational results show the proposed thresholding algorithm accomplishes well for change detection with high efficiency.
