Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Steganalysis: Using the blind deconvolution to retrieve the hidden data(2011) Jidesh, P.; George, S.Steganography has gained a substantial attention due to its application in wide areas. Steganography as it literally mean is hiding the information (stego data) inside the data (communication data) so that the receiver can only extract the desired information from the data. Steganalysis is the reverse process of steganography in which the information about the original data is hardly available, from the received data the extractor needs to identify the original data. Since this belong to a class of inverse problems it is hard to find the approximate match of the original data from the received one. In most of the cases this will fall under the category of ill-posed problems. The stego-data that has been embedded into the communication data can be considered as linear bounded operator operating on the input data and the reverse process (the Steganalysis) can be thought like a deconvolution problem by which we can extract the original data. Here we are assuming the watermarking as a linear operation with a bounded linear operator K : X→Y where X and Y are spaces of Bounded Variation (BV). The forward problem (the Steganography) is a direct convolution and the reverse (backward) problem (steganalysis) is a de-convolution procedure. In this work we are embedding a Gaussian random variable array with zero mean and with a specific variance into the data and we show how the original data can be extracted using the regularization method. The results are shown to substantiate the ability of the method to perform steganalysis. © 2011 IEEE.Item Computer interaction based on voluntary ocular motility for the physically challenged(2013) Ram, S.; Kalwad, P.Ocular Motility is a promising mode of input for human computer interaction. However, we are yet to engineer an application that is cost-effective, robust, user-friendly and far reaching. In this paper we describe an eye-controlled application which will be beneficial to the physically challenged community. Even people who can move only their eyes can use this with ease. This works on principles of Image processing and translates the eye movements into mouse commands, thereby enabling the physically challenged to interact with a computer. A user friendly graphical user interface has been developed to demonstrate the merits of eye controlled input as a natural mode of human-machine interaction. © 2013 IEEE.Item Reflection removal in smart devices using a prior assisted independent components analysis(SPIE spie@spie.org, 2015) Kalwad, P.; Prakash, D.; Peddigari, V.; Srinivasa, P.When photographs are taken through a glass or any other semi-reflecting transparent surface, in museums, shops, aquariums etc., we encounter undesired reflection. Reflection Removal is an ill-posed problem and is caused by superposition of two layers namely the scene in front of camera and the scene behind the camera getting reflected because of the semi-reflective surface. Modern day hand held Smart Devices (smartphones, tablets, phablets, etc) are typically used for capturing scenes as they are equipped with good camera sensors and processing capabilities and we can expect image quality to be similar to a professional camera. In this direction, we propose a novel method to reduce reflection in images, which is an extension of Independent Component Analysis (ICA) approach, by making use of two cameras present - a back camera (capturing actual scene) and a front facing camera. When compared to the original ICA implementation, our method gives on an average of 10% improvement on the peak signal to noise ratio of the image. © 2015 SPIE-IS&T.Item Automatic reflection removal using reflective layer image information(Institute of Electrical and Electronics Engineers Inc., 2015) Prakash, D.; Kalwad, P.S.; Peddigari, V.; Srinivasa, P.This paper tries to address the problem of removing unwanted reflection layer from the image mixtures. These reflections may occur due to semi-reflective glass mediums. This demands separating the input image into the reflecting layer and the subject layer, also known as, background layer which is the actual scene itself. But decomposing a single input image into these two layers is a massively ill-posed problem with infinite combinations of decomposition. This type of problem falls under the category of blind source separation. There are ample classical separation approaches available in the literature which either requires multiple images or works on a single image with user assistance. In this paper we propose a method for separating the two layers from a single input image without any user or human intervention using some prior information about the reflective layer. © 2015 IEEE.Item Currency recognition system using image processing(Institute of Electrical and Electronics Engineers Inc., 2017) Abburu, V.; Gupta, S.; Rimitha, S.R.; Mulimani, M.; Koolagudi, S.G.In this paper, we propose a system for automated currency recognition using image processing techniques. The proposed method can be used for recognizing both the country or origin as well as the denomination or value of a given banknote. Only paper currencies have been considered. This method works by first identifying the country of origin using certain predefined areas of interest, and then extracting the denomination value using characteristics such as size, color, or text on the note, depending on how much the notes within the same country differ. We have considered 20 of the most traded currencies, as well as their denominations. Our system is able to accurately and quickly identify test notes. © 2017 IEEE.Item Smart parking - An integrated solution for an urban setting(Institute of Electrical and Electronics Engineers Inc., 2017) Dsouza, K.B.; Yousuff, S.Parking could become a nightmare on a busy day, in a city like Delhi (India), which has about 7.35 million cars, as per MORTH Barclays Research (2012). An average of seventeen minutes and considerable amount of fuel is wasted in an effort to find a parking spot every time. Additional stress is induced due to parking hassles starting from finding an empty parking spot to relocating the car later. We propose a system leveraging the latest technologies that will help motorists overcome their parking problems and at the same time, make managing a parking space easier and cost effective by automating the entire process right from pre-booking a parking slot to making the payment. Since most of the parking spaces are equipped with CCTV surveillance cameras, we intend to use them to detect the presence of cars and measure the availability of parking spots within a parking space using techniques like image processing and machine learning. In order to test the performance of the proposed system, a prototype of the system is built that mimics the working of an actual parking space excluding minute details. A prototype of the application is built that would aid the user in booking the slot and guide him/her back to the allocated parking slot. The proposed system is compared with the existing systems and the results show superiority of the proposed system in terms of parameters like reliability, scalability and installation cost. © 2017 IEEE.Item Damage identification and assessment using image processing on post-disaster satellite imagery(Institute of Electrical and Electronics Engineers Inc., 2017) Joshi, A.R.; Tarte, I.; Suresh, S.; Koolagudi, S.G.Natural disasters such as earthquakes and tsunamis often have a devastating effect on human life and cause noticeable damage to infrastructure. Active research has been ongoing to mitigate the impact of these catastrophes and preclude the economic losses. The existing methods that utilize pre-event and post-event images not only require the immediate and guaranteed availability of the appropriate data set but are also encumbered by manual mapping of the images, necessitating the indication of corresponding control points in the two images. This paper highlights the use of only post-event imagery in the absence of reference data to achieve a more timely delivery to produce damage maps as the output. This eliminates the need for manual georeferencing of images. Our method incorporates simple linear iterative clustering (SLIC) for segmenting the images into uniform superpixels and extraction of 62 features for each superpixel. We used various classifiers of which Random Forest classifier was found to give a comparatively high accuracy of 90.4% over others. To enumerate the accuracy of the method proposed, we used 1500 data regions of which 20% were used for testing, and 80% were used for training. The aerial images taken by GeoEye1 after the 2011 Christchurch earthquake and 2011 Japan earthquake and tsunami are utilized in this study to detect building damage. In the case of availability of ground truth, we compare the histograms of the pre- and post-imagery to quantify similarity as the SSD (Sum of Squared Distances) value and thus, our approach produces an assessment as an output map displaying the extent of damage in the area covered by each superpixel. We consider 6 levels of damage ranging from 1 to 6, where 1 signifies no damage, and 6, maximum damage. © 2017 IEEE.Item Gender Detection using Handwritten Signatures(Institute of Electrical and Electronics Engineers Inc., 2018) Mohit Reddy, J.; Guru Pradeep Reddy, T.; Mishra, S.; Mulimani, M.; Koolagudi, S.G.In this paper, a method is proposed which uses both Image Processing and Machine Learning techniques which detects the gender of a person using handwritten signature. A photograph of a handwritten signature is given as input to the model which then extracts different features like pen pressure, slant angle, count external and internal contours etc. The features extracted from multiple images in the dataset are used to train the model, which then predicts the output of a new input given to it. Our objective is to collect unbiased datasets from a set of people and feed those signatures to the model, carrying out the statistical analysis and calculating the accuracy of the algorithm after every signature classification. We have used Adaboost classifier which gave a cross-validation accuracy of 73.2% compared to other classifiers like Gradient Boosting Classifier, Random Forest Trees and Multi-Layer Perceptron which gave 73.2%, 63.2% and 59.6% accuracies respectively. Copy Right © INDIACom-2018.Item Corrosion Damage Identification and Lifetime Estimation of Ship Parts using Image Processing(Institute of Electrical and Electronics Engineers Inc., 2018) Naladala, I.; Raju, A.; Aishwarya, C.; Koolagudi, S.G.Corrosion is a process that leads to early failure of ship parts, high maintenance costs and a shortened service life of the ship, as a whole. Human visual inspection is currently the most widely used method to assess corrosion. In this paper, we propose the use of image processing to determine the extent of corrosion and estimate the time period within which the ship parts have to be replaced. In the case of availability of pre-corrosion images, the histograms of the pre-corrosion and post-corrosion images are compared and their similarity is quantified as the Sum of Squared Distances (SSD) value. Our method then produces a numerical output which signifies the level of corrosion. We then correlate extent of damage and ship part replacement period. In the absence of pre-corrosion images, we classify superpixels in the post-corrosion image as undamaged or damaged with an accuracy of 92 per cent, using Random Forest classifier. We have also evaluated the performance of corrosion prevention measures such as galvanization, painting, etc on different parts of the ship, for example, parts exposed to only air and parts exposed to both saline water and air. © 2018 IEEE.Item Identifying Parking Lots from Satellite Images using Transfer Learning(Institute of Electrical and Electronics Engineers Inc., 2019) Kumar, S.; Thomas, E.; Horo, A.With the advent of digital image processing techniques and convolutional neural networks, the world has derived numerous benefits such as computerized photography, biological Image Processing, finger print and iris recognition, to name a few. Computer vision coupled with convolutional neural networks has attributed machines with a virtual intellectual ability to recognize and distinguish images based on several characteristics that may be impossible for the human eye to perceive. We have exploited this advancement in technology to particular use case of detecting number of empty and occupied parking slots from satellite images of parking lots. We have proposed a befitting sequence of classical image processing techniques and algorithms to perform pre-processing of satellite images of parking spaces. Moreover, we have proposed a Convolutional Neural Network model that takes as input these preprocessed images and identifies the empty and occupied parking slots with an accuracy of 97.73%. The potential benefits of using Neural Networks to realize the objective can be extended to open parking spaces of different configurations. This is due to the fact that establishing sensors over a large number of parking slots over a given open parking space can be a cumbersome and exorbitant task. The proposed model comprises of few convolutional layers and uses Rectified Linear Classification activation function. © 2019 IEEE.