Qualification: 
Ph.D, M.E
s_padmavathi@cb.amrita.edu

S.Padmavathi,  Assistant Professor (Selection Grade) works at Amrita School of Engineering since 2001. She joined Amrita after securing ME in Computer Science Engineering from Government College of Technology, Coimbatore. She did her PhD in Amrita Vishwa Vidyapeetham, Coimbatore. She has one year of teaching experience and one year of industry experience before her Master’s Degree. Her research interests are allied with Digital Image Processing, Video processing, Image Analysis and Pattern recognition. She received Best Woman of the city award from Lions club in March, 2013 and Senior Woman Educator and Researcher Award from National Foundation for Entrepreneurship Development in March 2014.

She has completed various academic Projects in Digital Image Inpainting,  Vision based Fabric Defect Detection, fire detection, Number plate recognition, Vehicle detection and speed estimation, Traffic congestion analysis, face recognition,  Face tracking, object tracking, Gait recognition, Gesture Recognition, Indian Sign Language recognition, Age Classification from facial Images , Conversion of Braille to text and speech in English, Tamil and Hindi , Night time video enhancement, Texture analysis and synthesis, Content based image retrival using Texture, Wavelet based Image Compression, Image Mosaicking, Image Steganography ,Audio Steganography , Speech synthesis for native languages, Volume Visualization and Mesh Generation of Tomographic Images.

She was the Chair for a UG Session in “International Conference on Advanced Communication Systems” Organised by Govt. College of Technology, 2007. She severed as reviewer for various papers in International conference such as WICT 2011, CCSEIT-2012, SEAS-2012, WIMON2013, ITCSE 2013, ICCSEA-2013, ICAIT-2013, DPPR 2013, ACITY-2013, SIPP-2014, SCAI-2014, ITCSE-2014, ICONIAAC-2014, ICCSEA-2014, CSE-2014, AIAP-2014 etc. She has also reviewed paper for International Journals such as International Journal of Computer Science & Information Technology, International Journal of Software Engineering & Applications , Signal & Image Processing: An International Journal ,  International Journal of Information Sciences and Techniques, IET Image Processing, Applied Mathematics and Computation, Frontiers in Psychological and Behavioral Science(FPBS) , British Journal of Mathematics & Computer Science etc.

She has organized various workshops including “Introduction to Bioinformatic Algorithms and their Parallel Implementation”, Nov. 2001, GENESIS 06 – Online Project Contest- October 2006, Faculty Development Programme on Dr. Scheme with TCS,  May 2009,  “Recent Trends in Image Analysis” May 2009, “Cloud Computing” December 2009, ”Effective teaching learning of Computer programming” December 2009, IETE- National Technical Paper Contest, march 2012, National workshop on mobile applications and developer challenges in android powered devices , April 2014, National workshop on 3D Vision & multimedia streaming, May2014, Coding contest in Anokha2012 and 2015.

 She has delivered invited lectures on various topics of Image processing, Data Structures, Analysis of Algorithms across many colleges in Tamilnadu.

Publications

Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2018

Conference Proceedings

S. Krishnan, B.A. Sabarish, Gayathri V., and Dr. Padmavathi S., “Enhanced Defogging System on Foggy Digital Color Images”, Computational Vision and Bio Inspired Computing, Part of the Lecture Notes in Computational Vision and Biomechanics book series, vol. 28. Springer International Publishing, Cham, pp. 488-495, 2018.[Abstract]


Images which are captured using camera can cause degradation in images by the effect of climatic conditions such as haze and fog. Image restoration makes a notable change in performing different application of computer vision and pattern recognition. The main aim of this paper is to improve the effect of fog and hazy images compared to the existing methods. The enhanced defogging system [EDS] consists of different image improvement techniques with a Dark Channel Prior [DCP] Algorithm to estimate the amount of fog is there in the images and transmission as well. Fusion based fog removal will reduce the amount of haze remained in those images. Experiments were done more than 100 images and the results are discussed below.

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2017

Conference Proceedings

Sujee R. and Dr. Padmavathi S., “Image enhancement through pyramid histogram matching”, 2017 International Conference on Computer Communication and Informatics (ICCCI). IEEE, Coimbatore, India, 2017.[Abstract]


Pyramids being an emerging technology in the field of image processing, this paper uses the same for enhancing images using histogram matching. It gives a detailed analysis of enhancing the images by improving their contrasts using histogram matching in the pyramid layers thus extracting the information from the images to the maximum possible. It also shows the variation in the contrast of the images when matched with different sets of images of different contrasts.

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2015

Conference Proceedings

Dr. Padmavathi S. and Abirami, G., “Differential Iilumination Enhancement Technique For A Night Time Video”, International Conference on Power, Circuit and Information Technology (ICPCIT-2015). p. 8, 2015.[Abstract]


Video surveillance has become a common security need in the present-world scenario. The nighttime video surveillance becomes more challenging due to the presence of extreme illumination conditions, which is not uniform in a frame. Pedestrian detection would be very difficult under such illumination condition. The system proposes a method to identify and segregate the differently illuminated regions with the help of day-time reference image. Various enhancement techniques are applied on these regions separately and the results are combined to obtain the enhanced frame. Results of these techniques are summarized and appropriate methods are suggested for specific cases.

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2013

Conference Proceedings

Dr. Padmavathi S., Dr. Soman K. P., and Aarthi, R., “Image restoration using knowledge from the image”, Advances in Intelligent Systems and Computing, vol. 177 AISC. Chennai, pp. 19-25, 2013.[Abstract]


There are various real world situations where, a portion of the image is lost or damaged which needs an image restoration. A Prior knowledge of the image may not be available for restoring the image, which demands for a knowledge derivation from the image itself. Restoring the lost portions of the image based on the knowledge obtained from the image area surrounding the lost area is called as Digital Image Inpainting. The information content in the lost area could contain structural information like edges or textural information like repeating patterns. This knowledge is derived from the boundary area surrounding the lost area. Based on this, the lost area is restored by looking at similar information in the same image. Experimentation have been done on various images and observed that the algorithm restores the image in a visually plausible way. © 2013 Springer-Verlag.

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2012

Conference Proceedings

R. K. Shangeetha., Valliammai., V., and Dr. Padmavathi S., “Computer vision based approach for Indian sign language character recognition”, 2012 International Conference on Machine Vision and Image Processing, MVIP 2012. Coimbatore, Tamil Nadu, pp. 181-184, 2012.[Abstract]


Deaf and dumb people communicate among themselves using sign languages, but they find it difficult to expose themselves to the outside world. This paper proposes a method to convert the Indian Sign Language (ISL) hand gestures into appropriate text message. In this paper the hand gestures corresponding to ISL English alphabets are captured through a webcam. In the captured frames the hand is segmented and the state of fingers is used to recognize the alphabet. The features such as angle made between fingers, number of fingers that are fully opened, fully closed or semi closed and identification of each finger are used for recognition. Experimentation done for single hand alphabets and the results are summarised. © 2012 IEEE.

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2012

Conference Proceedings

R. Aarthi, Arunkumar, C., and Dr. Padmavathi S., “A survey of different stages for monitoring traffic rule violation”, Communications in Computer and Information Science, vol. 270 CCIS. Vellore, pp. 566-573, 2012.[Abstract]


A traffic surveillance system is a controlled system that helps to monitor and regulate the traffic. In this paper, a method for extracting the license number of the vehicle that is exceeding the speed limit is proposed. A Study is conducted by covering various stages of monitoring system such as vehicle detection in the video, tracking the vehicle for speed calculation and extracting the vehicle number in the number plate that can be used in places with high public vicinity. © 2012 Springer-Verlag.

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2012

Conference Proceedings

Dr. Padmavathi S., Rajalaxmi, C., and Dr. Soman K. P., “Texel identification using K-means clustering method”, Advances in Intelligent and Soft Computing, vol. 167 AISC. New Delhi, pp. 285-294, 2012.[Abstract]


Identifying the smallest portion of the image that represents the entire image is a basic need for its efficient storage. Texture can be defined as a pattern that is repeated in a specific manner. The basic pattern that is repeated is called as Texel(Texture Element). This paper describes a method of extracting a Texel from the given textured image using K means clustering algorithm and validating it with the entire image. The number of gray levels in an image is reduced using a linear transformation function. The image is then divided in to sub windows of certain size. These sub windows are clustered together using K-means algorithm. Finally a heuristic algorithm is applied on the cluster labels to identify the Texel, which results in more than one candidate for Texel. The best among them is then chosen based on its similarity with the overall image. The similarity between the Texel and the image is calculated based on then Normalized Gray level co-occurrence matrix in the maximum gradient direction. Experiments are conducted on various texture images for various block sizes and the results are summarized. © 2012 Springer-Verlag GmbH.

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2012

Conference Proceedings

Dr. Padmavathi S., Satishkumar, P., and Krishnan, B. Rohit, “Fabric Defect Detection Using Statistical Texture Analysis”, International Conference on Computer Applications ICCA2012. pp. 69 - 73, 2012.

2012

Conference Proceedings

Dr. Padmavathi S. and Archana, N., “Hierarchical TV Inpainting”, International Conference On Recent Trends In Computer Science And Engineering ICRTCSE – 2012. p. 37, 2012.

2012

Conference Proceedings

S. Suganya and Dr. Padmavathi S., “Inpainting using Laplacian Pyramid”, International Conference On Recent Trends In Computer Science And Engineering ICRTCSE – 2012. p. 31, 2012.

2012

Conference Proceedings

J. Kandasamy and Dr. Padmavathi S., “Structural Information of images using DCT”, International Conference On Recent Trends In Computer Science And Engineering ICRTCSE – 2012. p. 25, 2012.

2012

Conference Proceedings

Dr. Padmavathi S. and B. Lakshmi, P., “Wavelet based Digital Image Inpainting”, International Conference On Recent Trends In Computer Science And Engineering ICRTCSE – 2012. p. 26, 2012.

2012

Conference Proceedings

K. Kannan, Arunkumar, C., and Dr. Padmavathi S., “Image Recovery in a Contactless Finger Print Image”, National Conference On Simulations In Computing Nexus (NCSCN’12). 2012.

2012

Conference Proceedings

P. Satishkumar, Dr. Padmavathi S., and Krishnan, B. Rohit, “Fabric Defect Detection Using Graylevel Co-Occurrence Matrices (GLCM)”, National Conference on Emerging Trends in Computer Applications and Management (NCETCAM’12. 2012.

Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

Dr. Padmavathi S. and Abirami, G., “Detection of abrupt transitions in night time video for illumination enhancement”, Advances in Intelligent Systems and Computing, vol. 397, pp. 809-817, 2016.[Abstract]


A large number of security-related problems could be addressed by night time video surveillance. This has additional challenges of handling nonuniformly illuminated areas in the frame as compared to day time videos. The presence of vehicle lights degrades the existing background extraction process. An intensity- based shot boundary detection technique is applied for the night time videos in this paper. This algorithm is used to identify the abrupt transition frames that are caused by the vehicle lights or moving lights. The frames occuring between such detected frames could be reliably used for background extraction and night time video enhancement. The algorithm is tested on few real-time videos and their results are provided. © Springer India 2016.

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2016

Journal Article

Shanmuga Priya S. and Dr. Padmavathi S., “Comparative analysis of classification algorithms for predicting the advertisements on webpages”, Asian Journal of Information Technology, vol. 15, no. 4, pp. 738-742, 2016.[Abstract]


The fast growth of the Internet has completely changed the way people using computers. In the current scenario, people are more exposed to media and internet has led to the creation of advertisement which can reach users and it has become the ultimate for most business to enhance their profit. More and more ads are being sold on a single-impression basis as opposed to bulk purchases. Identifying whether an image belongs to advertisement or not is of interest to many internet users. This study analyses the performance of probabilistic, tree based and rule based classifier for this classification. Their performances under various conditions are summarized. © Medwell Journals, 2016.

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2016

Journal Article

Dr. Padmavathi S., Naveen, C. R., and V. Kumari, A., “Vision based Vehicle Counting for Traffic Congestion Analysis during Night Time”, Indian Journal of Science and Technology, vol. 9, 2016.[Abstract]


Background/Objectives: To create an automated system that controls the traffic signals effectively based on the instantaneous calculation of traffic congestion during the given time using image processing techniques. Methods/ Statistical Analysis: Vision based Congestion analysis is done based on the vehicles counted from the camera fixed on the signal post. In this paper, the vehicles are detected based on head lights and counted as two wheelers or four wheelers. The congestion is categorized into light, medium and heavy based on this count. Findings: The headlights are separated from the illuminated bright spots by considering its circular and elliptical nature. The accuracy of identifying four wheelers depend on the pairing of the head lights. The existing pairing algorithm fails when one head light is hidden by other vehicles. In this paper, a simple pairing algorithm based on the spatial adjacency is experimented. This had less accuracy due to pairing of two wheeler head lights with that of four wheelers. The problem is alleviated by considering a varying scale factor proportional to the perspective projection of the vehicles in the line of sight. The accuracy of counting increased to 98%, where the drop is due to non-elliptical blobs of head lights that were not detected. Improvements: To guarantee the robust performance of the system, particularly the accuracy and the real-time processing speed. Limitation: The presence of fog lights in modern 4 wheelers are detected as separate cars which reduces the accuracy. The area of improvement will be to consider them as fog lights of the same car and not to mark them as a separate 4 wheelers. More »»

2016

Journal Article

M. Divya and Dr. Padmavathi S., “Identification of the movements of human in a video”, International Journal of Control Theory and Applications, vol. 9, pp. 1119-1123, 2016.[Abstract]


Automatic video surveillance is used for monitoring the behavior of people from distance. Identifying human movement is a major task involved in the process. In this paper the movements of human in a video is identified by using the Global GIST feature. This feature is used to track the corner of the moving body in each frame. The results for various videos involving single or two persons are summarized. © International Science Press. More »»

2015

Journal Article

D. Swaminathan, Kiruthika, S., Nivin, A. A. L., and Dr. Padmavathi S., “Video Based Indian Sign Language Recognition System for Single and Double Handed Gestures with Unique Motion Trace as Feature”, International Journal of Tomography & Simulation™, vol. 28, pp. 71–88, 2015.[Abstract]


Gestures or Signs are the means through which audibly challenged people communicate with each other. Sign Language Recognition Systems computerize the job of a sign language translator. Our SLR system is built using powerful Image processing techniques. The system architecture involves techniques for skin color segmentation, Motion-Trace feature extraction and DTW Classification. Our proposed system takes as input a video of the gesture and outputs the text corresponding to the gesture performed. Gestures and signs are used interchangeably used throughout the paper. The system has an overall accuracy of 74.14%.

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2014

Journal Article

M. Niranjan, Ajtih, J., and Dr. Padmavathi S., “Dynamic Indian Sign Language Character Recognition: Using HOG Descriptor and a Customized Neural Network”, International Journal of Imaging and Robotics™, vol. 14, pp. 35–46, 2014.[Abstract]


Communication is the exchange of thoughts, messages, or information, by speech, visual signals, writing, or behaviour. Deaf and dumb people communicate among themselves using sign languages, but they find it difficult to expose themselves to the outside world. This paper proposes a method for recognizing Indian sign language (ISL) Two hand characters given as input by the user in the form of hand gestures. There are 18 ISL characters which are shown using two hands. Unlike the conventional method, this method does not require any additional hardware and makes the user comfortable. The system takes input at real time through a webcam integrated in the laptop. The hand region is separated out from the background using skin segmentation and motion segmentation. Since the segregation of overlapping hands is difficult, two hand characters are identified using HOG features. A back propagation neural network is used as the learning algorithm to make the system adaptable for different users. The system has been tested for several people of varying skin complexions, in several environments and was found to have accuracy of about 90%. The accuracy mainly dropped due to the illumination of the environment and occlusion of hands involved in two hand gestures.

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2014

Journal Article

Dr. Padmavathi S. and Soman, K. P., “Laplacian Pyramid based Hierarchical Image Inpainting”, Advances in Image and Video Processing, vol. 2, pp. 09–22, 2014.[Abstract]


There are many real world scenarios where a portion of the image is damaged or lost. Restoring such an image without prior knowledge or a reference image is a difficult task. Image inpainting is a method that focuses on reconstructing the damaged or missing portion of images based on the information available from undamaged areas of the same image. The existing methods fill the missing area from the boundary. Their performance varies while reconstructing the structure and texture present in the image and majorly fails for larger inpainting area. This paper attempts to segregate the structure and texture using Laplacian Pyramid and inpaint them separately using a top down approach. The images are inpainted from the lowest spatial resolution using Exemplar based image synthesis. The results are updated before moving to the higher resolution levels. This multi resolution process ensures the coarser details being filled before the finer details. The structure propagation is better since it is handled separately. The top down approach alleviates the traditional boundary based filling and breaks the single large sized inpainting region into many smaller sized ones as we move down the pyramid. Different types of images have been experimented and the results are summarized.

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2014

Journal Article

S. Divya, Kiruthika, S., Anton, A. L. Nivin, and Dr. Padmavathi S., “Segmentation, Tracking And Feature Extraction For Indian Sign Language Recognition”, International Journal on Computational Sciences & Applications (IJCSA), vol. 4, pp. 57 - 72, 2014.

2014

Journal Article

V. Nivedita, Dr. Padmavathi S., Sankari, G., RaamPrashanth, N. S., and , “Economic Printing Of Braille Documents”, International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), vol. 2, pp. 170-173, 2014.

2014

Journal Article

Dr. Padmavathi S. and , “A survey on vision based fire detection in videos”, International Journal of Engineering Research and Applications, vol. 4, pp. 85 - 88, 2014.

2014

Journal Article

Dr. Padmavathi S. and , “Face Tracking in Video by using Kalman filter”, International Journal of Engineering Research and Applications, vol. 4, no. 6, pp. 54 - 58, 2014.

2014

Journal Article

Dr. Padmavathi S., Kiruthika, S., and Kumar, P. I. Navin, “Survey on Hand Gesture Recognition”, International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 2, pp. 943 - 946, 2014.

2014

Journal Article

Dr. Padmavathi S. and Divya, S., “Survey on Tracking Algorithms”, International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 2, pp. 830 - 834, 2014.

2014

Journal Article

Dr. Padmavathi S. and Sreenath, S. P., “Literature Survey on Gesture Classification Techniques”, International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 4, pp. 990 - 992, 2014.

2014

Journal Article

M. R. Priyadharshini and Dr. Padmavathi S., “Survey on Vehicle Detection techniques”, International Journal of Engineering Research & Technology (IJERT), , vol. 3, no. 3, pp. 420 - 423, 2014.

2014

Journal Article

K. Gunasekaran and Dr. Padmavathi S., “Night Time Vehicle Detection for Real Time Traffic Monitoring Systems: A Review”, International Journal of Computer Technology & Applications IJCTA, vol. 5, pp. 451 - 456, 2014.

2014

Journal Article

Dr. Padmavathi S. and K. Kumar, A., “A Survey on Algorithms of Shadow Removal in Vehicle Detection”, International Journal of Computer Technology & Applications IJCTA, vol. 5, no. 2, pp. 518 - 521, 2014.

2014

Journal Article

Dr. Padmavathi S. and Saradha, S., “Study on Braille Input Output Devices”, International Journal of Engineering Research and Applications, vol. 4, no. 4, pp. 88-93, 2014.

2013

Journal Article

K. S. Karthik and Dr. Padmavathi S., “Indian Sign Language Gesture Segmentation Using Active Contour”, IJECCE, vol. 4, pp. 761–766, 2013.

2013

Journal Article

Dr. Padmavathi S., S Reddy, S., Meenakshy, D., and , “Conversion of Braille to Text in English, Hindi and Tamil Languages”, International Journal of Computer Science, Engineering and Applications (IJCSEA), vol. 3, pp. 19-32, 2013.[Abstract]


The Braille system has been used by the visually impaired for reading and writing. Due to limited availability of the Braille text books an efficient usage of the books becomes a necessity. This paper proposes a method to convert a scanned Braille document to text which can be read out to many through the computer. The Braille documents are pre processed to enhance the dots and reduce the noise. The Braille cells are segmented and the dots from each cell is extracted and converted in to a number sequence. These are mapped to the appropriate alphabets of the language. The converted text is spoken out through a speech synthesizer. The paper also provides a mechanism to type the Braille characters through the number pad of the keyboard. The typed Braille character is mapped to the alphabet and spoken out. The Braille cell has a standard representation but the mapping differs for each language. In this paper mapping of English, Hindi and Tamil are considered.

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2013

Journal Article

Dr. Padmavathi S., Prem, P., and Praveenn, D., “Locating Fabric Defects Using Gabor Filters”, International Research Engineering Journal of Scientific & Technology (IJSRET), vol. 2, no. 8, pp. 472 - 478, 2013.

2013

Journal Article

Dr. Padmavathi S., .S.Saipreethy, M., and .Valliammai, V., “Indian Sign Language Character Recognition”, pp. 40 - 45, 2013.

2013

Journal Article

Dr. Padmavathi S., S, S. M., and V, V., “Indian Sign Language Character Recognition using Neural Networks”, IJCA Special Issue on Recent Trends in Pattern Recognition and Image Analysis, vol. RTPRIA, pp. 40-45, 2013.[Abstract]


Deaf and dumb people communicate among themselves using sign languages, but they find it difficult to expose themselves to the outside world. This paper proposes a method to convert the Indian Sign Language (ISL) hand gestures into appropriate text message. In this paper the hand gestures corresponding to ISL English alphabets are captured through a webcam. In the captured frames the hand is segmented and the neural network is used to recognize the alphabet. The features such as angle made between fingers, number of fingers that are fully opened, fully closed or semi closed and identification of each finger are used as input to the neural network. Experimentation done for single hand alphabets and the results are summarized.

More »»

2012

Journal Article

Dr. Padmavathi S. and Soman, K. P., “Comparative Analysis of Structure and Texture based Image Inpainting Techniques”, International Journal of Electronics and Computer Science Engineering,(IJECSE) Volume, vol. 1, pp. 1062 - 1069, 2012.

2012

Journal Article

Dr. Padmavathi S., Archana, N., and Soman, K. P., “Hierarchical Approach for Total Variation Digital Image Inpainting”, International Journal of Computer Science, Engineering and Applications (IJCSEA), vol. 2, no. 3, pp. 173 - 182, 2012.[Abstract]


The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consuming process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.

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2012

Journal Article

Dr. Padmavathi S. and Soman, K. P., “A Hierarchical Search Space Refinement and filling for Exemplar based Image Inpainting”, International Journal of Computer Applications, vol. 52, pp. 31-37, 2012.[Abstract]


There are many real world scenarios where a portion of the image is damaged or lost. Restoring such an image without prior knowledge or a reference image is a difficult task. Image inpainting is a method that focuses on reconstructing the damaged or missing portion of images based on the information available from undamaged areas of the same image. The existing methods fill the missing area from the boundary. Their performance varies while reconstructing structures and textures and many of them restrict the size of the area to be inpainted. In this paper exemplar based inpainting is adopted in a hierarchical framework. A hierarchical search space refinement and hierarchical filling are proposed in this paper which increases the accuracy and handles the extra cost due to multi resolution processing in a better way. The former tries to select an exemplar suitable at all resolution levels restricting the search space from the lower resolution level. The later fills the region at lower resolution level whose results are taken to the higher levels. This makes the non boundary pixels known in the higher resolution level which in turn helps in search space refinement while increasing accuracy.

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2012

Journal Article

Dr. Padmavathi S., Priyalakshmi, B., and K. P. Soman, “Hirarchical Digital Image Inpainting Using Wavelets”, Signal & Image Processing:An International Journal (SIPIJ), vol. 3, no. 4, pp. 85-93, 2012.[Abstract]


Inpainting is the technique of reconstructing unknown or damaged portions of an image in a visually plausible way. Inpainting algorithm automatically fills the damaged region in an image using the information available in undamaged region. Propagation of structure and texture information becomes a challenge as the size of damaged area increases. In this paper, a hierarchical inpainting algorithm using wavelets is proposed. The hierarchical method tries to keep the mask size smaller while wavelets help in handling the high pass structure information and low pass texture information separately. The performance of the proposed algorithm is tested using different factors. The results of our algorithm are compared with existing methods such as interpolation, diffusion and exemplar techniques.

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Publication Type: Conference Paper

Year of Publication Publication Type Title

2010

Conference Paper

R. Aarthi, Dr. Padmavathi S., and Amudha, J., “Vehicle detection in static images using color and corner map”, in ITC 2010 - 2010 International Conference on Recent Trends in Information, Telecommunication, and Computing, Kochi, Kerala, 2010, pp. 244-246.[Abstract]


This paper presents an approach to identify the vehicle in the static images using color and corner map. The detection of vehicles in a traffic scene can address wide range of traffic problems. Here an attempt has been made to reduce the search time to find the possible vehicle candidates thereby reducing the computation time without a full search. A color transformation is used to project all the colors of input pixels to a new feature space such that vehicle pixels can be easily distinguished from non-vehicle ones. Bayesian classifier is adopted for verifying the vehicle pixels from the background. Corner map is used for removing the false detections and to verify the vehicle candidates. © 2010 IEEE.

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2009

Conference Paper

Dr. Padmavathi S., P, I., T, N., D, S., A, S., and S, S., “Audio Steganography”, in National Conference on Cryptography and Network Security , 2009.

Honors and Awards 

  1. Best Woman of the city award by Lions club in March, 2013
  2. Senior Woman Educator and Researcher Award by National Foundation for Entrepreneurship Development in March 2014
  3. Session Chair for a UG Session in “International Conference on Advanced Communication Systems” Organised by Govt. College of Technology on 10/01/2007
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