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Dr. Sandeep M.
@svmandr
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Researcher, Academician, Trainer
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Experiência
Professor
jul. 2007 - mar. 2021 (13 anos, 8 meses)
Research Mentoring
Teaching: Subjects include Machine Learning, Artificial Intelligence, Data Analytics, IoT
Project Mentoring: 150+ UG projects, 30+ PG projects
Mentoring Research Scholars working in the field of Cognitive Radio, Machine Learning, Deep Learning, OpenCV, Data Analytics, Artificial Intelligence, Image Processing, Forgery Detection to name a few
Expertise in: Python, OpenCV, R, gretl, MatLab, Simulink, Arduino based Circuit design and building, IoT
Educação
Ph. D.
(7 anos)
ME(CSE)
(2 anos)
BE(ECE)
(5 anos)
Qualificações
R programming A-Z: R for Data science
Udemy
2019
Statistical information of the data will be a great resource while analysing data and building models to forecast or predictions. R programming is very powerful and handy tool for these purposes.
Starting from the raw data to cleaning to visualization, all the stages of data analytics are explained in depth. The course objective is to simplify the data analytics activities and provide effective outcomes.
Python for computer vision with OpenCV and Deep learning
Udemy
2019
Complete and detailed insights of how python helps the developers in building artificial intelligent agents through OpenCV and Deep learning. Computer vision is a major component of machine learning and artificial intelligence.
Publicações
Skeletal distance mapped functional features for improved CBIR
International Congress for Global Science and Technology (ICGST)-GVIP, 2011,11(3), 47-56
The size of the skeleton of the object is used for Content Based Image Retrieval. It makes use of a fast and efficient distance mapping technique DSFT.
Citations: 3
A scale and rotation invariant fast image mining for shapes
IEEE Conference on AI Tools in Engineering, Pune, India
An efficient and fast distance mapping tool called "Distance mapping through Scanning and Filling Technique" DSFT makes it possible to find the size of the object boundary and the inner and outer layers. This information allows us to find the shape signature for any given object. This makes the image mining invariant to rotation and scaling.
Citations: 6
Level set issues for efficient image segmentation
International Journal of Image and Data Fusion 2(1):75-92 DOI: 10.1080/19479832.2010.491802
This article presents the impact of several distance mapping and level set methods suggested in the literature and provides an effective way of handling it. Further, this article emphasises the need of periodic reinitialisation of the level set function to a signed distance function which makes curvature term become redundant. Frequent reinitialisation of the level set to signed distance function overcomes this limitation and increases the speed of evolution.
Citations: 6
Skeleton based Signatures for Content based Image Retrieval
International Journal of Computer Applications 23(7):29-34 DOI: 10.5120/2898-3793
Content Based Image Retrieval with fast and high matching retrieving ability is the need of the day for shape mining. A simple, fast, robust, invariant and efficient Content Based image Retrieval system with shape signatures derived from skeleton, region and boundary of the object is presented. The shape signatures derived from distance mapped function are invariant to rotation and scaling.
Citations: 3
Effective Level Sets and Shape Detection: An Application to Natural Images
VTU, Belagavi
My PhD Thesis
Use of Level set proposed by Chan-Vese, an attempt is made to segment the objects in the images of natural scenes and then the object is identified though the shape signature. The main contribution of the work is a novel distance mapping technique called " Distance mapping through Scanning and Filling Technique", DSFT. DSFT is very fast and efficient and hence made it possible to segment and derive the shape signatures for any situation.
Citation: 1
Redundant SIFT features via level sets for fast Copy Move Forgery detection
DOI: 10.1109/ICONSIP.2016.7857475 : Intl Conf on Signal & Information Processing IConSIP- 2016
Copy move forgery is a one of the common type of image forgery method. There are different methods of image forgery by copy move. One such method is where a background portion of image is copied and pasted on the foreground object to hide the some information from the image. In this paper we proposed a method for detection of such image tampering with the use of SIFT features and Chan-Vese's approach.
Citations: 3
Level sets for Real-world object segmentation
DOI: 10.1109/ICONSIP.2016.7857486: Intl Conf on Signal & Information Processing IConSIP- 2016
The work concentrates on the intelligent combination of the various features of the scene for efficient, robust & fast segmentation. DSFT, for distance mapping the level set and to find the shape signatures, provides a faster approach to segment the object and the powerful Chan-Vese's approach guarantees the segmentation within 5 iterations.
Citations: 2
Shape Based Copy Move Forgery Detection Using Level Set Approach
DOI: 10.1109/ICSIP.2014.40 : Fifth International Conference on Signal and Image Processing
This paper proposes a new method to Detect Copy Move Forgery using shape signatures derived from distance map. The method is simple, fast, robust, efficient as compared to traditional approaches and is invariant to scale, rotation and aspect ratio.
Citations: 11
Age Classification: Based On Wrinkle Analysis
IJRITCC, ISBN 2321-8169, 1(3), 119-124, 2013
As humans, we are capable to categorize a person's age group from an image of the person's face. This ability has not been pursued in the computer vision community. The method proposed in this article is capable of segregating the given input images into three clusters namely: Baby; Adult; Senior. The computations are based on wrinkle analysis algorithms.
Citations: 3
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