Publications

@inproceedings{venkatachalam_voice-driven_2024,

title = {Voice-Driven Panoramic Imagery: Real-Time Generative AI for Immersive Experiences},

author = {N. Venkatachalam and M. Rayana and S. Bala Vignesh and S. Prathamesh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190121845&doi=10.1109%2fIDCIoT59759.2024.10467441&partnerID=40&md5=6594fbab013d9156b79a887f0d7209cb},

doi = {10.1109/IDCIoT59759.2024.10467441},

isbn = {979-835032753-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Int. Conf. Intell. Data Commun. Technol. Internet Things, IDCIoT},

pages = {1133–1138},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {This research study introduces an innovative system that aims to synthesize 360-degree panoramic images in Realtime based on vocal prompts from the user, leveraging state-of-The-Art Generative AI with a combination of advanced NLP models. The primary objective of this system is to transform spoken descriptions into immersive and interactive visual scenes, specifically designed to provide users with first-person field views. This cutting-edge technology has the potential to revolutionize the realm of virtual reality (VR) experiences, enabling users to effortlessly create and navigate through personalized environments. The fundamental goal of this system is to enable the generation of real-Time images that are seamlessly compatible with VR headsets, offering a truly immersive and adaptive visual experience. Beyond its technological advancements, this research also highlights its significant potential for creating a positive social impact. One notable application lies in psychological interventions, particularly in the context of phobia treatment and therapeutic settings. Here, patients can safely confront and work through their fears within these synthesized environments, potentially offering new avenues for therapy. Furthermore, the system serves educational and entertainment purposes by bringing users' imaginations to life, providing an unparalleled platform for exploring the boundaries of virtual experiences. Overall, this research represents a promising stride towards a more immersive and adaptable future in VR technology, with the potential to enhance various aspects of human lives, from mental health treatment to entertainment and education. © 2024 IEEE.},

keywords = {Adaptive Visual Experience, First person, First-Person view, generative artificial intelligence, Generative Artificial Intelligence (AI), Image processing, Immersive, Immersive visual scene, Immersive Visual Scenes, Language processing, Natural Language Processing, Natural Language Processing (NLP), Natural language processing systems, Natural languages, Panoramic Images, Patient treatment, Personalized environment, Personalized Environments, Phobia Treatment, Prompt, prompts, Psychological intervention, Psychological Interventions, Real-Time Synthesis, User interaction, User interfaces, Virtual experience, Virtual Experiences, Virtual Reality, Virtual Reality (VR), Virtual-reality headsets, Visual experiences, Visual languages, Visual scene, Voice command, Voice commands, VR Headsets},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{franchiniSpecializedArchitectureColor2013,

title = {A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra},

author = { Silvia Franchini and Antonio Gentile and Giorgio Vassallo and Filippo Sorbello and Salvatore Vitabile},

doi = {10.1109/CISIS.2013.29},

isbn = {978-0-7695-4992-7},

year  = {2013},

date = {2013-01-01},

pages = {128--135},

abstract = {Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. textcopyright 2013 IEEE.},

keywords = {Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{franchini_specialized_2013,

title = {A specialized architecture for color image edge detection based on Clifford algebra},

author = {Silvia Franchini and Antonio Gentile and Giorgio Vassallo and Filippo Sorbello and Salvatore Vitabile},

doi = {10.1109/CISIS.2013.29},

isbn = {978-0-7695-4992-7},

year  = {2013},

date = {2013-01-01},

pages = {128–135},

abstract = {Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. © 2013 IEEE.},

keywords = {Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{franchiniCliffordAlgebraBased2012,

title = {Clifford Algebra Based Edge Detector for Color Images},

author = { Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},

doi = {10.1109/CISIS.2012.128},

isbn = {978-0-7695-4687-2},

year  = {2012},

date = {2012-01-01},

pages = {84--91},

abstract = {Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. textcopyright 2012 Crown Copyright.},

keywords = {Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{franchini_clifford_2012,

title = {Clifford Algebra based edge detector for color images},

author = {Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},

doi = {10.1109/CISIS.2012.128},

isbn = {978-0-7695-4687-2},

year  = {2012},

date = {2012-01-01},

pages = {84–91},

abstract = {Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. © 2012 Crown Copyright.},

keywords = {Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation},

pubstate = {published},

tppubtype = {inproceedings}

}