His primary areas of study are Artificial intelligence, Computer vision, Mobile computing, Image retrieval and Histogram. Vijay Chandrasekhar interconnects Speech recognition and Natural language processing in the investigation of issues within Artificial intelligence. His research in Computer vision intersects with topics in Visual search and Pattern recognition.
As part of the same scientific family, Vijay Chandrasekhar usually focuses on Pattern recognition, concentrating on Feature and intersecting with Search engine indexing. His Mobile computing study combines topics in areas such as Image processing, Augmented reality, Multimedia and Robustness. Vijay Chandrasekhar is interested in Visual Word, which is a branch of Image retrieval.
Vijay Chandrasekhar mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Image retrieval and Histogram. His work is connected to Feature, Visual Word, Feature extraction, Scale-invariant feature transform and Histogram of oriented gradients, as a part of Artificial intelligence. His research investigates the connection between Computer vision and topics such as Visual search that intersect with problems in Camera phone.
Vijay Chandrasekhar usually deals with Pattern recognition and limits it to topics linked to Quantization and Quantization, Uncompressed video, Convolutional neural network and Artificial neural network. As a part of the same scientific family, Vijay Chandrasekhar mostly works in the field of Image retrieval, focusing on Vector quantization and, on occasion, Entropy. His Augmented reality course of study focuses on Mobile computing and Robustness and Multimedia.
Vijay Chandrasekhar mainly investigates Artificial intelligence, Applied mathematics, Mixture model, Saddle and Saddle point. His Artificial intelligence research includes themes of Machine learning and Pattern recognition. His work deals with themes such as Pooling and Relevance, which intersect with Pattern recognition.
His Applied mathematics research is multidisciplinary, relying on both Convergence and Bilinear interpolation. His Convergence study integrates concerns from other disciplines, such as Stability and Face. Gradient descent, Class and Variational inequality are fields of study that intersect with his Saddle study.
Vijay Chandrasekhar spends much of his time researching Convergence, Applied mathematics, Mixture model, Bilinear interpolation and Anomaly detection. His Convergence study frequently draws connections to other fields, such as Gradient descent. His studies in Applied mathematics integrate themes in fields like Stability, Face and Moving average.
His biological study spans a wide range of topics, including Range, Image and Leverage. Range is a primary field of his research addressed under Artificial intelligence. As part of his studies on Artificial intelligence, he often connects relevant areas like Bandwidth.
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ICDAR 2015 competition on Robust Reading
Dimosthenis Karatzas;Lluis Gomez-Bigorda;Anguelos Nicolaou;Suman Ghosh.
international conference on document analysis and recognition (2015)
Compressed Histogram of Gradients: A Low-Bitrate Descriptor
Vijay Chandrasekhar;Gabriel Takacs;David M. Chen;Sam S. Tsai.
International Journal of Computer Vision (2012)
Localization in underwater sensor networks: survey and challenges
Vijay Chandrasekhar;Winston Kg Seah;Yoo Sang Choo;How Voon Ee.
Proceedings of the 1st ACM international workshop on Underwater networks (2006)
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
Gabriel Takacs;Vijay Chandrasekhar;Natasha Gelfand;Yingen Xiong.
multimedia information retrieval (2008)
Mobile Visual Search
B Girod;V Chandrasekhar;D M Chen;Ngai-Man Cheung.
IEEE Signal Processing Magazine (2011)
Efficient GAN-Based Anomaly Detection
Houssam Zenati;Chuan Sheng Foo;Bruno Lecouat;Gaurav Manek.
arXiv: Learning (2018)
CHoG: Compressed histogram of gradients A low bit-rate feature descriptor
Vijay Chandrasekhar;Gabriel Takacs;David Chen;Sam Tsai.
computer vision and pattern recognition (2009)
Unified Real-Time Tracking and Recognition with Rotation-Invariant Fast Features
Gabriel Takacs;Vijay Chandrasekhar;Sam Tsai;David Chen.
computer vision and pattern recognition (2010)
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
Juan Pablo Correa-Baena;Kedar Hippalgaonkar;Jeroen van Duren;Shaffiq Jaffer.
An Area Localization Scheme for Underwater Sensor Networks
V. Chandrasekhar;W. Seah.
OCEANS 2006 - Asia Pacific (2006)
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