Rama Krishna Sai Subrahmanyam Gorthi

• Professor (Associate) at Indian Institute of Technology Tirupati

In the realm of object detection, the remarkable strides made by deep neural networks over the past decade have been hampered by challenges such as data labeling and the need to capture natural variations in training samples. Existing benchmark datasets are confined with limited set of classes, and natural variations. This paper presents "SA-LfV",...

Online Signature Verification (OSV) is a widely adapted biometric characteristics, that objects to determine the signature’s authenticity by computing its unique features. OSV has attracted greater attention in recent years in vital real-time applications like access control, m-commerce, etc. Deep learning (DL)-based Convolutional Neural Networks (...

Occlusion is a frequent phenomenon that hinders the task of visual object tracking. Since occlusion can be from any object and in any shape, data augmentation techniques will not greatly help identify or mitigate the tracker loss. Some of the existing works deal with occlusion only in an unsupervised manner. This paper proposes a generic deep learn...

The design of digital filters is now mostly automated with convolutional neural networks (CNNs). State-of-the-art works in tracking methods, including the well-known correlation and deep Siamese trackers, use features from such CNNs. However, deep learning requires huge data, high computational resources, and more training time. Hence, smart and si...

Occlusion is a frequent phenomenon that hinders the task of visual object tracking. Since occlusion can be from any object and in any shape, data augmentation techniques will not greatly help identify or mitigate the tracker loss. Some of the existing works deal with occlusion only in an unsupervised manner. This paper proposes a generic deep learn...

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To a...

Fringe projection profilometry (FPP) is the most commonly used structured light approach for 3D object profiling. Traditional FPP algorithms have multistage procedures that can lead to error propagation. Deep-learning-based end-to-end models currently have been developed to mitigate this error propagation and provide faithful reconstruction. In thi...

The Visual Object Tracking challenge VOT2022 is the tenth annual tracker benchmarking activity organized by the VOT initiative. Results of 93 entries are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2022 challenge was composed of seven sub-challenges focusing on...

Cranial implant design is a sophisticated time-intensive process performed by specialists uniquely for each patient using a set of standardized cranioplasty procedures. Automating the design of cranial implants for the ‘in-Operating-Room’ (in-OR) manufacturing pipeline is required to perform cranioplasty immediately after the primary surgery, there...

Fringe projection profilometry (FPP) is a widely used non-contact optical method for 3D profiling of objects. The commonly used linear fringe pattern in FPP has periodic intensity variations along the lateral direction. As a result, the linear fringe pattern used in FPP cannot uniquely represent the lateral shift induced by the objects having surfa...

The blood smear analysis provides vital information and forms the basis to diagnose most of the diseases. With recent developments, deep learning methods can analyze the microscopic blood sample using image processing and classification tasks with less human effort and increased accuracy. In this work, embarking upon domain-specific feature extract...

Fringe projection techniques are widely used for precise three-dimensional depth profiling of objects. Existing signal processing-based fringe projection techniques measure the phase deformation of the projected fringe patterns with a sequence of operations, such as fringe denoising, fringe analysis for wrapped phase extraction, followed by phase u...

Recently Deep Learning based Siamese Networks with region proposals for visual object tracking becoming more popular. These networks, while testing, perform extra computations on output if trained network, to predict the bounding box. This however hampering the precision of bounding box. In this work, the authors have proposed a network guided by I...

The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusin...

The application of machine learning principles in the photometric search of elusive astronomical objects has been a less-explored frontier of research. Here, we have used three methods, the neural network and two variants of k-nearest neighbour, to identify brown dwarf candidates using the photometric colours of known brown dwarfs. We initially che...

The application of machine learning principles in the photometric search of elusive astronomical objects has been a less-explored frontier of research. Here we have used three methods: the Neural Network and two variants of k-Nearest Neighbour, to identify brown dwarf candidates using the photometric colours of known brown dwarfs. We initially chec...

Visual object tracking is one of the major challenges in the field of computer vision. Correlation Filter (CF) trackers are one of the most widely used categories in tracking. Though numerous tracking algorithms based on CFs are available today, most of them fail to efficiently detect the object in an unconstrained environment with dynamically chan...

Visual object tracking is one of the major challenges in the field of computer vision. Correlation Filter (CF) trackers are one of the most widely used categories in tracking. Though numerous tracking algorithms based on CFs are available today, most of them fail to efficiently detect the object in an unconstrained environment with dynamically chan...

Correlation filter based object tracking has recently gained popularity due to continuous improvements in the tracking accuracy and robustness. However, these trackers are limited by the model drift problem due to wrong target appearances learned from an incorrectly tracked frame. The model drift increases as more frames are processed and restricts...

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popula...

In the recent years, convolutional neural networks (CNN) have been extensively employed in various complex computer vision tasks including visual object tracking. In this paper, we study the efficacy of temporal regression with Tikhonov regularization in generic object tracking. Among other major aspects, we propose a different approach to regress...

Star cluster studies hold the key to understanding star formation, stellar evolution, and origin of galaxies. The detection and characterization of clusters depend on the underlying background density and the cluster richness. We examine the ability of the Parzen Density Estimation (a.k.a. Parzen Windows) method, which is a generalization of the we...

Cloud Analysis plays an important role in understanding the climate changes which will be helpful in taking necessary mitigation policies. This work mainly aims to provide a novel framework for tracking as well as extracting characteristics of multiple cloud clusters by combining dense and sparse motion estimation techniques. The dense optical flow...

Visual Object tracking research has undergone significant improvement in the past few years. The emergence of tracking by detection approach in tracking paradigm has been quite successful in many ways. Recently, deep convolutional neural networks have been extensively used in most successful trackers. Yet, the standard approach has been based on co...

To catalyze Cr(III)/Cr(II) redox couple, Pb was used as candidate catalysts. Pb has a remarkable behavior on reduction and oxidation of chromium couple to enhance the performance of Fe-Cr redox flow cell. In the present investigation catalyzed carbon felt substrates were used as electrodes on negative side which were electrochemically plated/ loade...