Srinath Sridhar

Learn More
Tracking the articulated 3D motion of the hand has important applications, for example, in human-computer interaction and teleoperation. We present a novel method that can capture a broad range of articulated hand motions at interactive rates. Our hybrid approach combines, in a voting scheme, a discriminative, part-based pose retrieval method with a(More)
Markerless tracking of hands and fingers is a promising enabler for human-computer interaction. However, adoption has been limited because of tracking inaccuracies, incomplete coverage of motions, low framerate, complex camera setups, and high computational requirements. In this paper, we present a fast method for accurately tracking rapid and complex(More)
Real-time marker-less hand tracking is of increasing importance in human-computer interaction. Robust and accurate tracking of arbitrary hand motion is a challenging problem due to the many degrees of freedom, frequent selfocclusions, fast motions, and uniform skin color. In this paper, we propose a new approach that tracks the full skeleton motion of the(More)
Real-time simultaneous tracking of hands manipulating and interacting with external objects has many potential applications in augmented reality, tangible computing, and wearable computing. However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the(More)
In-vehicle contextual augmented reality (I-CAR) has the potential to provide novel visual feedback to drivers for an enhanced driving experience. To enable I-CAR, we present a parametrized road trench model (RTM) for dynamically extracting display surfaces from a driver’s point of view that is adaptable to constantly changing road curvature and(More)
This paper contributes a novel sensing approach to support on- and above-skin finger input for interaction on the move. WatchSense uses a depth sensor embedded in a wearable device to expand the input space to neighboring areas of skin and the space above it. Our approach addresses challenging camera-based tracking conditions, such as oblique viewing angles(More)
Using hand gestures as input in human–computer interaction is of everincreasing interest. Markerless tracking of hands and fingers is a promising enabler, but adoption has been hampered because of tracking problems, complex and dense capture setups, high computing requirements, equipment costs, and poor latency. In this paper, we present a method that(More)
This paper investigates an emerging input method enabled by progress in hand tracking: input by free motion of fingers. The method is expressive, potentially fast, and usable across many settings as it does not insist on physical contact or visual feedback. Our goal is to inform the design of high-performance input methods by providing detailed analysis of(More)
An algorithm proposed by Sridhar and Kumaravel is extended to include a framework for the detection of renal calculi. Calculi occur due to abnormal collection of certain chemicals like oxalate, phosphate and uric acid. These calculi can be present in the kidney, ureter or urinary bladder. Performance analysis is done to a set of five known algorithms using(More)
The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining(More)