Siddhant Ahuja

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In this paper we describe a fully integrated, real-time, miniaturized embedded stereo vision system (MESVS-II), which fits within 5times5cm and consumes very low power. This is a significant improvement over the original MESVS-I system in terms of performance, quality and accuracy of results. MESVS-II running at 600MHz per core, is capable of operating at(More)
In this paper, we present a efficient algorithm for real-time ellipse detection. Unlike Hough transform algorithm that is computationally intense and requires a higher dimensional parameter space, our proposed method reduces the computational complexity significantly, and accurately detects ellipses in realtime. We present a new method of detecting(More)
Standard Gaussian mixture modeling (GMM) is a well-known method for image segmentation. However, the pixels themselves are considered independent of each other, making the segmentation result sensitive to noise. To reduce the sensitivity of the segmented result with respect to noise, Markov random field (MRF) models provide a powerful way to account for(More)
We have developed a fully integrated, miniaturized embedded stereo vision system (MESVS-I) which fits into a tiny package of 5 times 5 cm and consumes very low power (700 mA @ 3.3 V). The system consists of two small profile CMOS cameras, and a power efficient, dual-core embedded media processor, running at 600 MHz per core. The stereo-matching engine(More)
Scan registration methods can often suffer from convergence and accuracy issues when the scan points are sparse or the environment violates the assumptions the methods are founded on. We propose an alternative approach to 3D scan registration using the curve let transform that performs multi-resolution geometric analysis to obtain a set of coefficients(More)
Topographic mapping in planetary environments relies on accurate 3D scan registration methods. However, most global registration algorithms relying on features such as FPFH and Harris-3D show poor alignment accuracy in these settings due to the poor structure of the Mars-like terrain and variable resolution, occluded, sparse range data that is hard to(More)
Topographic mapping in planetary environments relies on accurate 3D scan registration methods. However, most registration algorithms such as ICP, GICP and NDT show poor convergence properties in these settings due to the poor structure of the Mars-like terrain and variable resolution, occluded, sparse range data that is hard to register without some(More)
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