TR2009-035

Geometric Sequence (GS) Imaging with Bayesian Smoothing for Optical and Capacitive Imaging Sensors


    •  Sengupta, K., Porikli, F., "Geometric Sequence (GS) Imaging with Bayesian Smoothing for Optical and Capacitive Imaging Sensors", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/​CVPR.2009.5205205, June 2009, pp. 90-97.
      BibTeX TR2009-035 PDF
      • @inproceedings{Sengupta2009jun,
      • author = {Sengupta, K. and Porikli, F.},
      • title = {Geometric Sequence (GS) Imaging with Bayesian Smoothing for Optical and Capacitive Imaging Sensors},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2009,
      • pages = {90--97},
      • month = jun,
      • doi = {10.1109/CVPR.2009.5205205},
      • url = {https://www.merl.com/publications/TR2009-035}
      • }
  • Research Area:

    Computer Vision

Abstract:

In this paper, we introduce a novel technique called Geometric Sequence (GS) imaging, specifically for the purpose of low power and light weight tracking in human computer interface design. The imaging sensor is programmed to capture the scene with a train of packets, where each packet constitutes a few images. The delay or the baseline associated with consecutive image pairs in a packet follows a fixed ratio, as in a geometric sequence. The image pair with shorter baseline or delay captures fast motion, while the image pair with larger baseline or delay captures slow motion. Given an image packet, the motion confidence maps computed from the slow and the fast image pairs are fused into a single map. Next, we use a Bayesian update scheme to compute the motion hypotheses probability map, given the information of prior packets. We estimate the motion from this probability map. The GS imaging system reliably tracks slow movement as well as fast movements, a feature that is important in realizing applications such as a touchpad type system. Compared to continuous imaging with short delay between consecutive pairs, the GS imaging technique enjoys several advantages. The overall power consumption and the CPU load are significantly low. We present results in the domain of optical camera based human computer interface (HCI) applications, as well as for capacitive fingerprint imaging sensor based touch pad systems.

 

  • Related News & Events

    •  NEWS    CVPR 2009: 6 publications by Amit Agrawal and others
      Date: June 20, 2009
      Where: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
      Research Area: Computer Vision
      Brief
      • The papers "3D Pose Estimation and Segmentation using Specular Cues" by Chang, J.-Y., Raskar, R. and Agrawal, A., "Coded Exposure Deblurring: Optimized Codes for PSF Estimation and Invertibility" by Agrawal, A. and Xu, Y., "Enforcing Integrability by Error Correction using $l_1$-minimization" by Reddy, D., Agrawal, A. and Chellappa, R., "Multi-Class Active Learning for Image Classification" by Joshi, A.J., Porikli, F. and Papanikolopoulos, N., "Optimal Single Image Capture for Motion Deblurring" by Agrawal, A. and Raskar, R. and "Geometric Sequence (GS) Imaging with Bayesian Smoothing for Optical and Capacitive Imaging Sensors" by Sengupta, K. and Porikli, F. were presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    •