Non-Parametric Sequential Frame Decimation for Scene Reconstruction in Low-Memory Streaming Environments

  • Daniel Knoblauch
  • Mauricio Hess-Flores
  • Mark A. Duchaineau
  • Kenneth I. Joy
  • Falko Kuester
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)


This paper introduces a non-parametric sequential frame decimation algorithm for image sequences in low-memory streaming environments. Frame decimation reduces the number of input frames to increase pose and structure robustness in Structure and Motion (SaM) applications. The main contribution of this paper is the introduction of a sequential low-memory work-flow for frame decimation in embedded systems where memory and memory traffic come at a premium. This approach acts as an online preprocessing filter by removing frames that are ill-posed for reconstruction before streaming. The introduced sequential approach reduces the number of needed frames in memory to three in contrast to global frame decimation approaches that use at least ten frames in memory and is therefore suitable for low-memory streaming environments. This is moreover important in emerging systems with large format cameras which acquire data over several hours and therefore render global approaches impossible.

In this paper a new decimation metric is designed which facilitates sequential keyframe extraction fit for reconstruction purposes, based on factors such as a correspondence-to-feature ratio and residual error relationships between epipolar geometry and homography estimation. The specific design of the error metric allows a local sequential decimation metric evaluation and can therefore be used on the fly. The approach has been tested with various types of input sequences and results in reliable low-memory frame decimation robust to different frame sampling frequencies and independent of any thresholds, scene assumptions or global frame analysis.


Camera Movement Fundamental Matrix Epipolar Geometry Input Frame Reprojection Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel Knoblauch
    • 1
  • Mauricio Hess-Flores
    • 2
  • Mark A. Duchaineau
    • 3
  • Kenneth I. Joy
    • 2
  • Falko Kuester
    • 1
  1. 1.University of CaliforniaSan DiegoUSA
  2. 2.University of CaliforniaDavisUSA
  3. 3.Lawrence Livermore National LaboratoryLivermoreUSA

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