288,596 research outputs found
Bio-inspired speed detection and discrimination
In the field of computer vision, a crucial task is the detection of motion
(also called optical flow extraction). This operation allows analysis such as
3D reconstruction, feature tracking, time-to-collision and novelty detection
among others. Most of the optical flow extraction techniques work within a
finite range of speeds. Usually, the range of detection is extended towards
higher speeds by combining some multiscale information in a serial
architecture. This serial multi-scale approach suffers from the problem of
error propagation related to the number of scales used in the algorithm. On the
other hand, biological experiments show that human motion perception seems to
follow a parallel multiscale scheme. In this work we present a bio-inspired
parallel architecture to perform detection of motion, providing a wide range of
operation and avoiding error propagation associated with the serial
architecture. To test our algorithm, we perform relative error comparisons
between both classical and proposed techniques, showing that the parallel
architecture is able to achieve motion detection with results similar to the
serial approach
Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation
Scene flow estimation, which extracts point-wise motion between scenes, is
becoming a crucial task in many computer vision tasks. However, all of the
existing estimation methods utilize only the unidirectional features,
restricting the accuracy and generality. This paper presents a novel scene flow
estimation architecture using bidirectional flow embedding layers. The proposed
bidirectional layer learns features along both forward and backward directions,
enhancing the estimation performance. In addition, hierarchical feature
extraction and warping improve the performance and reduce computational
overhead. Experimental results show that the proposed architecture achieved a
new state-of-the-art record by outperforming other approaches with large margin
in both FlyingThings3D and KITTI benchmarks. Codes are available at
https://github.com/cwc1260/BiFlow.Comment: Accepted as a conference paper at European Conference on Computer
Vision (ECCV) 202
FlowNet: Learning Optical Flow with Convolutional Networks
Convolutional neural networks (CNNs) have recently been very successful in a
variety of computer vision tasks, especially on those linked to recognition.
Optical flow estimation has not been among the tasks where CNNs were
successful. In this paper we construct appropriate CNNs which are capable of
solving the optical flow estimation problem as a supervised learning task. We
propose and compare two architectures: a generic architecture and another one
including a layer that correlates feature vectors at different image locations.
Since existing ground truth data sets are not sufficiently large to train a
CNN, we generate a synthetic Flying Chairs dataset. We show that networks
trained on this unrealistic data still generalize very well to existing
datasets such as Sintel and KITTI, achieving competitive accuracy at frame
rates of 5 to 10 fps.Comment: Added supplementary materia
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