2,729 research outputs found
Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European
Conference on Computer Vision (ECCV) 2016;
https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels.
The problem of estimating subjective visual properties from image and video
has attracted increasing interest. A subjective visual property is useful
either on its own (e.g. image and video interestingness) or as an intermediate
representation for visual recognition (e.g. a relative attribute). Due to its
ambiguous nature, annotating the value of a subjective visual property for
learning a prediction model is challenging. To make the annotation more
reliable, recent studies employ crowdsourcing tools to collect pairwise
comparison labels because human annotators are much better at ranking two
images/videos (e.g. which one is more interesting) than giving an absolute
value to each of them separately. However, using crowdsourced data also
introduces outliers. Existing methods rely on majority voting to prune the
annotation outliers/errors. They thus require large amount of pairwise labels
to be collected. More importantly as a local outlier detection method, majority
voting is ineffective in identifying outliers that can cause global ranking
inconsistencies. In this paper, we propose a more principled way to identify
annotation outliers by formulating the subjective visual property prediction
task as a unified robust learning to rank problem, tackling both the outlier
detection and learning to rank jointly. Differing from existing methods, the
proposed method integrates local pairwise comparison labels together to
minimise a cost that corresponds to global inconsistency of ranking order. This
not only leads to better detection of annotation outliers but also enables
learning with extremely sparse annotations. Extensive experiments on various
benchmark datasets demonstrate that our new approach significantly outperforms
state-of-the-arts alternatives.Comment: 14 pages, accepted by IEEE TPAM
Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics
This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p
- …