5,239 research outputs found
Edge Detection: A Collection of Pixel based Approach for Colored Images
The existing traditional edge detection algorithms process a single pixel on
an image at a time, thereby calculating a value which shows the edge magnitude
of the pixel and the edge orientation. Most of these existing algorithms
convert the coloured images into gray scale before detection of edges. However,
this process leads to inaccurate precision of recognized edges, thus producing
false and broken edges in the image. This paper presents a profile modelling
scheme for collection of pixels based on the step and ramp edges, with a view
to reducing the false and broken edges present in the image. The collection of
pixel scheme generated is used with the Vector Order Statistics to reduce the
imprecision of recognized edges when converting from coloured to gray scale
images. The Pratt Figure of Merit (PFOM) is used as a quantitative comparison
between the existing traditional edge detection algorithm and the developed
algorithm as a means of validation. The PFOM value obtained for the developed
algorithm is 0.8480, which showed an improvement over the existing traditional
edge detection algorithms.Comment: 5 Page
Frustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and
outdoor scenes. While previous methods focus on images or 3D voxels, often
obscuring natural 3D patterns and invariances of 3D data, we directly operate
on raw point clouds by popping up RGB-D scans. However, a key challenge of this
approach is how to efficiently localize objects in point clouds of large-scale
scenes (region proposal). Instead of solely relying on 3D proposals, our method
leverages both mature 2D object detectors and advanced 3D deep learning for
object localization, achieving efficiency as well as high recall for even small
objects. Benefited from learning directly in raw point clouds, our method is
also able to precisely estimate 3D bounding boxes even under strong occlusion
or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection
benchmarks, our method outperforms the state of the art by remarkable margins
while having real-time capability.Comment: 15 pages, 12 figures, 14 table
Distant Vehicle Detection Using Radar and Vision
For autonomous vehicles to be able to operate successfully they need to be
aware of other vehicles with sufficient time to make safe, stable plans. Given
the possible closing speeds between two vehicles, this necessitates the ability
to accurately detect distant vehicles. Many current image-based object
detectors using convolutional neural networks exhibit excellent performance on
existing datasets such as KITTI. However, the performance of these networks
falls when detecting small (distant) objects. We demonstrate that incorporating
radar data can boost performance in these difficult situations. We also
introduce an efficient automated method for training data generation using
cameras of different focal lengths
DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data
We introduce the DROW detector, a deep learning based detector for 2D range
data. Laser scanners are lighting invariant, provide accurate range data, and
typically cover a large field of view, making them interesting sensors for
robotics applications. So far, research on detection in laser range data has
been dominated by hand-crafted features and boosted classifiers, potentially
losing performance due to suboptimal design choices. We propose a Convolutional
Neural Network (CNN) based detector for this task. We show how to effectively
apply CNNs for detection in 2D range data, and propose a depth preprocessing
step and voting scheme that significantly improve CNN performance. We
demonstrate our approach on wheelchairs and walkers, obtaining state of the art
detection results. Apart from the training data, none of our design choices
limits the detector to these two classes, though. We provide a ROS node for our
detector and release our dataset containing 464k laser scans, out of which 24k
were annotated.Comment: Lucas Beyer and Alexander Hermans contributed equall
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