25,973 research outputs found
Superpixels: An Evaluation of the State-of-the-Art
Superpixels group perceptually similar pixels to create visually meaningful
entities while heavily reducing the number of primitives for subsequent
processing steps. As of these properties, superpixel algorithms have received
much attention since their naming in 2003. By today, publicly available
superpixel algorithms have turned into standard tools in low-level vision. As
such, and due to their quick adoption in a wide range of applications,
appropriate benchmarks are crucial for algorithm selection and comparison.
Until now, the rapidly growing number of algorithms as well as varying
experimental setups hindered the development of a unifying benchmark. We
present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms
utilizing a benchmark focussing on fair comparison and designed to provide new
insights relevant for applications. To this end, we explicitly discuss
parameter optimization and the importance of strictly enforcing connectivity.
Furthermore, by extending well-known metrics, we are able to summarize
algorithm performance independent of the number of generated superpixels,
thereby overcoming a major limitation of available benchmarks. Furthermore, we
discuss runtime, robustness against noise, blur and affine transformations,
implementation details as well as aspects of visual quality. Finally, we
present an overall ranking of superpixel algorithms which redefines the
state-of-the-art and enables researchers to easily select appropriate
algorithms and the corresponding implementations which themselves are made
publicly available as part of our benchmark at
davidstutz.de/projects/superpixel-benchmark/
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
We present DroTrack, a high-speed visual single-object tracking framework for
drone-captured video sequences. Most of the existing object tracking methods
are designed to tackle well-known challenges, such as occlusion and cluttered
backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in
three-dimensional space, causes high uncertainty. The uncertainty problem leads
to inaccurate location predictions and fuzziness in scale estimations. DroTrack
solves such issues by discovering the dependency between object representation
and motion geometry. We implement an effective object segmentation based on
Fuzzy C Means (FCM). We incorporate the spatial information into the membership
function to cluster the most discriminative segments. We then enhance the
object segmentation by using a pre-trained Convolution Neural Network (CNN)
model. DroTrack also leverages the geometrical angular motion to estimate a
reliable object scale. We discuss the experimental results and performance
evaluation using two datasets of 51,462 drone-captured frames. The combination
of the FCM segmentation and the angular scaling increased DroTrack precision by
up to and decreased the centre location error by pixels on average.
DroTrack outperforms all the high-speed trackers and achieves comparable
results in comparison to deep learning trackers. DroTrack offers high frame
rates up to 1000 frame per second (fps) with the best location precision, more
than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Efforts to automate the reconstruction of neural circuits from 3D electron
microscopic (EM) brain images are critical for the field of connectomics. An
important computation for reconstruction is the detection of neuronal
boundaries. Images acquired by serial section EM, a leading 3D EM technique,
are highly anisotropic, with inferior quality along the third dimension. For
such images, the 2D max-pooling convolutional network has set the standard for
performance at boundary detection. Here we achieve a substantial gain in
accuracy through three innovations. Following the trend towards deeper networks
for object recognition, we use a much deeper network than previously employed
for boundary detection. Second, we incorporate 3D as well as 2D filters, to
enable computations that use 3D context. Finally, we adopt a recursively
trained architecture in which a first network generates a preliminary boundary
map that is provided as input along with the original image to a second network
that generates a final boundary map. Backpropagation training is accelerated by
ZNN, a new implementation of 3D convolutional networks that uses multicore CPU
parallelism for speed. Our hybrid 2D-3D architecture could be more generally
applicable to other types of anisotropic 3D images, including video, and our
recursive framework for any image labeling problem
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