6,933 research outputs found
Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations
This paper presents a co-clustering technique that, given a collection of
images and their hierarchies, clusters nodes from these hierarchies to obtain a
coherent multiresolution representation of the image collection. We formalize
the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a
linear programming relaxation approach that makes effective use of information
from hierarchies. Initially, we address the problem of generating an optimal,
coherent partition per image and, afterwards, we extend this method to a
multiresolution framework. Finally, we particularize this framework to an
iterative multiresolution video segmentation algorithm in sequences with small
variations. We evaluate the algorithm on the Video Occlusion/Object Boundary
Detection Dataset, showing that it produces state-of-the-art results in these
scenarios.Comment: International Conference on Computer Vision (ICCV) 201
A framework for evaluating automatic image annotation algorithms
Several Automatic Image Annotation (AIA) algorithms have been introduced recently, which have been found to outperform previous models. However, each one of them has been evaluated using either different descriptors, collections or parts of collections, or "easy" settings. This fact renders their results non-comparable, while we show that collection-specific properties are responsible for the high reported performance measures, and not the actual models. In this paper we introduce a framework for the evaluation of image annotation models, which we use to evaluate two state-of-the-art AIA algorithms. Our findings reveal that a simple Support Vector Machine (SVM) approach using Global MPEG-7 Features outperforms state-of-the-art AIA models across several collection settings. It seems that these models heavily depend on the set of features and the data used, while it is easy to exploit collection-specific properties, such as tag popularity especially in the commonly used Corel 5K dataset and still achieve good performance
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
Multi-Modal Trip Hazard Affordance Detection On Construction Sites
Trip hazards are a significant contributor to accidents on construction and
manufacturing sites, where over a third of Australian workplace injuries occur
[1]. Current safety inspections are labour intensive and limited by human
fallibility,making automation of trip hazard detection appealing from both a
safety and economic perspective. Trip hazards present an interesting challenge
to modern learning techniques because they are defined as much by affordance as
by object type; for example wires on a table are not a trip hazard, but can be
if lying on the ground. To address these challenges, we conduct a comprehensive
investigation into the performance characteristics of 11 different colour and
depth fusion approaches, including 4 fusion and one non fusion approach; using
colour and two types of depth images. Trained and tested on over 600 labelled
trip hazards over 4 floors and 2000m in an active construction
site,this approach was able to differentiate between identical objects in
different physical configurations (see Figure 1). Outperforming a colour-only
detector, our multi-modal trip detector fuses colour and depth information to
achieve a 4% absolute improvement in F1-score. These investigative results and
the extensive publicly available dataset moves us one step closer to assistive
or fully automated safety inspection systems on construction sites.Comment: 9 Pages, 12 Figures, 2 Tables, Accepted to Robotics and Automation
Letters (RA-L
Persistent Evidence of Local Image Properties in Generic ConvNets
Supervised training of a convolutional network for object classification
should make explicit any information related to the class of objects and
disregard any auxiliary information associated with the capture of the image or
the variation within the object class. Does this happen in practice? Although
this seems to pertain to the very final layers in the network, if we look at
earlier layers we find that this is not the case. Surprisingly, strong spatial
information is implicit. This paper addresses this, in particular, exploiting
the image representation at the first fully connected layer, i.e. the global
image descriptor which has been recently shown to be most effective in a range
of visual recognition tasks. We empirically demonstrate evidences for the
finding in the contexts of four different tasks: 2d landmark detection, 2d
object keypoints prediction, estimation of the RGB values of input image, and
recovery of semantic label of each pixel. We base our investigation on a simple
framework with ridge rigression commonly across these tasks, and show results
which all support our insight. Such spatial information can be used for
computing correspondence of landmarks to a good accuracy, but should
potentially be useful for improving the training of the convolutional nets for
classification purposes
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