45,662 research outputs found
Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution
Given a set of images containing objects from the same category, the task of
image co-localization is to identify and localize each instance. This paper
shows that this problem can be solved by a simple but intriguing idea, that is,
a common object detector can be learnt by making its detection confidence
scores distributed like those of a strongly supervised detector. More
specifically, we observe that given a set of object proposals extracted from an
image that contains the object of interest, an accurate strongly supervised
object detector should give high scores to only a small minority of proposals,
and low scores to most of them. Thus, we devise an entropy-based objective
function to enforce the above property when learning the common object
detector. Once the detector is learnt, we resort to a segmentation approach to
refine the localization. We show that despite its simplicity, our approach
outperforms state-of-the-art methods.Comment: Accepted to Proc. European Conf. Computer Vision 201
Incorporating Road Networks into Territory Design
Given a set of basic areas, the territory design problem asks to create a
predefined number of territories, each containing at least one basic area, such
that an objective function is optimized. Desired properties of territories
often include a reasonable balance, compact form, contiguity and small average
journey times which are usually encoded in the objective function or formulated
as constraints. We address the territory design problem by developing graph
theoretic models that also consider the underlying road network. The derived
graph models enable us to tackle the territory design problem by modifying
graph partitioning algorithms and mixed integer programming formulations so
that the objective of the planning problem is taken into account. We test and
compare the algorithms on several real world instances
Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments
We consider the problem of sensor localization in a wireless network in a
multipath environment, where time and angle of arrival information are
available at each sensor. We propose a distributed algorithm based on belief
propagation, which allows sensors to cooperatively self-localize with respect
to one single anchor in a multihop network. The algorithm has low overhead and
is scalable. Simulations show that although the network is loopy, the proposed
algorithm converges, and achieves good localization accuracy
EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy
Visual inspection of the EEG to determine the seizure onset zone (SOZ) in the context of the presurgical evaluation in epilepsy is time-consuming and often challenging or impossible. We offer an approach that uses EEG source imaging (ESI) in combination with functional connectivity analysis (FC) to localize the SOZ from ictal EEG.
Ictal low-density-scalp EEG from 111 seizures in 27 patients who were rendered-seizure free after surgery was analyzed. For every seizure, ESI (LORETA) was applied on an artifact-free epoch selected around the seizure onset. Additionally, FC was applied on the reconstructed sources. We estimated the SOZ in two ways: (i)the source with highest power after ESI and (ii)the source with the most outgoing connections after ESI and FC. For both approaches, the distance between the estimated SOZ and the resected zone (RZ) of the patient were calculated.
Using ESI alone, the SOZ was estimated inside the RZ in 31% of the seizures and within 10mm from the border of the RZ in 42%. For 18.5% of the patients, all seizures were estimated within 10mm of the RZ. Using ESI and FC, 72% of the seizures were estimated inside the RZ, and 94% within 10mm. For 85% of the patients, all seizures were estimated within 10mm of the RZ. FC provided a significant added value to ESI alone (p<0.001).
ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy
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