7,651 research outputs found
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
In this paper, a new method for generating object and action proposals in
images and videos is proposed. It builds on activations of different
convolutional layers of a pretrained CNN, combining the localization accuracy
of the early layers with the high informative-ness (and hence recall) of the
later layers. To this end, we build an inverse cascade that, going backward
from the later to the earlier convolutional layers of the CNN, selects the most
promising locations and refines them in a coarse-to-fine manner. The method is
efficient, because i) it re-uses the same features extracted for detection, ii)
it aggregates features using integral images, and iii) it avoids a dense
evaluation of the proposals thanks to the use of the inverse coarse-to-fine
cascade. The method is also accurate. We show that our DeepProposals outperform
most of the previously proposed object proposal and action proposal approaches
and, when plugged into a CNN-based object detector, produce state-of-the-art
detection performance.Comment: 15 page
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
The detection performance of small objects in remote sensing images is not
satisfactory compared to large objects, especially in low-resolution and noisy
images. A generative adversarial network (GAN)-based model called enhanced
super-resolution GAN (ESRGAN) shows remarkable image enhancement performance,
but reconstructed images miss high-frequency edge information. Therefore,
object detection performance degrades for small objects on recovered noisy and
low-resolution remote sensing images. Inspired by the success of edge enhanced
GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN
(EESRGAN) to improve the image quality of remote sensing images and use
different detector networks in an end-to-end manner where detector loss is
backpropagated into the EESRGAN to improve the detection performance. We
propose an architecture with three components: ESRGAN, Edge Enhancement Network
(EEN), and Detection network. We use residual-in-residual dense blocks (RRDB)
for both the ESRGAN and EEN, and for the detector network, we use the faster
region-based convolutional network (FRCNN) (two-stage detector) and single-shot
multi-box detector (SSD) (one stage detector). Extensive experiments on a
public (car overhead with context) and a self-assembled (oil and gas storage
tank) satellite dataset show superior performance of our method compared to the
standalone state-of-the-art object detectors.Comment: This paper contains 27 pages and accepted for publication in MDPI
remote sensing journal. GitHub Repository:
https://github.com/Jakaria08/EESRGAN (Implementation
Efficient Processing of Spatial Joins Using R-Trees
Abstract: In this paper, we show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a so-called shared virtual memory which is well-suited for the design and implementation of parallel spatial join algorithms. We start with an algorithm that consists of three phases: task creation, task assignment and parallel task execu-tion. In order to reduce CPU- and I/O-cost, the three phases are processed in a fashion that pre-serves spatial locality. Dynamic load balancing is achieved by splitting tasks into smaller ones and reassigning some of the smaller tasks to idle processors. In an experimental performance compar-ison, we identify the advantages and disadvantages of several variants of our algorithm. The most efficient one shows an almost optimal speed-up under the assumption that the number of disks is sufficiently large. Topics: spatial database systems, parallel database systems
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