851 research outputs found
MirrorNet: Bio-Inspired Camouflaged Object Segmentation
Camouflaged objects are generally difficult to be detected in their natural
environment even for human beings. In this paper, we propose a novel
bio-inspired network, named the MirrorNet, that leverages both instance
segmentation and mirror stream for the camouflaged object segmentation.
Differently from existing networks for segmentation, our proposed network
possesses two segmentation streams: the main stream and the mirror stream
corresponding with the original image and its flipped image, respectively. The
output from the mirror stream is then fused into the main stream's result for
the final camouflage map to boost up the segmentation accuracy. Extensive
experiments conducted on the public CAMO dataset demonstrate the effectiveness
of our proposed network. Our proposed method achieves 89% in accuracy,
outperforming the state-of-the-arts.
Project Page: https://sites.google.com/view/ltnghia/research/camoComment: Under Revie
Novel deep learning architectures for marine and aquaculture applications
Alzayat Saleh's research was in the area of artificial intelligence and machine learning to autonomously recognise fish and their morphological features from digital images. Here he created new deep learning architectures that solved various computer vision problems specific to the marine and aquaculture context. He found that these techniques can facilitate aquaculture management and environmental protection. Fisheries and conservation agencies can use his results for better monitoring strategies and sustainable fishing practices
Whale Detection Enhancement through Synthetic Satellite Images
With a number of marine populations in rapid decline, collecting and
analyzing data about marine populations has become increasingly important to
develop effective conservation policies for a wide range of marine animals,
including whales. Modern computer vision algorithms allow us to detect whales
in images in a wide range of domains, further speeding up and enhancing the
monitoring process. However, these algorithms heavily rely on large training
datasets, which are challenging and time-consuming to collect particularly in
marine or aquatic environments. Recent advances in AI however have made it
possible to synthetically create datasets for training machine learning
algorithms, thus enabling new solutions that were not possible before. In this
work, we present a solution - SeaDroneSim2 benchmark suite, which addresses
this challenge by generating aerial, and satellite synthetic image datasets to
improve the detection of whales and reduce the effort required for training
data collection. We show that we can achieve a 15% performance boost on whale
detection compared to using the real data alone for training, by augmenting a
10% real data. We open source both the code of the simulation platform
SeaDroneSim2 and the dataset generated through it
Determination of Biomass in Shrimp-Farm using Computer Vision
The automation in the aquaculture is proving to be more and more effective these days.
The economic drain on the aquaculture farmers due to the high mortality of the shrimps can be reduced by ensuring the welfare of the animals. The health of shrimps can decline with even barest of changes in the conditions in the farm. This is the result of increase in stress. As shrimps are quite sensitive to the changes, even small changes can increase the stress in the animals which results in the decline of health. This severely dampens the mortality rate in the animals.
Also, human interference while feeding the shrimps severely induces the stress on the shrimps and thereby affecting the shrimp’s mortality. So, to ensure the optimum
efficiency of the farm, the feeding of the shrimps is made automated. The underfeeding and overfeeding also affects the growth of shrimps. To determine the right amount of food to provide for shrimps, Biomass is a very helpful parameter.
The use of artificial intelligence (AI) to calculate the farm's biomass is the project's primary area of interest. This model uses the cameras mounted on top of the tank at densely populated areas. These cameras monitor the farm, and our model detects the biomass. By doing so, it is possible to estimate how much food should be distributed at that particular area. Biomass of the shrimps can be calculated with the help of the number of shrimps and the average lengths of the shrimps detected. With the reduced human interference in calculating the biomass, the health of the animals improves and thereby making the process sustainable and economical
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Real-time spatial modeling to detect and track resources on construction sites
For more than 10 years the U.S. construction industry has experienced over 1,000
fatalities annually. Many fatalities may have been prevented had the individuals and
equipment involved been more aware of and alert to the physical state of the environment
around them. Awareness may be improved by automatic 3D (three-dimensional) sensing
and modeling of the job site environment in real-time. Existing 3D modeling approaches
based on range scanning techniques are capable of modeling static objects only, and thus
cannot model in real-time dynamic objects in an environment comprised of moving
humans, equipment, and materials. Emerging prototype 3D video range cameras offer
another alternative by facilitating affordable, wide field of view, automated static and
dynamic object detection and tracking at frame rates better than 1Hz (real-time).
This dissertation presents an imperical work and methodology to rapidly create a
spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a
static or moving position. Existing construction site 3D modeling approaches based on
optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling
approaches (dense, sparse, etc.) that offered potential solutions for this research are
reviewed. The choice of an emerging sensing tool and preliminary experiments with this
prototype sensing technology are discussed. These findings led to the development of a
range data processing algorithm based on three-dimensional occupancy grids which is
demonstrated in detail. Testing and validation of the proposed algorithms have been
conducted to quantify the performance of sensor and algorithm through extensive
experimentation involving static and moving objects. Experiments in indoor laboratory
and outdoor construction environments have been conducted with construction resources
such as humans, equipment, materials, or structures to verify the accuracy of the
occupancy grid modeling approach. Results show that modeling objects and measuring
their position, dimension, direction, and speed had an accuracy level compatible to the
requirements of active safety features for construction. Results demonstrate that video
rate 3D data acquisition and analysis of construction environments can support effective
detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated
three-dimensional models for improved visualization, communications, and process
control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction
activities control. In combination with effective management practices, this sensing
approach has the potential to assist equipment operators to avoid incidents that result in
reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin
The Combined Use of Optical and SAR Data for Large Area Impervious Surface Mapping
One of the megatrends marking our societies today is the rapid growth of urban agglomerations which is accompanied by a continuous increase of impervious surface (IS) cover. In light of this, accurate measurement of urban IS cover as an indicator for both, urban growth and environmental quality is essential for a wide range of urban ecosystems studies. The aim of this work is to present an approach based on both optical and SAR data in order to quantify urban impervious surface as a continuous variable on regional scales. The method starts with the identification of relevant areas by a semi automated detection of settlement areas on the basis of single-polarized TerraSAR-X data. Thereby the distinct texture and the high density of dihedral corner reflectors prevailing in build-up areas are utilized to automatically delineate settlement areas by the use of an object-based image classification method. The settlement footprints then serve as reference area for the impervious surface estimation based on a Support Vector Regression (SVR) model which relates percent IS to spectral reflectance values. The training procedure is based on IS values derived from high resolution QuickBird data. The developed method is applied to SPOT HRG data from 2005 and 2009 covering almost the whole are of Can Tho Province in the Mekong Delta, Vietnam. In addition, a change detection analysis was applied in order to test the suitability of the modelled IS results for the automated detection of constructional developments within urban environments. Overall accuracies between 84 % and 91% for the derived settlement footprints and absolute mean errors below 15% for the predicted versus training percent IS values prove the suitability of the approach for an area-wide mapping of impervious surfaces thereby exclusively focusing on settlement areas on the basis of remotely sensed image data
To see and not be seen
Die vorliegende Arbeit behandelt Tarnung im Tierreich und der Menschheitsgeschichte. Um die Mechanismen zur Täuschung von Feinden und Vermeidung von Entdeckung und Attackierung besser zu verstehen, werden die Eigenschaften von visuellen Systemen unterschiedlicher Spezies und aus der Sicht verschiedener Wahrnehmer genauer betrachtet. Während der Beschäftigung damit, wie sich Feinde und Artgenossen wahrnehmen, wird ein Überblick über einige Testmaterialien gegeben, wie die Manipulation von Farbmustern oder Körperteilen eines Objektes um die Antwort eines Empfängers zu untersuchen, Manipulation des visuellen Hintergrunds, Computersimulationen und auch Szenarien zur visuellen Suche. Bereiche der visuellen Wahrnehmung welche “Camouflage breaking” beeinflussen, beinhalten unter anderem Prinzipien von Figur-Hintergrund-Segmentation, Objekterkennung und Kantenerkennung im menschlichen visuellen System, welche mit tierischen Sinnessystemen verglichen werden. Mechanismen von Tarnung und Täuschungsfärbung aus der Natur wurden auch im humanen Kontext angewendet. Beginnend mit den umfassendenen Gebieten von Kunst, Militär und “dazzle painted“ Schiffen, wird die Verbindung von Camouflage mit der menschlichen Kultur, und neuere Entwicklungen auf dem technologischen Sektor präsentiert. Trotz allen Erkenntnissen ist das Wissen um die genauen Wirkungsmechanismen von Tarnung spärlich. Durch weitere Forschung auf dem Gebiet der Interaktion von visuellen Systemen können diese jedoch genauer verstanden werden.This work focuses on camouflage in the animal kingdom and in human history. In order to gain a deeper knowledge of mechanisms for avoiding detection or attack and for deceiving predators, the properties of visual systems of different species are explored from the mind and eyes of various perceivers. While inspecting how predators and conspecifics see each other, an overview of several testing material is given, such as manipulations of color patterns and body parts of an object to examine the response of a receiver, manipulation of visual backgrounds, computer simulations and also visual search scenarios. Areas of visual perception that influence camouflage breaking include among others principles of target-background segmentation, object recognition and edge detection in the human visual system, which are compared with animal sensory systems. Mechanisms of camouflage and deceptive coloration from nature have been adopted to the human context. Starting with the broad area of art, military and dazzle painted ships, the connection of camouflage with human culture and recent developments on the technological sector is presented. Despite all that insight, knowledge of how camouflage works is spare but by further examing the interactions of visual systems we can understand perception more precisely
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