9 research outputs found
Survey of Object Detection Methods in Camouflaged Image
Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection
methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this
research paper authors presented survey of camouflage detection methods for different applications and areas
Camouflage assessment:Machine and human
A vision model is designed using low-level vision principles so that it can perform as a human observer model for camouflage assessment. In a camouflaged-object assessment task, using military patterns in an outdoor environment, human performance at detection and recognition is compared with the human observer model. This involved field data acquisition and subsequent image calibration, a human experiment, and the design of the vision model. Human and machine performance, at recognition and detection, of military patterns in two environments was found to correlate highly. Our model offers an inexpensive, automated, and objective method for the assessment of camouflage where it is impractical, or too expensive, to use human observers to evaluate the conspicuity of a large number of candidate patterns. Furthermore, the method should generalize to the assessment of visual conspicuity in non-military contexts.</p
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
Explicit Visual Prompting for Universal Foreground Segmentations
Foreground segmentation is a fundamental problem in computer vision, which
includes salient object detection, forgery detection, defocus blur detection,
shadow detection, and camouflage object detection. Previous works have
typically relied on domain-specific solutions to address accuracy and
robustness issues in those applications. In this paper, we present a unified
framework for a number of foreground segmentation tasks without any
task-specific designs. We take inspiration from the widely-used pre-training
and then prompt tuning protocols in NLP and propose a new visual prompting
model, named Explicit Visual Prompting (EVP). Different from the previous
visual prompting which is typically a dataset-level implicit embedding, our key
insight is to enforce the tunable parameters focusing on the explicit visual
content from each individual image, i.e., the features from frozen patch
embeddings and high-frequency components. Our method freezes a pre-trained
model and then learns task-specific knowledge using a few extra parameters.
Despite introducing only a small number of tunable parameters, EVP achieves
superior performance than full fine-tuning and other parameter-efficient
fine-tuning methods. Experiments in fourteen datasets across five tasks show
the proposed method outperforms other task-specific methods while being
considerably simple. The proposed method demonstrates the scalability in
different architectures, pre-trained weights, and tasks. The code is available
at: https://github.com/NiFangBaAGe/Explicit-Visual-Prompt.Comment: arXiv admin note: substantial text overlap with arXiv:2303.1088
Agent-based framework for person re-identification
In computer based human object re-identification, a detected human is recognised to a
level sufficient to re-identify a tracked person in either a different camera capturing the
same individual, often at a different angle, or the same camera at a different time and/or
the person approaching the camera at a different angle. Instead of relying on face
recognition technology such systems study the clothing of the individuals being monitored
and/or objects being carried to establish correspondence and hence re-identify the human
object.
Unfortunately present human-object re-identification systems consider the entire human
object as one connected region in making the decisions about similarity of two objects
being matched. This assumption has a major drawback in that when a person is partially
occluded, a part of the occluding foreground will be picked up and used in matching. Our
research revealed that when a human observer carries out a manual human-object re-identification
task, the attention is often taken over by some parts of the human
figure/body, more than the others, e.g. face, brightly colour shirt, presence of texture
patterns in clothing etc., and occluding parts are ignored.
In this thesis, a novel multi-agent based framework is proposed for the design of a human
object re-identification system. Initially a HOG based feature extraction is used in a SVM
based classification of a human object as a human of a full-body or of half body nature.
Subsequently the relative visual significance of the top and the bottom parts of the human,
in re-identification is quantified by the analysis of Gray Level Co-occurrence based
texture features and colour histograms obtained in the HSV colour space. Accordingly
different weights are assigned to the top and bottom of the human body using a novel
probabilistic approach. The weights are then used to modify the Hybrid Spatiogram and
Covariance Descriptor (HSCD) feature based re-identification algorithm adopted.
A significant novelty of the human object re-identification systems proposed in this thesis
is the agent based design procedure adopted that separates the use of computer vision
algorithms for feature extraction, comparison etc., from the decision making process of re-identification. Multiple agents are assigned to execute different algorithmic tasks and
the agents communicate to make the required logical decisions.
Detailed experimental results are provided to prove that the proposed multi agent based
framework for human object re-identification performs significantly better than the state of-the-art algorithms. Further it is shown that the design flexibilities and scalabilities of
the proposed system allows it to be effectively utilised in more complex computer vision
based video analytic/forensic tasks often conducted within distributed, multi-camera
systems
Mrna Granules And Ischemic Preconditioning
Brain ischemia and reperfusion that occurs after stroke and cardiac arrest, causes translation arrest (TA) in neurons which is irreversible in neurons that will undergo delayed neuronal death. TA is linked to mRNA granules, which are involved in ischemia-induced stress genes translation. Ischemic preconditioning (IPC) is the most protective response known that protects neurons from a lethal ischemic insult. In this thesis I studied the effects of (1) sublethal durations of ischemia, (2) IPC and (3) cycloheximide (CHX) on the formation of mRNA granules at 1 hour of reperfusion and the colocalization of HuR in the mRNA granules. All durations tested, from 2 to 8 min ischemia caused formation of mRNA granules, and HuR colocalized in the mRNA granules at lower ischemia durations in CA1 neurons. However, IPC appeared to attenuate the formation of mRNA granules at 1 hour reperfusion and did not enhance HuR colocalization. CHX inhibited mRNA granules at 1 hour reperfusion, but had no effect in animals subjected to prior IPC. These results show that sublethal durations of ischemia cause CA1 neurons to behave similarly to CA3 neurons. However, the mRNA granule response appears to be less important after a 10 min ischemia in preconditioned animals. These results show that the 10 min ischemia period is perceived differently by the preconditioned CA1 neuron compared to a non-preconditioned neuron. Moreover, these results shed important light on the post-ischemic neuronal response, and will help in the effort to develop effective therapies to protect against stroke and cardiac arrest brain damage
Remote Sensing of Earth Resources: A literature survey with indexes (1970 - 1973 supplement). Section 1: Abstracts
Abstracts of reports, articles, and other documents introduced into the NASA scientific and technical information system between March 1970 and December 1973 are presented in the following areas: agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis