Elastic distortion of fingerprints has a negative effect on
the performance of fingerprint recognition systems. This
negative effect brings inconvenience to users in authentication
applications. However, in the negative recognition
scenario where users may intentionally distort their fingerprints,
this can be a serious problem since distortion will
prevent recognition system from identifying malicious users.
Current methods aimed at addressing this problem still have
limitations. They are often not accurate because they estimate
distortion parameters based on the ridge frequency
map and orientation map of input samples, which are not
reliable due to distortion. Secondly, they are not efficient
and requiring significant computation time to rectify samples.
In this paper, we develop a rectification model based
on a Deep Convolutional Neural Network (DCNN) to accurately
estimate distortion parameters from the input image.
Using a comprehensive database of synthetic distorted
samples, the DCNN learns to accurately estimate distortion
bases ten times faster than the dictionary search methods
used in the previous approaches. Evaluating the proposed
method on public databases of distorted samples shows that
it can significantly improve the matching performance of
distorted samples.
more »
« less
Automatic target recognition using deep convolutional neural networks
In this paper, we propose a new Automatic Target Recognition (ATR) system, based on Deep Convolutional Neural Network (DCNN), to detect the targets in Forward Looking Infrared (FLIR) scenes and recognize their classes. In our proposed ATR framework, a fully convolutional network (FCN) is trained to map the input FLIR imagery data to a fixed stride correspondingly-sized target score map. The potential targets are identified by applying a threshold on the target score map. Finally, corresponding regions centered at these target points are fed to a DCNN to classify them into different target types while at the same time rejecting the false alarms. The proposed architecture achieves a significantly better performance in comparison with that of the state-of-the-art methods on two large FLIR image databases.
more »
« less
- Award ID(s):
- 1650474
- PAR ID:
- 10091236
- Date Published:
- Journal Name:
- SPIE Defense + Security 2018
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Interstitial lung disease (ILD) causes pulmonary fibrosis. The correct classification of ILD plays a crucial role in the diagnosis and treatment process. In this research work, we propose a lung nodules recognition method based on a deep convolutional neural network (DCNN) and global features, which can be used for computer-aided diagnosis (CAD) of global features of lung nodules. Firstly, a DCNN is constructed based on the characteristics and complexity of lung computerized tomography (CT) images. Then we discussed the effects of different iterations on the recognition results and influence of different model structures on the global features of lung nodules. We also incorporated the improvement of convolution kernel size, feature dimension, and network depth. Thirdly, the effects of different pooling methods, activation functions and training algorithms we proposed has been analyzed to demonstrate the advantages of the new strategy. Finally, the experimental results verify the feasibility of the proposed DCNN for CAD of global features of lung nodules, and the evaluation shown that our proposed method could achieve an outstanding results compare to state-of-arts.more » « less
-
The number of Circulating Tumor Cells (CTCs) in blood indicates the tumor response to chemotherapeutic agents and disease progression. In early cancer diagnosis and treatment monitoring routine, detection and enumeration of CTCs in clinical blood samples have significant applications. In this paper, we design a Deep Convolutional Neural Network (DCNN) with automatically learned features for image-based CTC detection. We also present an effective training methodology which finds the most representative training samples to define the classification boundary between positive and negative samples. In the experiment, we compare the performance of auto-learned feature from DCNN and hand-crafted features, in which the DCNN outperforms hand-crafted feature. We also prove that the proposed training methodology is effective in improving the performance of DCNN classifiers.more » « less
-
Annotating automatic target recognition images is challenging; for example, sometimes there is labeled data in the source domain but no labeled data in the target domain. Therefore, it is essential to construct an optimal target domain classifier using the labeled information of the source domain images. For this purpose, we propose a transductive transfer learning (TTL) network consisting of an unpaired domain translation network, a pretrained source domain classifier, and a gradually constructed target domain classifier. We delve into the unpaired domain translation network, which simultaneously optimizes cycle consistency and modulated noise contrastive losses (MoNCE). Furthermore, the proposed hybrid CUT module integrated into the TTL network generates synthetic negative patches by noisy features mixup, and all the negative patches provide modulated weight into the NCE loss by considering similarity to the query. Apart from that, this hybrid CUT network considers query selection by entropy-based attention to specifying domain variants and invariant regions. The extensive analysis depicted that the proposed transductive network can successfully annotate civilian, military vehicles, and ship targets into the three benchmark ATR datasets. We further demonstrate the importance of each component of the TTL network through extensive ablation studies into the DSIAC dataset.more » « less
-
As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2304643
