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A preliminary approach to intelligent x-ray imaging for baggage inspection at airports
Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted
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μ£Ό.Biometric authentication using fingerprints requires a method for image denoising and inpainting to extract fingerprints from degraded fingerprint images. A few deep learning models for fingerprint image denoising and inpainting were proposed in ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018. In this thesis, a new deep learning model for fingerprint image denoising is proposed. The proposed model is adapted from FusionNet, which is a convolutional neural network based deep learning model for image segmentation. The performance of the proposed model was demonstrated using the dataset from the ECCV 2018 ChaLearn Competition. It was shown that the proposed model obtains better results compared with the models that achieved high performances in the competition.μ§λ¬Έμ μ¬μ©ν μ체 μΈμ μΈμ¦μ νμ§μ΄ μ νλ μ§λ¬Έ μμμμ μ§λ¬Έμ μΆμΆνκΈ° μν μμ μ‘μ μ κ±° λ° λ³΅μ λ°©λ²μ νμλ‘ νλ€. μ§λ¬Έ μμ μ‘μ μ κ±° λ° λ³΅μμ μν λͺ κ°μ§ λ₯λ¬λ λͺ¨λΈμ΄ ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018μμ μ μλμλ€. λ³Έ λ
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μ μ¬μ©νμ¬ κ²μ¦λμλ€. μ΄λ₯Ό ν΅ν΄ μ μλ λͺ¨λΈμ΄ λνμμ λμ μ±λ₯μ νλν λ€λ₯Έ λͺ¨λΈλ€μ λΉνμ¬ λ λμ κ²°κ³Όλ₯Ό μ»μμ νμΈνμλ€.Abstract i
Contents ii
1 Introduction 1
2 Related Work 3
2.1 Residual Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Convolutional Neural Networks for Semantic Segmentation . . . . . . 4
2.2.1 U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2.2 FusionNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Recent Trends in Fingerprint Image Denoising . . . . . . . . . . . . . 6
3 Proposed Model 7
3.1 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Architecture Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2.1 Residual Block . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2.2 Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2.3 Bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2.4 Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Experiments 13
4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.4.1 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.4.2 Comparison with Other Models . . . . . . . . . . . . . . . . 17
5 Conclusion 21
Abstract (In Korean)Maste
A Translation And Rotation Independent Fingerprint Identification Approach
This thesis describes a new approach for fingerprint identification that will be shift and rotation independent. Detailed descriptions of directional filtering, foreground and background segmentation, feature extraction, and matching based on structural correlation are the main topics of this thesis. The fingerprint identification system consists of image preprocessing, feature extraction, and matching which run on a PC platform. The preprocessing step includes histogram equalization, block-based directional filtering, thinning, and adaptive thresholding to enhance the original images for successful feature extraction. The features extracted will be stored in the database for matching. The matching algorithm presented is a modification and improvement of the structural approach. A two-step process of local feature matching and global feature matching guarantees the correct matching results
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