2,332 research outputs found
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
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
There are two types of information in each handwritten word image: explicit
information which can be easily read or derived directly, such as lexical
content or word length, and implicit attributes such as the author's identity.
Whether features learned by a neural network for one task can be used for
another task remains an open question. In this paper, we present a deep
adaptive learning method for writer identification based on single-word images
using multi-task learning. An auxiliary task is added to the training process
to enforce the emergence of reusable features. Our proposed method transfers
the benefits of the learned features of a convolutional neural network from an
auxiliary task such as explicit content recognition to the main task of writer
identification in a single procedure. Specifically, we propose a new adaptive
convolutional layer to exploit the learned deep features. A multi-task neural
network with one or several adaptive convolutional layers is trained
end-to-end, to exploit robust generic features for a specific main task, i.e.,
writer identification. Three auxiliary tasks, corresponding to three explicit
attributes of handwritten word images (lexical content, word length and
character attributes), are evaluated. Experimental results on two benchmark
datasets show that the proposed deep adaptive learning method can improve the
performance of writer identification based on single-word images, compared to
non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
Review of steganalysis of digital images
Steganography is the science and art of embedding hidden messages into cover multimedia such as text, image, audio and video. Steganalysis is the counterpart of steganography, which wants to identify if there is data hidden inside a digital medium. In this study, some specific steganographic schemes such as HUGO and LSB are studied and the steganalytic schemes developed to steganalyze the hidden message are studied. Furthermore, some new approaches such as deep learning and game theory, which have seldom been utilized in steganalysis before, are studied. In the rest of thesis study some steganalytic schemes using textural features including the LDP and LTP have been implemented
Textural Difference Enhancement based on Image Component Analysis
In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms.
We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image.
We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly.
We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics.
Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics.
The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method
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