The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit...
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Human gesture recognition has drawn much attention in the area of computervision. However, the performance of gesture recognition is always influenced by some gesture-irrelevant factors like the background and the cl...
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Recently developed sophisticated image processing techniques and tools have made easier the creation of high-quality forgeries of handwritten documents including financial and property records. To detect such forgerie...
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Recently developed sophisticated image processing techniques and tools have made easier the creation of high-quality forgeries of handwritten documents including financial and property records. To detect such forgeries of handwritten documents, this paper presents a new method by exploring the combination of Chebyshev-Harmonic-Fourier-Moments (CHFM) and deep Convolutional Neural Networks (D-CNNs). Unlike existing methods work based on abrupt changes due to distortion created by forgery operation, the proposed method works based on inconsistencies and irregular changes created by forgery operations. Inspired by the special properties of CHFM, such as its reconstruction ability by removing redundant information, the proposed method explores CHFM to obtain reconstructed images for the color components of the Original, Forged Noisy and Blurred classes. Motivated by the strong discriminative power of deep CNNs, for the reconstructed images of respective color components, the proposed method used deep CNNs for forged handwriting detection. Experimental results on our dataset and benchmark datasets (namely, ACPR 2019, ICPR 2018 FCD and IMEI datasets) show that the proposed method outperforms existing methods in terms of classification rate.
Person images captured by surveillance cameras are often occluded by various obstacles, which lead to defective feature representation and harm person re-identification (Re-ID) performance. To tackle this challenge, w...
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The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks poses a significant challenge to their deployment in safety-critical applications. While extensive research has addressed various attack scenario...
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Use of handwriting words for person identification in contrast to biometric features is gaining importance in the field of forensic applications. As a result, forging handwriting is a part of crime applications and he...
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Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functio...
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Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNNAM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging
The spatial resolution of remotely sensed images has seen significant improvements; higher resolution facilitates the understanding of remote sensing images and improves the accuracy of building footprint extraction. ...
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The spatial resolution of remotely sensed images has seen significant improvements; higher resolution facilitates the understanding of remote sensing images and improves the accuracy of building footprint extraction. Thus, this paper introduces a novel approach that combines super-resolution reconstruction with semantic segmentation deep models for building footprint extraction. To begin, a super-resolution reconstruction deep model is employed to enhance the resolution of the target images. Subsequently, training samples are generated using various data augmentation techniques based on the reconstructed images. Finally, a semantic segmentation deep model is trained to perform the final building footprint extraction. For validation, our proposed framework was tested on two publicly available aerial image datasets: the WHU building dataset and the Massachusetts building dataset. The results obtained from these datasets demonstrated the effectiveness of our method, achieving high Intersection over Union (IoU) scores of 90.57% and 75.02%, respectively. Furthermore, our approach outperformed seven other competing methods, showcasing its superior performance.
Due to change in mindset and living style of humans, the numbers of diversified marriages are increasing all around the world irrespective of race, color, religion and culture. As a result, it is challenging for resea...
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Recently, many forensic techniques have been developed to detect the use of a certain processing operation. When utilizing several manipulations to alter an image, artifacts left by manipulations that have been applie...
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Recently, many forensic techniques have been developed to detect the use of a certain processing operation. When utilizing several manipulations to alter an image, artifacts left by manipulations that have been applied later can potentially disguise traces left by manipulations that were applied earlier. Therefore, the detection of manipulations become difficult. In this paper, we focus on identifying the manipulations in an image operation chain composed of multiple manipulations in a certain order. To address this issue, we analyze the relationship between manipulations identification and blind signal separation. Then, we propose a features decoupling method based on blind signal separation, which decouples the coupled features due to the superimposed processing artifacts and exploits the decoupled features to identify multiple operations. The experiments carried out on two image operation chains confirm the effectiveness of the proposed method.
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