Recently, sparse representation has been utilized in many computer vision tasks and adapted for visual tracking. Sparsity-based visual tracking is formulated as searching candidates with minimal reconstruction errors ...
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Advances in imaging devices and web technologies have brought dramatic improvements in collecting, storing, and sharing images. The leakage of privacy information in the process becomes an important issue that has sta...
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Advances in imaging devices and web technologies have brought dramatic improvements in collecting, storing, and sharing images. The leakage of privacy information in the process becomes an important issue that has started drawing attention from both academia and industry. In this work, we study the problem of privacy preserving with focus on license plate number protecting in imagery. Specifically, we present a novel method for de-identifying license plate images with the least degradation in image visual quality for privacy protection. Unlike previous de-identification methods that pay little attention to the image quality preservation, our method, named inhomogeneous principal component blur (IPCB), adaptively blurs different pixels of a license plate by taking into account the prior distribution of sensitive information. We tested the proposed method on a public dataset in comparison with several popular de-identification methods. The evaluation shows that our method successfully de-identified the privacy information with the least damage of image quality when compared with several other solutions.
Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the...
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ISBN:
(纸本)9781424441211
Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the key landmarks to construct the midsagittal reference plane. In this paper, we propose a learning-based approach to automatically detect the Dent-landmark in the 3D cone-beam computed tomography (CBCT) dental data. Specifically, a detector is learned using the random forest with sampled context features. Furthermore, we use spacial prior to build a constrained search space other than use the full three dimensional space. The proposed method has been evaluated on a dataset containing 73 CBCT dental volumes and yields promising results.
In this paper we propose using a learning-based method for vessel segmentation in mammographic images. To capture the large variation in vessel patterns not only across subjects, but also within a subject, we create a...
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In this paper we propose using a learning-based method for vessel segmentation in mammographic images. To capture the large variation in vessel patterns not only across subjects, but also within a subject, we create a feature pool containing local, Gabor and Haar features extracted from mammographic images generating a feature space of very high dimension. We also employ a huge number of training samples, which essentially contains the pixels in the training images. To deal with the very high dimensional feature space and the huge number of training samples, we apply a forest with boosting trees for vessel segmentation. Specifically, we use the standard AdaBoost algorithm for each tree in the forest. The randomness is encoded, when training each AdaBoost tree, using randomly sampled training set (pixels) and randomly selected features from the whole feature pool. The proposed method is tested using a real dataset with 20 anonymous mammographic images. The effectiveness of the proposed features and classifiers is demonstrated in the experiments where we compare different approaches and feature combinations. In the paper, we also present full analysis of different types of features.
For Wireless Body Area Networks (WBANs), the security of sensitive data of patients is of the utmost importance, particularly in healthcare environments. This study presents a novel methodology for improving the effic...
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For Wireless Body Area Networks (WBANs), the security of sensitive data of patients is of the utmost importance, particularly in healthcare environments. This study presents a novel methodology for improving the efficacy of signature aggregation in a scenario involving doctors and patients while mitigating concerns about location privacy. Though there have been prior proposals for signature aggregation schemes, the proposed approach seeks to optimize the aggregation process within the considered scenario, thereby improving performance and reducing computational and communication burden. In addition, the proposed scheme integrates a resilient mechanism that safeguards the doctor’s location privacy by utilizing the Chinese Remainder Theorem (CRT). Advanced cryptographic algorithms and location-anonymization techniques are employed in the proposed method to safeguard the confidentiality of the doctors’ location. The security of the proposed scheme is formally analyzed using the Burrows-Abadi-Needham (BAN) logic and formally verified using the automated software validation tool, known as the Scyther tool, and an informal analysis of various security attributes confirms the security robustness of the proposed scheme. The efficacy is evaluated in comparison to analogous works utilizing the Cygwin software. The performance evaluation shows that the proposed scheme has lower communication costs as compared to existing competing schemes. Moreover, the serving ratio in the proposed scheme is high even if the number of patients is low for doctors.
This book constitutes the refereed proceedings of the 17th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2022, held in Salamanca, Spain, in September 2022.;The 43 full papers presented in thi...
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ISBN:
(数字)9783031154713
ISBN:
(纸本)9783031154706
This book constitutes the refereed proceedings of the 17th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2022, held in Salamanca, Spain, in September 2022.;The 43 full papers presented in this book were carefully reviewed and selected from 67 submissions. They were organized in topical sections as follows: bioinformatics; data mining and decision support systems; deep learning; evolutionary computation; HAIS applications; image and speech signal processing; and optimization techniques.
The rise of social media has led to vast amounts of user-generated content, with emotions ranging from joy to anger. Negative comments often target individuals, communities, or brands, prompting successful efforts to ...
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The rise of social media has led to vast amounts of user-generated content, with emotions ranging from joy to anger. Negative comments often target individuals, communities, or brands, prompting successful efforts to detect harmful speech such as hate speech, cyberbullying, and abuse. Recently, another type of speech referred to as ‘Hope Speech’ has gained attention from the research community. Hope speech consists of positive affirmations or words of reassurance, encouragement, consolation or motivation offered to the affected individual/ community during the lean periods of life. However, there has been relatively less research focused on the detection of hope speech, more particularly in low-resource languages. This paper, therefore, attempts to develop an ensemble model for detecting hope speech in some low-resource languages. data for four different languages, namely English, Kannada, Malayalam and Tamil are obtained and experimented with different deep learning-based models. An ensemble model is proposed to combine the advantages of the better performing models. Experimental results demonstrate the superior performance of the proposed Ensemble (LSTM, mBERT, XLM-RoBERTa) model compared to individual models based on data from all four languages (weighted average F1-score for English is 0.93; for Kannada is 0.74; for Malayalam is 0.82; and for Tamil is 0.60). Thus, the proposed ensemble model proves to be a suitable approach for hope speech detection in the given low resource languages.
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