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- 저자 : Aun Irtaza (검색결과 4 건)
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Mahmood, Toqeer,Irtaza, Aun,Mehmood, Zahid,Tariq Mahmood, Muhammad Elsevier Sequoia 2017 Forensic science international Vol.279 No.-
<P><B>Abstract</B></P> <P>The most common image tampering often for malicious purposes is to copy a region of the same image and paste to hide some other region. As both regions usually have same texture properties, therefore, this artifact is invisible for the viewers, and credibility of the image becomes questionable in proof centered applications. Hence, means are required to validate the integrity of the image and identify the tampered regions. Therefore, this study presents an efficient way of copy-move forgery detection (CMFD) through local binary pattern variance (LBPV) over the low approximation components of the stationary wavelets. CMFD technique presented in this paper is applied over the circular regions to address the possible post processing operations in a better way. The proposed technique is evaluated on CoMoFoD and Kodak lossless true color image (KLTCI) datasets in the presence of translation, flipping, blurring, rotation, scaling, color reduction, brightness change and multiple forged regions in an image. The evaluation reveals the prominence of the proposed technique compared to state of the arts. Consequently, the proposed technique can reliably be applied to detect the modified regions and the benefits can be obtained in journalism, law enforcement, judiciary, and other proof critical domains.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A copy-move forgery detection technique for forensic analysis in digital images is proposed. </LI> <LI> The technique utilizes LBPV based features extracted from the overlapping blocks of approximation sub-band of SWT. </LI> <LI> The proposed technique performed precisely for various image post-processing operations. </LI> </UL> </P>
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Fall detection through acoustic Local Ternary Patterns
Adnan, Syed M.,Irtaza, Aun,Aziz, Sumair,Ullah, M. Obaid,Javed, Ali,Mahmood, Muhammad Tariq Elsevier 2018 Applied acoustics Vol.140 No.-
<P><B>Abstract</B></P> <P>In this paper, we propose a framework that detects falls by using acoustic Local Ternary Patterns (acoustic-LTPs) by analyzing environmental sounds. The proposed method suppresses silence zones in sound signals and distinguishes overlapping sounds. Acoustic features are extracted from the Separated source components by using the proposed acoustic-LTPs. Subsequently, fall events are detected through a support vector machine (SVM) based classifier. The performance of the proposed descriptor is evaluated against state-of-the-art methods that are applied on well-known sound databases. A comparative analysis demonstrates that the proposed descriptor is more powerful and reliable in terms of fall detection than other methods, and it also performs well in a multi-class environment. Moreover, the proposed descriptor possesses a rotation invariant property, and therefore, it demonstrates significant resistance against the rotated sound signals.</P>
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Plants Disease Phenotyping using Quinary Patterns as Texture Descriptor
( Wakeel Ahmad ),( S. M. Adnan Shah ),( Aun Irtaza ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.8
Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0o, 45o, 90o, and 135o directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.
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Video augmentation technique for human action recognition using genetic algorithm
Nudrat Nida,Muhammad Haroon Yousaf,Aun Irtaza,Sergio A. Velastin 한국전자통신연구원 2022 ETRI Journal Vol.44 No.2
Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi-Uncut, iXMAS, and IAVID-1 datasets.
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