Browsing by Author "Mehbodniya, Abolfazl"
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Item A Transformer-Based Framework for Scene Text Recognition(Institute of Electrical and Electronics Engineers Inc., 2022) Selvam, Prabu; Sundar Koilraj, Joseph Abraham; Tavera Romero, Carlos Andres; Alharbi, Meshal; Mehbodniya, AbolfazlScene Text Recognition (STR) has become a popular and long-standing research problem in computer vision communities. Almost all the existing approaches mainly adopt the connectionist temporal classification (CTC) technique. However, these existing approaches are not much effective for irregular STR. In this research article, we introduced a new encoder-decoder framework to identify both regular and irregular natural scene text, which is developed based on the transformer framework. The proposed framework is divided into four main modules: Image Transformation, Visual Feature Extraction (VFE), Encoder and Decoder. Firstly, we employ a Thin Plate Spline (TPS) transformation in the image transformation module to normalize the original input image to reduce the burden of subsequent feature extraction. Secondly, in the VFE module, we use ResNet as the Convolutional Neural Network (CNN) backbone to retrieve text image features maps from the rectified word image. However, the VFE module generates one-dimensional feature maps that are not suitable for locating a multi-oriented text on two-dimensional word images. We proposed 2D Positional Encoding (2DPE) to preserve the sequential information. Thirdly, the feature aggregation and feature transformation are carried out simultaneously in the encoder module. We replace the original scaled dot-product attention model as in the standard transformer framework with an Optimal Adaptive Threshold-based Self-Attention (OATSA) model to filter noisy information effectively and focus on the most contributive text regions. Finally, we introduce a new architectural level bi-directional decoding approach in the decoder module to generate a more accurate character sequence. Eventually, We evaluate the effectiveness and robustness of the proposed framework in both horizontal and arbitrary text recognition through extensive experiments on seven public benchmarks including IIIT5K-Words, SVT, ICDAR 2003, ICDAR 2013, ICDAR 2015, SVT-P and CUTE80 datasets. We also demonstrate that our proposed framework outperforms most of the existing approaches by a substantial margin.Item Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM(Tech Science Press, 2022) Priyadarsini S.; Tavera Romero, Carlos Andres; Mehbodniya, Abolfazl; Sagar, P. Vidya; Sengan, SudhakarIn the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging. The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation. The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary. Also, this manual technique requires a large number of image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-Angle Texture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from its neighbouring soft tissues with related strength. Then, the filtered images are given as the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection (CED) technique, which segments the Liver Region (LR) from the given CT images. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error (VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applying the MAT-ACM, which accurately segments the LR from the segmented LRs. The evaluation of the proposed method is compared with the existing LTS methods using various performance measures to prove the superiority of the proposed MAT-ACM method.Item Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky(Institute of Electrical and Electronics Engineers Inc., 2022) Karn, Arodh Lal; Tavera Romero, Carlos Andres; Sengan, Sudhakar; Mehbodniya, Abolfazl; Webber, Julian L.; Pustokhin, Denis A.; Wende, Frank-DetlefThe Sloan Digital Sky Survey (SDSS) comprises about one billion objects classified spectrometrically. Because astronomical datasets are so enormous, manually classifying them is nearly impossible - a huge dataset results in class imbalance and overfitting. We recommend a framework in this research study that overcomes these constraints. The framework uses a hybrid Synthetic Minority Oversampling Technique + Edited Nearest Neighbor (SMOTE + ENN) balancer. The balanced dataset is then used to extract features via a non-linear algorithm using Kernel Principal Component Analysis (KPCA). The features are then passed into the proposed Int-T2-Fuzzy Support Vector Machine classifier, which uses a modified type reducer and inference engine to achieve more precise categorization. Using the Sloan Digital Sky Survey dataset and a number of evaluation metrics, the SMOTE+ENN model's performance is measured. The research shows that the model does a good job.