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U-NET based CT Image reconstruction and segmentation using Deep Learning Layer structure

U-NET based CT Image reconstruction and segmentation using D...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Abbasi, Altamash Ahmad Farooqi, Ashfaq Hussain Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331510503

CT imaging is critical for medical diagnostics, yet traditional reconstruction methods often struggle with noise and artifact reduction while maintaining image clarity. In this research work, an advanced methodology for computed tomography image reconstruction leveraging a U-Net model, combined with preprocessing through bilateral filtering and subsequent image segmentation. U-Net model is trained to reconstruct high-fidelity CT images from sparse-view data, ensuring superior detail preservation and artifact minimization. Our approach also incorporates a bilateral filter during preprocessing to enhance edge preservation and reduce noise. Post-reconstruction, we employ segmentation to isolate and highlight critical anatomical structures, further improving the interpretability and diagnostic value of the images. The de-noised image is then segmented using thresholding scheme. 2016 AAPM dataset has been used to check the effectiveness of the proposed methodology, demonstrating significant improvements in image quality metrics compared to conventional reconstruction techniques. Our results indicate that the integration of bilateral filtering and U-Net-based reconstruction, followed by precise segmentation, offers a robust solution for enhancing CT imaging quality. Our proposed approach has obtained PSNR of 30.461, NMSE of 0.036 and SSIM of 0.863. This approach holds substantial potential for clinical applications, providing clearer, more accurate images for diagnosis and treatment planning. © 2024 IEEE.

关键词： Image reconstruction

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Effect of Varied datasets on Training of a Segmentation Model Used in Visual Navigation

Effect of Varied Datasets on Training of a Segmentation Mode...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Wada, Marin Adachi, Miho Miyamoto, Ryusuke Graduate School of Science and Technology Dept. of Computer Science Japan Meiji University School of Science and Technology Dept. of Computer Science Kawasaki Japan

ISBN: (纸本)9798350324457

A visual navigation method based on results of semantic segmentation showed interesting results in previous researches. The most significant problem of the method is that the moving performance is affected by the segmentation accuracy, which strongly depends on the training data even though SOTA methods are adopted. To create high-quality dataset for this application without huge human efforts, the authors tries to generate datasets for semantic segmentation from a 3D scanned data composed of colored point clouds. In this study, we investigate what kinds of variations are effective to construct a classifier: variation of augmentation considering shadows, shooting angles, and shooting locations. Experimental results using actual images for evaluation and generated dataset for training captured at the course of Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that adding shadows in training datasets improved the mIoU but random changes to the shooting angle and location did not always work well. By the result, it is shown that augmentation considering the characteristic of the target environment becomes important for practical use. © 2023 IEEE.

关键词： 3D scanned data dataset generation semantic segmentation visual navigation

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Revolutionizing Leaf Classification for Agricultural Sustainability and Plant Species Analysis

Revolutionizing Leaf Classification for Agricultural Sustain...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Mumtaz, Sara Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504465

Agricultural production is critical for economic stability and food security, consequently early disease identification is essential for preserving plant health. Our study solves this need by incorporating Random Forest into a complete framework for leaf categorization. We recommend applying Particle Swarm Optimization (PSO) to construct an AI-driven, four-step procedure for this type of segregation. Crop images may be processed by preprocessing, segmenting, extracting features, and classifying them with Random Forest. During pre-processing, a Median Filter was applied, proceeded by thresholding for segmentation, eventually Kaze and HOG (Histogram of Oriented Gradient) were used to extract relevant features. A Random Forest classifier that has been optimized with PSO was used for classification. The random forest-based technique outperformed alternative methods such as naive-bayes and adaboost with an accuracy of 92.63. © 2024 IEEE.

关键词： Median filters

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Robust Machine Learning Approach to Plant Species Classification for Sustainable Agriculture 3

Robust Machine Learning Approach to Plant Species Classifica...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Mumtaz, Sara Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Sustaining resilient agricultural practices is essential for the overall yield because it guarantees both economic stability and food supplies. By incorporating Zero-Shot learning into a strong leaf classification architecture, our method meets this demand. We optimized an Al-powered four-step categorization process using the Bee Colony Algorithm. To pre-process, segment, extract features from, and classify leaf image data, zero-shot learning is employed. Pre-processing, which includes histogram equalization and contrast stretching, is followed by morphological segmentation. In feature extraction, two methods are used: Harris corner detection and SURF (Speeded Up Robust Features). Zero-Shot learning for classification is optimized using the Bee Colony Algorithm. At 92.73% accuracy, the suggested method surpasses out previous methods like CNN and SVM, suggesting that it has the potential to enhance crop quality and disease control in agriculture. ©2024 IEEE.

关键词： Zero-shot learning

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Pattern Analytics of Healthy and Diseased Leaves Recognition using Genetic Algorithm 5

Pattern Analytics of Healthy and Diseased Leaves Recognition...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Abbasi, Altamash Ahmad Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Farming techniques have been reorganized with the involvement of Artificial Intelligence. Smart and Precision agriculture has improved the yield and provided healthy crops as compared to the traditional measures involving chemicals for removal of weed. One of the steps in maintaining quality of yield is to segregate healthy crop from diseased one. We propose utilization of genetic algorithm for deploying an AI based four-step method for such segregation. Images of crop can be utilized for this purpose by employing pre-processing, followed by segmentation, extracting the useful feature and finally classification, which in this case is based on Genetic Algorithm. Groundnut, pepper, tomato and potato leaves have been utilized during the work by employing the proposed technique at two distinct datasets involving various stages of diseased. Images of size $256 \times 256$ have been utilized. Median Filter was deployed during pre-processing, k-means for segmentation, while Kaze and HOG (Histogram of Oriented Gradient) were chosen for extracting selected features. Classification was carried out using genetic algorithm. Results have been compared to random-forest, adaboost and naive-bayes, whereby our results show that proposed approach outperforms other methods by providing an accuracy of 81% on both datasets. © 2024 IEEE.

关键词： Genetic algorithms

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Efficient Aerial Images Algorithms over Multi-objects labeling and Semantic Segmentation 5

Efficient Aerial Images Algorithms over Multi-objects labeli...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Naseer, Aysha Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Aerial imagery is gaining significance across various domains, comprising agriculture, disaster management, and surveillance. Durin this study, a unique method to tackle the persistent problems in accurate and efficient multi-object recognition in aerial imagery has been introduced. Our suggested approach combines the abstraction of spectral and spatial texture details with the incorporation of a color space segmentation algorithm. We convert the original data into a single feature space by utilizing multiple kernel learning (MKL), which improves feature representation accuracy and, in turn, improves classification accuracy. Finally, various objects in aerial images may be recognized and categorized according to the application of markov random field. By addressing variances in illumination, orientation, and size, this novel approach seeks to provide a complete solution for improving multi-object recognition in aerial data. Our findings demonstrate a significant improvement in object detection accuracy 98.13%. © 2024 IEEE.

关键词： Object recognition

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Plant Disease Detection using Image Processing and Generative Adversarial Network 14

Plant Disease Detection using Image Processing and Generativ...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Teresha, Y. Shaji, Nikhil John Jarial, Manav Mirjankar, Ved Vishal Patil, Swetha Computer Science Engineering Dept. PES University Bengaluru India

ISBN: (纸本)9798331531935

Agriculture is a key contributor to the GDP of our country, India. Plants play a very important role in the ecosystem and plant diseases can negatively impact the agricultural industry by causing huge losses to the food supply. Plant leaves are prone to a variety of diseases, including blights, leaf spots, and other infections. Therefore, identifying diseases efficiently is crucial to pave the way for sustainable agriculture. In the field of agriculture, traditional and manual methods for detecting diseases in plants have sometimes been found to be inefficient and error prone. We therefore present a novel solution to these issues by introducing a method to automate plant disease detection in the Tomato plant leaf by using advanced Convolutional Neural Network (CNN) frameworks, image processing libraries and Generative Adversarial Networks (GANs). The pipeline of our work involves preprocessing plant image datasets, generating synthetic images using GANs, segmenting region of interests via a U-Net model, and classifying diseases with a CNN-based classifier. A deployed model ensures accurate disease identification from user input. © 2025 IEEE.

关键词： Photointerpretation

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Gender Classification from Thermal Images using CNN

Gender Classification from Thermal Images using CNN

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Gurram, Harika Kethineni, Keerthi Kommineni, Deva Harshini Soumya, Kakani VRSEC Dept. Computer Science & Engineering Vijayawada India

ISBN: (纸本)9798350361537

In this study, two deep learning architectures - AlexNet and InceptionV3 - are used to evaluate gender categorization using thermal imagery. One of the most promising techniques for classifying gender is thermal imaging, which offers distinct insights into physiological traits. Divided into separate training and testing subsets, the dataset is made up of carefully preprocessed thermal pictures. InceptionV3, known for its complex design combining inception modules, and AlexNet, a pioneering convolutional neural network (CNN), are refined on the training data to adjust to the complexities of gender categorization. Then, in order to measure the performance of the trained models, they are rigorously evaluated on the testing set. Specifically, their accuracy is evaluated. Experimental findings show that InceptionV3 performs better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This significant disparity highlights how much better InceptionV3 is at identifying subtle thermal patterns and characteristics, which improves gender categorization accuracy. The work makes a substantial contribution to the rapidly developing area of thermal imagingbased gender categorization by highlighting how crucial it is to use advanced deep learning architectures in order to improve performance and accuracy. Subsequent investigations might delve into novel methodologies, including multi-modal fusion or sophisticated methods, to enhance precision and resilience in tasks using thermal-based gender categorization. © 2024 IEEE.

关键词： AlexNet Convolutional Neural networks Deep learning Gender classification Inception V3 Thermal images

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Development of a Self-Study Environment for Voice Actors Using VR 13

Development of a Self-Study Environment for Voice Actors Usi...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Kambe, Riho Kurihara, Kazutaka Tsuda University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

In this study, we propose a VR system that allows voice actors to practice effectively alone. This VR system allows users to practice acting in a space that recreates the world described in the script and learn about the learning issues related to acting. Based on the idea that voice actors practice acting after first learning stage acting, we implemented both a space where they can experience stage acting and a space where they can act only with their voices, and furthermore, we made it possible to transition from stage acting to voice-only acting in stages, so that the ratio of relying on imagination can gradually increase. The transition from stage acting to voice-only acting can be made in stages, gradually increasing the ratio of reliance on imagination. Ultimately, the goal is to assist voice actors in performing only with their voices, without moving their bodies, in a way comparable to when they are acting through experience. © 2024 IEEE.

关键词： Virtual environments

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Understanding the Evolution in Tourist Behavior Patterns through Context-Aware Spatio-Temporal k-Means

Understanding the Evolution in Tourist Behavior Patterns thr...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Belussi, Alberto Vecchia, Anna Dalla Gambini, Mauro Migliorini, Sara Quintarelli, Elisa University of Verona Dept. of Computer Science Verona Italy

ISBN: (纸本)9798350362480

Understanding tourist behavior patterns is crucial for developing effective recommendation and decision support systems. The behaviors are often captured through the trajectories followed by tourists during their journeys or the sequences of visited Points of Interest (PoIs). Identifying common patterns and tracking their evolution over time can enhance the ability to understand, predict, and influence tourist choices, ultimately supporting goals like promoting specific destinations and fostering sustainable visitation patterns. Clustering algorithms like k-Means are commonly used to extract frequent patterns, requiring a tailored distance metric suited to the task. Since tourist trajectories combine spatial, temporal, and semantic features, defining a distance function that accurately captures these multifaceted aspects is essential. This paper examines various methods for encoding trajectory data and explores their effects on the clustering process. Finally, we compare and validate their suitability by using a real-world dataset of visits performed by tourists in Verona (Italy) from 2014 to 2022. © 2024 IEEE.

关键词： Spatio-temporal data

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