H.3.2.2. Computer vision
Elahe Yadolahi; Sheis Abolmaali
Abstract
Semantic segmentation is a critical task in computer vision, focused on extracting and analyzing detailed visual information. Traditional artificial neural networks (ANNs) have made significant strides in this area, but spiking neural networks (SNNs) are gaining attention for their energy efficiency ...
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Semantic segmentation is a critical task in computer vision, focused on extracting and analyzing detailed visual information. Traditional artificial neural networks (ANNs) have made significant strides in this area, but spiking neural networks (SNNs) are gaining attention for their energy efficiency and biologically inspired time-based processing. However, existing SNN-based methods for semantic segmentation face challenges in achieving high accuracy due to limitations such as quantization errors and suboptimal membrane potential distribution. This research introduces a novel spiking approach based on Spiking-DeepLab, incorporating a Regularized Membrane Potential Loss (RMP-Loss) to address these challenges. Built upon the DeepLabv3 architecture, the proposed model leverages RMP-Loss to enhance segmentation accuracy by optimizing the membrane potential distribution in SNNs. By optimizing the storage of membrane potentials, where values are stored only at the final time step, the model significantly reduces memory usage and processing time. This enhancement not only improves the computational efficiency but also boosts the accuracy of semantic segmentation, enabling more accurate temporal analysis of network behavior. The proposed model also demonstrates better robustness against noise, maintaining its accuracy under varying levels of Gaussian noise, which is common in real-world scenarios. The proposed approach demonstrates competitive performance on standard datasets, showcasing its potential for energy-efficient image processing applications.
H.3. Artificial Intelligence
Seyed Alireza Bashiri Mosavi; Omid Khalaf Beigi; Arash Mahjoubifard
Abstract
Using intelligent approaches in diagnosing the COVID-19 disease based on machine learning algorithms (MLAs), as a joint work, has attracted the attention of pattern recognition and medicine experts. Before applying MLAs to the data extracted from infectious diseases, techniques such as RAT and RT-qPCR ...
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Using intelligent approaches in diagnosing the COVID-19 disease based on machine learning algorithms (MLAs), as a joint work, has attracted the attention of pattern recognition and medicine experts. Before applying MLAs to the data extracted from infectious diseases, techniques such as RAT and RT-qPCR were used by data mining engineers to diagnose the contagious disease, whose weaknesses include the lack of test kits, the placement of the specialist and the patient pointed at a place and low accuracy. This study introduces a three-stage learning framework including a feature extractor by visual geometry group 16 (VGG16) model to solve the problems caused by the lack of samples, a three-channel convolution layer, and a classifier based on a three-layer neural network. The results showed that the Covid VGG16 (CoVGG16) has an accuracy of 96.37% and 100%, precision of 96.52% and 100%, and recall of 96.30% and 100% for COVID-19 prediction on the test sets of the two datasets (one type of CT-scan-based images and one type of X-ray-oriented ones gathered from Kaggle repositories).
H.5. Image Processing and Computer Vision
Farima Fakouri; Mohsen Nikpour; Abbas Soleymani Amiri
Abstract
Due to the increased mortality caused by brain tumors, accurate and fast diagnosis of brain tumors is necessary to implement the treatment of this disease. In this research, brain tumor classification performed using a network based on ResNet architecture in MRI images. MRI images that available in the ...
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Due to the increased mortality caused by brain tumors, accurate and fast diagnosis of brain tumors is necessary to implement the treatment of this disease. In this research, brain tumor classification performed using a network based on ResNet architecture in MRI images. MRI images that available in the cancer image archive database included 159 patients. First, two filters called median and Gaussian filters were used to improve the quality of the images. An edge detection operator is also used to identify the edges of the image. Second, the proposed network was first trained with the original images of the database, then with Gaussian filtered and Median filtered images. Finally, accuracy, specificity and sensitivity criteria have been used to evaluate the results. Proposed method in this study was lead to 87.21%, 90.35% and 93.86% accuracy for original, Gaussian filtered and Median filtered images. Also, the sensitivity and specificity was calculated 82.3% and 84.3% for the original images, respectively. Sensitivity for Gaussian and Median filtered images was calculated 90.8% and 91.57%, respectively and specificity was calculated 93.01% and 93.36%, respectively. As a conclusion, image processing approaches in preprocessing stage should be investigated to improve the performance of deep learning networks.
V. Torkzadeh; S. Toosizadeh
Abstract
In this study, an automatic system based on image processing methods using features based on convolutional neural networks is proposed to detect the degree of possible dipping and buckling on the sandwich panel surface by a colour camera. The proposed method, by receiving an image of the sandwich panel, ...
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In this study, an automatic system based on image processing methods using features based on convolutional neural networks is proposed to detect the degree of possible dipping and buckling on the sandwich panel surface by a colour camera. The proposed method, by receiving an image of the sandwich panel, can detect the dipping and buckling of its surface with acceptable accuracy. After a panel is fully processed by the system, an image output is generated to observe the surface status of the sandwich panel so that the supervisor of the production line can better detect any potential defects at the surface of the produced panels. An accurate solution is also provided to measure the amount of available distortion (depth or height of dipping and buckling) on the sandwich panels without needing expensive and complex equipment and hardware.
