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共 1637 条记录，以下是271-280 订阅

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Marginal Contrastive Loss: A Step Forward for Forward-Forward

Marginal Contrastive Loss: A Step Forward for Forward-Forwar...

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Iranian Machine vision and image processing (MVIP)

作者： Hossein Aghagolzadeh Mehdi Ezoji Faculty of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran

The Forward-Forward algorithm was developed to increase the resemblance of artificial neural network training processes to those occurring in the brain, in contrast to the backpropagation algorithm, which has been shown to have less similarity to brain processes. While Forward-Forward is a fascinating and novel idea, it significantly differs in performance from backpropagation. Forward-Forward strives to achieve this similarity by updating each layer independently of the others, through the introduction of a loss function that facilitates the separability of data from different classes. This inherent nature of creating discrimination between data of different classes inspired us to take advantages of contrastive learning to improve Forward-Forward performance. We modified the contrastive loss to be used in Forward-Forward, and our experimental results show that the proposed method improves the model accuracy and increases the convergence speed by more than 20 times.

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Brain Tumor Segmentation in MRI images using Deformable and Dilated Convolutions

Brain Tumor Segmentation in MRI Images using Deformable and ...

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Iranian Machine vision and image processing (MVIP)

作者： Nasim Amini Mohsen Soryani Mohammad Reza Mohammadi School of Computer Engineering Iran University of Science and Technology Tehran Iran

Segmentation of brain tumor images is an important issue in medical image processing and can help surgeons to accurately assess the tumor area. Since tumors vary in shape, size, and location from patient to patient, segmenting brain tumors is a challenge. In addition, small tumors are more difficult to segment than larger ones. In this paper, we present a method based on deep convolutional networks to improve the segmentation accuracy of brain tumors, especially small tumors in MRI images. In this method we have increased the accuracy of tumor segmentation by adding a module to UNet model. The proposed module uses deformable and dilated convolutions, which provide more spatial information to the network and thus increase the accuracy of tumor segmentation. The results show that our method is able to achieve a Dice of 0.8877 in the whole tumor section. For the core and enhancing tumor sections, we were able to achieve Dice values of 0.8683 and 0.8176, respectively.

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Design of a vision-Based Dynamic Positioning Control System for a Work-class ROV Using Hardware-in-the-Loop Simulation

Design of a Vision-Based Dynamic Positioning Control System ...

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Iranian Machine vision and image processing (MVIP)

作者： Ali Nourmohammadi Mehdi Loueipour Alireza Hosseinnajad Department of Mechanical Engineering Isfahan University of Technology Isfahan Iran Research Institute for Subsea Science and Technology Isfahan University of Technology Isfahan Iran

This paper introduces a vision-based dynamic positioning (DP) control system and develops a hardware-in-the-loop (HIL) platform to validate the performance of the controller applied to a work-class remotely operated vehicle (ROV). The proposed platform consists of three main parts: hardware, image processing part and controller. The hardware included a calibrated camera that was connected to a dedicated computer via USB 2.0. In the image processing part after pre-processing a circular Hough transform was used to detect and determine the position of the target in the image plane. Furthermore, this paper proposed a feedforward proportional-integral-derivative (PID) controller. To evaluate the performance of the proposed controller, two scenarios were implemented. In the first scenario, the target was considered stationary and a disturbance was applied to the ROV in the simulation environment. In the second scenario, the target object has moved along a rectangular path, and the objective was to stabilize the ROV at the desired points. In both scenarios, the reference signal was acquired by a calibrated camera from the target and sent to the controller. The results showed the desirable performance of the proposed controller.

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Performance Enhancement in Agriculture Sector Based on image processing 3

Performance Enhancement in Agriculture Sector Based on Image...

引用

3rd international conference on Innovations in computer Science and Software Engineering, ICONICS 2022

作者： Hassan, Ali Rehman, Faisal Ashraf, Muhammad Shahbaz Ashfaq, Ahmed Sharif, Hanan Zeeshan, Rana Akram, Salman Akram, Hira Lahore Leads University Department of Computer Science and Information Technology Lahore Pakistan

ISBN: (纸本)9781665461412

In the realm of agriculture and horticulture, machine vision and soft computing approaches have shown promise in overcoming the limitations of traditional methods for identifying plant illnesses utilizing various plant components. In all relevant studies such as fruit grading, leaf lesion area identification, and so on, image segmentation is the first and most important step. In order to diagnose the illness more effectively, a robust approach for numerous crops employing diverse plant components such as Fruit, Flower, and Leaf has been suggested in this study. Before segmentation, the acquired real-Time pictures are pre-processed for illumination normalization and color space conversion. To enhance the segmentation outcomes, the conventional ML, image processing and deep learning approaches scheme has been made adaptable, and edge detection transformations have been implemented. To separate the sick regions from photos, the aim function of the Machine Learning approach has been adjusted, and cluster centers have also been upgraded. The many classes of plant illnesses noticed by shooting various types of images of diseases in plants, including many popular plants such as grapes, apple, and tomato. In terms of both broad human observation and computing time, the results achieved are superior. © 2022 IEEE.

关键词： image enhancement

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Threat Detection in Self-Driving Vehicles Using computer vision 3rd

Threat Detection in Self-Driving Vehicles Using Computer Vis...

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3rd international conference on Machine Learning, image processing, Network Security and Data Sciences, MIND 2021

作者： Goenka, Umang Jagetia, Aaryan Patil, Param Singh, Akshay Sharma, Taresh Saini, Poonam Indian Institute of Information Technology Lucknow India Sardar Patel Institute of Technology Mumbai India Punjab Engineering College Chandigarh India

ISBN: (纸本)9789811958670

On-road obstacle detection is an important field of research that falls in the scope of intelligent transportation infrastructure systems. The use of vision-based approaches results in an accurate and cost-effective solution to such systems. In this research paper, we propose a threat detection mechanism for autonomous self-driving cars using dash-cam videos to ensure the presence of any unwanted obstacle on the road that falls within its visual range. This information can assist the vehicle’s program to enroute safely. There are four major components, namely YOLO to identify the objects, advanced lane detection algorithm, multi-regression model to measure the distance of the object from the camera, the two-second rule for measuring the safety, and limiting speed. In addition, we have used the Car Crash Dataset (CCD) for calculating the accuracy of the model. The YOLO algorithm gives an accuracy of around 93%. The final accuracy of our proposed Threat Detection Model (TDM) is 82.65%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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A vision-Based Traffic Accident Analysis and Tracking system from Traffic Surveillance Video 3

A Vision-Based Traffic Accident Analysis and Tracking system...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal processing, INCOS 2024

作者： Anjana, P. Nallasivan, G. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (纸本)9798350361186

Traffic accidents pose significant challenges to road safety and transportation management worldwide. Timely and accurate analysis of these incidents is crucial for effective response and mitigation. This paper presents a novel visionBased Traffic Accident Analysis and Tracking System designed to detect, analyze, and track traffic accidents from surveillance videos using advanced image processing techniques. Recent research methodology has unlimited in realizing the object trajectories of vehicle accident connected information detecting and take out the features relationships. The proposed research paper recommends a road traffic accident tracking systems that assist to establish whether every video sequence demonstrates mishap by producing and considering vehicle object trajectories using Vehicle Influence Location Generator and CNN classifier. In the proposed system, the vehicle influence location generator includes three parameters: vehicle object trajectories the distances between vehicles and accident analysis. First parameter, vehicle portions are identified by rectangle bounding box in green colour. The second parameter, vehicle portion centre positions are represented by dots using Region of Interest (ROI). The final parameters, the distances between previous vehicle frame and current vehicle frame are represented by points. The proposed system considered threshold value for distance calculation and update the values. Finally, the opportunity of a disaster is characterized by red circles. A CNN will be used to analyze the accident from the Vehicle Influence Location Generator produced by vehicle object trajectories. The proposed model was trained using CADP dataset in the training and testing stage, and it was able to obtain an estimated 96.5 % accuracy rate in overall proposed traffic accident detection. © 2024 IEEE.

关键词： Location

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Detection of Breast Cancer from Histopathological images using image processing and Deep-Learning 3

Detection of Breast Cancer from Histopathological Images usi...

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Thomas, Anusha Maria Adithya, G. Arunselvan, A.S. Karthik, R. Vellore Institute of Technology Electronics and Computer Engineering Chennai India Vellore Institute of Technology Centre for Cyber Physical Systems School of Electronics Engineering Chennai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Breast cancer is the most commonly occurring cancer in women. Cancer patients frequently develop metastasis, which is responsible for more than 90% of their deaths. The mortality rate will be significantly reduced if it is identified and treated in an early phase. The categorization of cancer cells is critical for medical diagnosis, tailored therapy, and disease prevention. Classifying various types of these cells with great precision has remained a difficult issue. Deep learning has emerged as a significant tool for such challenging tasks in the fields of biology and medicine. In this research, we propose a novel model that throws light on image processing and deep learning for breast cancer classification from histopathological images. The proposed vision transformer model outperforms the state-of-the-art convolution neural networks in classifying the breast cancer cell with an accuracy of 96%. © 2022 IEEE.

关键词： Diseases

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A Comparison Between CCTV and Industrial Cameras for Vehicle Attribute Recognition

A Comparison Between CCTV and Industrial Cameras for Vehicle...

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Iranian Machine vision and image processing (MVIP)

作者： Mohammadreza Asadi Mohammad Yasin Fakhar Seyedeh Sogand Hashemi Safiyeh Rezaei Mohamad Kiani Abari Seyed Alireza Abbaspour AI Research Center Hoopad Vision Company Isfahan Iran

In machine/computer vision, cameras serve a major role in image acquisition. Surveillance scenarios typically rely on Closed-Circuit Television (CCTV) cameras. This study aims to evaluate industrial cameras within a surveillance application, contrasting their performance with that of CCTV cameras. We explore the comparative analysis of CCTV and industrial cameras for vehicle attribute recognition, specifically concentrating on the recognition of vehicle color and model using deep learning techniques. To train and evaluate the models, we have created datasets from images captured by both a CCTV and an industrial camera. Our findings indicate that the industrial camera outperforms the CCTV. However, employing advanced processing algorithms has the potential to minimize the performance gap between these two cameras. Our research represents one of the initial comparative analyses between these camera types, offering valuable guidance in selecting the most suitable camera for specific applications.

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MaskChanger: A Transformer-Based Model Tailoring Change Detection with Mask Classification

MaskChanger: A Transformer-Based Model Tailoring Change Dete...

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Iranian Machine vision and image processing (MVIP)

作者： Mohammad Ebrahimzadeh Mohammad Taghi Manzuri Department of Computer Engineering Sharif University of Technology Tehran Iran

Change detection in multi-temporal remote sensing data enables crucial urban analysis and environmental monitoring applications. However, complex factors like illumination variance and occlusion make robust automated change interpretation challenging. We propose MaskChanger - a novel deep learning paradigm tailored for satellite image change detection. Our method adapts the segmentation-specialized Mask2Former architecture by incorporating Siamese networks to extract features separately from bi-temporal images, while retaining the original mask transformer decoder. To our knowledge, this is the first study in which change detection is converted from the existing per-pixel classification approach into a mask classification approach. Evaluated on the LEVIR-CD benchmark of over 600 very high-resolution image pairs exhibiting real-world rural and urban changes, MaskChanger achieves Fl-Score of 91.96%, outperforming prior transformer-based change detection approaches.

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Human Identification via Dental Panoramic images Using Simple Features

Human Identification via Dental Panoramic Images Using Simpl...

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Iranian Machine vision and image processing (MVIP)

作者： Mahdi Hariri Behnaz Tutunchi Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran Department of Computer Engineering Islamic Azad University Zanjan Branch Zanjan Iran

In mass disasters, quick identification of the victims is important even in rescuing the survivors. Teeth are one of the hard parts of the human body, which are more resistant to decay and destruction than other biometric modalities, and due to the variety of numbers, types, and shapes, they are considered a suitable option for identification. Due to the lack of Panoramic dental images from people before their death, the number of database images for training today’s modern identification systems is very limited which reduces the accuracy of recognition. In this article, three relatively time-robust features, the number of teeth, the number and position of restored teeth, and the interdental distances are used for identification. For the independence of identification to the variety of processes and the angle of photographs, we use spatial histograms of empty tooth areas as feature vectors. By comparing the Euclidean distance, the images of the data set can be recovered with a similarity of more than 85% to the input image, which is an acceptable accuracy for the simple and easy access of the features. Considering the expert’s role in the final decision, this system helps facilitate and accelerate identification.

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