Relevant bibliographies by topics / Region based convolutional neural networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Region based convolutional neural networks'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Region based convolutional neural networks"

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M., Sushma Sri, Rajendra Naik B., and Jaya Sankar K. "Object Detection Based on Faster R-Cnn." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 3 (2021): 72–76. https://doi.org/10.35940/ijeat.C2186.0210321.

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In recent years there is rapid improvement in Object detection in areas of video analysis and image processing applications. Determing a desired object became an important aspect, so that there are many numerous of methods are evolved in Object detection. In this regard as there is rapid development in Deep Learning for its high-level processing, extracting deeper features, reliable and flexible compared to conventional techniques. In this article, the author proposes Object detection with deep neural networks and faster region convolutional neural networks methods for providing a simple algor
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Bollimuntha, Mahalakshmi. "Region-Based Convolutional Neural Networks Based Automated Detection and Classification of Diabetic Retinopathy." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45749.

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Abstract - One of the main causes of vision impairment in diabetic patients is diabetic retinopathy (DR), which is brought on by long-term elevated blood sugar levels damaging the retinal blood vessels. Early and accurate detection is essential for effective treatment. Deep learning-based approaches have shown significant promise in automated Diabetic Retinopathy (DR) diagnosis by leveraging advanced feature extraction and classification techniques. This study explores a deep learning framework utilizing a Region-based Convolutional Neural Network (RCNN) for Diabetic Retinopathy (DR) detection
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Ye, Mengqi, and Lijun Zhang. "Empirical Analysis of Financial Depth and Width Based on Convolutional Neural Network." Computational Intelligence and Neuroscience 2021 (December 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/8650059.

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There are great differences in financial and economic development in different regions. In different time series and different regions, the effects of financial depth and width on economic development are also different. This paper selects neural network to establish the economic benefit model of financial depth and breadth, which can deeply explore the relationship between financial data and economic data. In order to determine the optimal convolutional neural network parameters, the optimal convolutional neural network parameters are determined through comparative simulation analysis. The co
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WANG Chun-zhe, 王春哲, 安军社 AN Jun-she, 姜秀杰 JIANG Xiu-jie, and 邢笑雪 XING Xiao-xue. "Region proposal optimization algorithm based on convolutional neural networks." Chinese Optics 12, no. 6 (2019): 1348–61. http://dx.doi.org/10.3788/co.20191206.1348.

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Deng, Zihan, and Ang Li. "Object Detection Algorithms Based on Convolutional Neural Networks." Highlights in Science, Engineering and Technology 81 (January 26, 2024): 243–51. http://dx.doi.org/10.54097/vyfg4e34.

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Object detection is a fundamental and challenging task in computer vision, and it has attracted much attention from researchers worldwide. In recent years, deep learning technology has made remarkable progress and enabled new possibilities for object detection. Convolutional neural networks (CNNs), which are powerful tools for feature extraction and representation learning, have become the dominant approach for object detection, surpassing the traditional methods. This article reviews the development history of CNNs and their applications to object detection. It also introduces and compares tw
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Yan, Shiyang, Yizhang Xia, Jeremy S. Smith, Wenjin Lu, and Bailing Zhang. "Multiscale Convolutional Neural Networks for Hand Detection." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9830641.

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Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper,
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Pramila, P. V., and Atani Yamini. "Precision Analysis of Salient Object Detection in Moving Video Using Region Based Convolutional Neural Network Compared Over Optical Character Recognition." ECS Transactions 107, no. 1 (2022): 14001–15. http://dx.doi.org/10.1149/10701.14001ecst.

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To detect the number plate of the vehicles which violates the traffic rules using optical character recognition compared over region based convolutional networks. Materials and methods: In this work, number plates are identified by optical character recognition with the sample size of 22 and region based convolutional neural network of sample size 22. The number plate of the vehicle will be detected and converted into string format. Results: A prediction accuracy of 96.4% using the optical character recognition method was achieved, while the region based convolutional neural network was 94.2.%
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Xu, Yuelei, Mingming Zhu, Shuai Li, Hongxiao Feng, Shiping Ma, and Jun Che. "End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks." Remote Sensing 10, no. 10 (2018): 1516. http://dx.doi.org/10.3390/rs10101516.

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Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learnin
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Hu, Donghui, Qiang Shen, Shengnan Zhou, Xueliang Liu, Yuqi Fan, and Lina Wang. "Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks." Security and Communication Networks 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2314860.

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Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image an
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de Silva, Akila, Issei Mori, Gregory Dusek, James Davis, and Alex Pang. "Automated rip current detection with region based convolutional neural networks." Coastal Engineering 166 (June 2021): 103859. http://dx.doi.org/10.1016/j.coastaleng.2021.103859.

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Dissertations / Theses on the topic "Region based convolutional neural networks"

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Stone, David L. "The Application of Index Based, Region Segmentation, and Deep Learning Approaches to Sensor Fusion for Vegetation Detection." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5708.

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This thesis investigates the application of index based, region segmentation, and deep learning methods to the sensor fusion of omnidirectional (O-D) Infrared (IR) sensors, Kinnect sensors, and O-D vision sensors to increase the level of intelligent perception for unmanned robotic platforms. The goals of this work is first to provide a more robust calibration approach and improve the calibration of low resolution and noisy IR O-D cameras. Then our goal was to explore the best approach to sensor fusion for vegetation detection. We looked at index based, region segmentation, and deep learning me
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Grahn, Fredrik, and Kristian Nilsson. "Object Detection in Domain Specific Stereo-Analysed Satellite Images." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159917.

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Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more
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Hebert, Joshua A. "Ballistocardiography-based Authentication using Convolutional Neural Networks." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1228.

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This work demonstrates the viability of the ballistocardiogram (BCG) signal derived from a head-worn device as a biometric modality for authentication. The BCG signal is the measure of an individual's body acceleration as a result of the heart's ejection of blood. It is a characterization of an individual's cardiac cycle and can be derived non-invasively from the measurement of subtle movements of a person's extremities. Through the use of accelerometer and gyroscope sensors on a Smart Eyewear (SEW) device, derived BCG signals are used to train a convolutional neural network (CNN) as an authen
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Nikzad, Dehaji Mohammad. "Structural Improvements of Convolutional Neural Networks." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/410448.

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Over the last decade, deep learning has demonstrated outstanding performance in almost every application domain. Among different types of deep frameworks, convolutional neural networks (CNNs), inspired by the biological process of the visual system, can learn to extract discriminative features from raw inputs without any prior manipulation. However, efficient information circulation and the ability to explore effective new features are still two key and challenging factors for a successful deep neural network. In this thesis, we aim at presenting novel structural improvements of the CNN framew
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Song, Weilian. "Image-Based Roadway Assessment Using Convolutional Neural Networks." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/78.

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Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost of data acquisition and manual annotation has restricted the effect of these programs. In this thesis, we propose methods to automate the task of roadway safety assessment using deep learning. Specifically, we trained convolutional neural networks on publicly available roadway images to predict safety-related metrics: the star rating score and free-
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Jonnarth, Arvi. "Camera-Based Friction Estimation with Deep Convolutional Neural Networks." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355618.

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During recent years, great progress has been made within the field of deep learning, and more specifically, within neural networks. Deep convolutional neural networks (CNN) have been especially successful within image processing in tasks such as image classification and object detection. Car manufacturers, amongst other actors, are starting to realize the potential of deep learning and have begun applying it to autonomous driving. This is not a simple task, and many challenges still lie ahead. A sub-problem, that needs to be solved, is a way of automatically determining the road conditions, in
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Julin, Fredrik. "Vision based facial emotion detection using deep convolutional neural networks." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-42622.

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Emotion detection, also known as Facial expression recognition, is the art of mapping an emotion to some sort of input data taken from a human. This is a powerful tool to extract valuable information from individuals which can be used as data for many different purposes, ranging from medical conditions such as depression to customer feedback. To be able to solve the problem of facial expression recognition, smaller subtasks are required and all of them together form the complete system to the problem. Breaking down the bigger task at hand, one can think of these smaller subtasks in the form of
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Khlif, Wafa. "Multi-lingual scene text detection based on convolutional neural networks." Thesis, La Rochelle, 2022. http://www.theses.fr/2022LAROS022.

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Cette thèse propose des approches de détection de texte par des techniques d'apprentissage profond pour explorer et récupérer des contenus faiblement structurés dans des images de scène naturelles. Ces travaux proposent, dans un premier temps, une méthode de détection de texte dans des images de scène naturelle basée sur une analyse multi-niveaux des composantes connexes (CC) et l'apprentissage des caractéristiques du texte par un réseau de neurones convolutionnel (CNN), suivie d'un regroupement des zones de texte détectées par une méthode à base de graphes. Les caractéristiques des composante
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Söderström, Albin. "Anomaly-based Intrusion Detection Using Convolutional Neural Networks for IoT Devices." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21870.

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Background. The rapid growth of IoT devices in homes put people at risk of cyberattacks and the low power and computing capabilities in IoT devices make it difficultto design a security solution for them. One method of preventing cyber attacks isan Intrusion Detection System (IDS) that can identify incoming attacks so that anappropriate action can be taken. Previous attempts have been made using machinelearning and deep learning however these attempts have struggled at detecting newattacks.Objectives. In this work we use a convolutional neural network IoTNet designed forIoT devices to classify
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Li, Xile. "Real-time Multi-face Tracking with Labels based on Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36707.

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This thesis presents a real-time multi-face tracking system, which is able to track multiple faces for live videos, broadcast, real-time conference recording, etc. The real-time output is one of the most significant advantages. Our proposed tracking system is comprised of three parts: face detection, feature extraction and tracking. We deploy a three-layer Convolutional Neural Network (CNN) to detect a face, a one-layer CNN to extract the features of a detected face and a shallow network for face tracking based on the extracted feature maps of the face. The performance of our multi-face
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Books on the topic "Region based convolutional neural networks"

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Mou, Lili, and Zhi Jin. Tree-Based Convolutional Neural Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1870-2.

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Jin, Zhi, and Lili Mou. Tree-Based Convolutional Neural Networks: Principles and Applications. Springer, 2018.

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Artificial Intelligence Impacting Diagnosis of Glaucoma and Understanding the Regulatory Aspects of AI-Based Software As Medical Device: Glaucoma Diagnosis,Convolutional Neural Networks. Independently Published, 2021.

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Book chapters on the topic "Region based convolutional neural networks"

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Zhang, Dingqian, Hui Zhang, Haichang Li, and Xiaohui Hu. "RR-FCN: Rotational Region-Based Fully Convolutional Networks for Object Detection." In Engineering Applications of Neural Networks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98204-5_5.

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Ram, Shrawan, and Anil Gupta. "Multiple Sclerosis Disorder Detection Through Faster Region-Based Convolutional Neural Networks." In Inventive Computation and Information Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4305-4_16.

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Sarda, Ekta, Priyanka Deshmukh, Snehal Bhole, and Shubham Jadhav. "Estimating Food Nutrients Using Region-Based Convolutional Neural Network." In Proceedings of International Conference on Computational Intelligence and Data Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8767-2_36.

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Liu, Ting, Jun Wan, Tingzhao Yu, Zhen Lei, and Stan Z. Li. "Age Estimation Based on Multi-Region Convolutional Neural Network." In Biometric Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46654-5_21.

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Uma, K., B. Sathya Bama, and M. Maheesha. "Emergency Vehicle Detection in Traffic Surveillance Using Region-Based Convolutional Neural Networks." In Advances in Automation, Signal Processing, Instrumentation, and Control. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_49.

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Qadir, Hemin Ali, Ilangko Balasingham, and Younghak Shin. "Region-Based Convolutional Neural Network for Polyp Detection and Segmentation." In Computer-Aided Analysis of Gastrointestinal Videos. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64340-9_11.

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Vanusha, D., and B. Amutha. "Diabetic Retinopathy Image Segmentation Using Region-Based Convolutional Neural Network." In Proceedings of International Conference on Deep Learning, Computing and Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5652-1_57.

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Vanusha, D., and B. Amutha. "Diabetic Retinopathy Image Segmentation Using Region-Based Convolutional Neural Network." In Proceedings of International Conference on Deep Learning, Computing and Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5652-1_57.

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Hossen, Rakib, Minhazul Arefin, and Mohammed Nasir Uddin. "Object Detection on Dental X-ray Images Using Region-Based Convolutional Neural Networks." In Machine Intelligence and Data Science Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2347-0_26.

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Ghosh, Supratim, Mahantapas Kundu, and Mita Nasipuri. "Region Separated Vessel Segmentation in Fundus Image Using Multi-scale Layer-Based Convolutional Neural Network." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5191-6_56.

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Conference papers on the topic "Region based convolutional neural networks"

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Gunabhiram, D. K., Sumathi D, and Karthika Natarajan. "Chicken Object Detection Using Faster Region-Based Convolutional Neural Networks." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10987538.

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ThondralNayagi, A., and T. Nandhakumar. "Road Lane Detection using Region-based Convolutional Neural Network (RCNN)." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934619.

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N, Navaprakash, S. Vikram Reddy, and S. Dakshinesh. "Improving accuracy in Text Extraction From images using Region-Based Convolutional Neural Networks algorithm compared to Convolutional Neural Network algorithm." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10987308.

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Pattabiraman, V., K. Hari Hara Sudhan, and V. Logeshwari. "Bone Fracture Detection using Region-Based Convolutional Neural Network." In 2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI). IEEE, 2024. https://doi.org/10.1109/cvmi61877.2024.10782572.

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Desu, Jyothirmayi, Venkata Abhishek Chigulla, Karthik Sai Chittibomma, Mounika Ayinapuru, and Siva Satya Sreedhar P. "Weapon Detection Using Region-Based Convolutional Neural Network (RCNN)." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031511.

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Velvizhi, R., and B. Ankayarkanni. "Segmenting Stenotic Regions from Coronary Angiograms using Mask Region-based Convolutional Neural Network." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810886.

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Kalpana, Ponugoti, Surendar Rama Sitaraman, Sunil S. Harakannanavar, Zaid Alsalami, and S. Nagaraj. "Efficient Multimodal Biometric Recognition for Secure Authentication Based on Faster Region-Based Convolutional Neural Network." In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON). IEEE, 2024. http://dx.doi.org/10.1109/nmitcon62075.2024.10699089.

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Chowdhury, Priyanjana, Nikhil Das, Sanghamitra Nath, and Utpal Sharma. "Convolutional Neural Network Based Broadcast News Summarization using Acoustic-Prosodic Features." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10903109.

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M R, Tejonidhi, Santosh Kumar Sahoo, Manjula B M, Thota Soujanya, and Saravanan Kandaneri Ramamoorthy. "Vision-Based Complete Scene Understanding Using Faster Region-Convolutional Neural Network." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10690903.

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Kumar, M. Prema, K. Ashfaq Ahmed, K. Subash, and Anurag Aeron. "Oral Cancer Detections and Classification Using Region Based Convolutional Neural Network." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545203.

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Reports on the topic "Region based convolutional neural networks"

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Lukow, Steven, Ross Lee, David Grow, and Jonathan Gigax. Advancing Vision-based Feedback and Convolutional Neural Networks for Visual Outlier Detection. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1889960.

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa
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Meni, Mackenzie, Ryan White, Michael Mayo, and Kevin Pilkiewicz. Entropy-based guidance of deep neural networks for accelerated convergence and improved performance. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49805.

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Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can
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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line
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Slone, Scott Michael, Marissa Torres, Nathan Lamie, Samantha Cook, and Lee Perren. Automated change detection in ground-penetrating radar using machine learning in R. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49442.

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Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the interna
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