2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)最新文献_第2页

Innovative tools for investigation on flame dynamics by means of fast imaging 利用快速成像技术研究火焰动力学的创新工具

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10052919

A. Ferrante, G. Molfetta

{"title":"Innovative tools for investigation on flame dynamics by means of fast imaging","authors":"A. Ferrante, G. Molfetta","doi":"10.1109/IPAS55744.2022.10052919","DOIUrl":"https://doi.org/10.1109/IPAS55744.2022.10052919","url":null,"abstract":"Computer vision and image processing are increasingly being used as investigative tools in various fields of science. Compared to other types of measurement techniques, image processing of high-speed camera shooting allows to obtain semi-quantitative information on dynamic aspects of the phenomenon under investigation. Combustion, both for propulsion and for energy production, is characterized by very fast phenomena that cannot be revealed by the typical measurement techniques used in test rigs for the development of prototypes. These dynamic phenomena, often called flame dynamics or flame instability, heavily affect the performance of modern combustors and their study has become essential, not only for the scientific knowledge of the phenomena, but also for the development of new combustion techniques. Therefore, for more than twenty years, the scientific community has been using fast imaging techniques to reveal phenomena that occur during combustion and this has allowed us to deepen our knowledge of the complex phenomena related to combustion itself. This paper describes the use of fast imaging and image processing techniques for the investigation of flame instability phenomena generated by gas turbine burners in an atmospheric test rig where a full-scale burner is tested. Optical investigations are conducted in the visible region of the electromagnetic spectrum.","PeriodicalId":322228,"journal":{"name":"2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114463348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domestic Solid Waste Classification Using Convolutional Neural Networks 基于卷积神经网络的生活垃圾分类

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10052971

Surajsingh Dookhee

{"title":"Domestic Solid Waste Classification Using Convolutional Neural Networks","authors":"Surajsingh Dookhee","doi":"10.1109/IPAS55744.2022.10052971","DOIUrl":"https://doi.org/10.1109/IPAS55744.2022.10052971","url":null,"abstract":"The overwhelming amount of household solid waste generated daily is alarming, and this contributes to the rise in pollution and drastic climate change. In such a context, automated waste classification at the initial stage of disposal can be an effective solution to separate recyclable items. Convolutional Neural Networks based on deep learning are often used for automated waste classification, but however, research works are limited to insufficient categories of waste such as the TrashNet dataset consisting of 2,527 images and 6 categories of waste. This dataset does not include other important categories such as battery, biological, and clothing items to reflect real-life environmental problems. Therefore, in this paper, a larger dataset consisting of 15,515 images and 12 categories of common household solid waste was used to evaluate the performance of DenseNet121, DenseNet169, EfficientNetB0, InceptionV3, MobileNetV2, ResNet50, VGG16, VGG19, and Xception Convolutional Neural Network models. Data augmentation was applied to solve the problem of class imbalance, and findings of my first research showed that the Xception model compiled with Adam optimiser outperformed all other models with an accuracy of 88.77% and an F1-score of 0.89. The performance of the model was improved to 89.57% with an F1-score of 0.90 when compiled with Nadam optimiser. However, further experimentation showed that the model did not generalise well despite reaching an accuracy of 93.42% and an F1-score of 0.93 when trained without data augmentation. This demonstrates the feasibility of the proposed model for real-life environmental problems.","PeriodicalId":322228,"journal":{"name":"2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114832963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Image Processing Based Classifier to Support Safe Dropping for Delivery-by-Drone 基于图像处理的分类器支持无人机安全投递

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10052868

A. Alsawy, Alan Hicks, Dan Moss, Susan Mckeever

引用次数: 5

Optical tracking system based on COTS components 基于COTS组件的光学跟踪系统

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10053039

Débora N.P. Oliveira, Marcos R. A. Morais, Antonio M. N. Lima

引用次数: 0

A microfluidic system, utilising image processing methods, for the detection of blood coagulation and erythrocyte aggregation 一种微流体系统，利用图像处理方法，用于检测血液凝固和红细胞聚集

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10053010

Marinos Louka, Andreas Passos, Antonis Inglezakis, Constantinos Loizou, E. Kaliviotis

{"title":"A microfluidic system, utilising image processing methods, for the detection of blood coagulation and erythrocyte aggregation","authors":"Marinos Louka, Andreas Passos, Antonis Inglezakis, Constantinos Loizou, E. Kaliviotis","doi":"10.1109/IPAS55744.2022.10053010","DOIUrl":"https://doi.org/10.1109/IPAS55744.2022.10053010","url":null,"abstract":"Hemostasis is a defence mechanism that prevents blood losses in cases of vessel injuries, and other related disorders. In many cases, patients need to frequently monitor their blood coagulation tendency in order to regulate their medication. In addition, red blood cell aggregation (RBCA) is related to blood inflammation, and it appears elevated in many pathological conditions. Blood coagulation and RBCA can be studied by analysing the dynamic changes of light transmittance though a clotting/aggregating sample, and indeed various works in the literature exploit this approach. In this work, blood coagulation and RBCA are examined by utilising single drops of blood in an inexpensive camera-based microfluidic system, designed for low computational and production cost. Results are compared with a microscopy-camera system, with both setups utilizing the same custom made microchannel. Three image processing algorithms are developed to analyze the averaged light intensity, and the local structural chracteristics of blood, through a binarization and region classification method, using logical operations. The results illustrate the repeatability of the technique and the donor-to-donor variation within the proposed approach. Based on the image processing analysis, the developed coagulation and aggregation indices show great potential of utilisation in an inexpensive and robust point of care device.","PeriodicalId":322228,"journal":{"name":"2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129837148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Segmentation of Shipping Bags in RGB-D Images RGB-D图像中运输袋的分割

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10052982

E. Vasileva, Z. Ivanovski

{"title":"Segmentation of Shipping Bags in RGB-D Images","authors":"E. Vasileva, Z. Ivanovski","doi":"10.1109/IPAS55744.2022.10052982","DOIUrl":"https://doi.org/10.1109/IPAS55744.2022.10052982","url":null,"abstract":"This paper presents a convolutional neural network (CNN) architecture for segmenting partially transparent shipping bags in RGB-D images of cluttered scenes containing different packaging items in unstructured configurations. The proposed architecture is optimized for training with a limited number of samples with high variability. The analysis of the results with regard to the input type, network architecture, and lighting conditions, proves that including low-resolution depth information improves the segmentation of objects with similar colors and objects in previously unseen lighting conditions, and the high-resolution color photographs greatly improve the segmentation of details. This motivates the proposed multi-input architecture with early feature fusion in order to fully utilize the benefits of high-resolution photographs and low-resolution depth information. The proposed CNN architecture performs successful segmentation of shipping bags in a cluttered environment among packages and items of different colors and materials with irregular shapes. The CNN provides an improvement in accuracy over well-known semantic segmentation architectures while significantly reducing the required processing time, making it suitable for real-time application.","PeriodicalId":322228,"journal":{"name":"2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122941461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Photorealistic Image Stylization Techniques 逼真图像风格化技术综述

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10053049

Hassaan A. Qazi

引用次数: 0

Human Dendritic Cells Classification based on Possibility Theory 基于可能性理论的人类树突状细胞分类

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10052863

Mouna Zouari Mehdi, A. Benzinou, J. Elleuch, K. Nasreddine, Dhia Ammeri, D. Sellami

{"title":"Human Dendritic Cells Classification based on Possibility Theory","authors":"Mouna Zouari Mehdi, A. Benzinou, J. Elleuch, K. Nasreddine, Dhia Ammeri, D. Sellami","doi":"10.1109/IPAS55744.2022.10052863","DOIUrl":"https://doi.org/10.1109/IPAS55744.2022.10052863","url":null,"abstract":"Dendritic cells can be seen as a mirror of our immune system. Based on their in virto analysis, biological experts are now able to study the impact of food contaminants on the human immune system. Accordingly, a visual characterization of dendritic cell morphology can provide an indirect estimation of the toxicity. In this paper, we propose an automatic classification of dendritic cells that could serve as a second non-subjective opinion for pathologists. The proposed approach is built on pre-processing steps for segmentation and cell detection in microscopic images. Then, a set of features such as shape descriptors are extracted for cell characterization. At this step, three cell classes are distinctively identified by experts. Nevertheless, a high ambiguity is revealed between cell classes. Possibility theory can offer a realistic framework for making reliable decisions under high ambiguity. It exploits a human natural concept of the implicit use of probability distribution for deciding on the possibility of some assertions in some contexts where a cognitive conflict is observed while interfering existing related postulates, leading to high ambiguity. Based on the consistency concept of Dubois and Prade, a transformation of the probability into a possibility distribution is undertaken. Under possibility paradigm, a further feature selection in the possibility space using the Shapely index. Compared to state-of-the art methods the proposed approach yielded on a real dataset of nearly 630 samples an improvement in terms of the mean precision rate, the Recall rate, and the F1-measure.","PeriodicalId":322228,"journal":{"name":"2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125860370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combination of Object Tracking and Object Detection for Animal Recognition 目标跟踪与目标检测相结合的动物识别

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10053017

Francis Williams, L. Kuncheva, Juan José Rodríguez Diez, Samuel L. Hennessey

引用次数: 0

Image Interpolation with Edges Preserving and Implementation on the Real ADAS Platform 基于边缘保持的图像插值及其在实际ADAS平台上的实现

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) Pub Date : 2022-12-05 DOI: 10.1109/IPAS55744.2022.10052818

Božidar Kelava, M. Vranješ, D. Vranješ, Ž. Lukač

{"title":"Image Interpolation with Edges Preserving and Implementation on the Real ADAS Platform","authors":"Božidar Kelava, M. Vranješ, D. Vranješ, Ž. Lukač","doi":"10.1109/IPAS55744.2022.10052818","DOIUrl":"https://doi.org/10.1109/IPAS55744.2022.10052818","url":null,"abstract":"To save transmission, processing and memory resources in Advanced Driver Assistance Systems (ADAS), it is often necessary to reduce the image resolution. Sometimes it is necessary to increase it after the transmission. Both resolution changes involve an image interpolation process. This paper describes implementation for three well-known interpolation methods, nearest neighbour interpolation (NN), bilinear interpolation (BL) and bicubic interpolation (BC), on a real automotive AMV ALPHA platform, using multiple processors on the same System on Chip (SoC). Implementation was done using C programming language and Vision Software Development Kit (VSDK). Specific attention is given to the optimal distribution of tasks to the certain processor. The results have shown that, on the real automotive AMV ALPHA platform, BL interpolation achieves the best trade-off between the quality of interpolated image for the usage in automotive image-processing based algorithms and execution time, especially for the algorithms where the lower frame rate is acceptable (like surround-view, park assist, etc.).","PeriodicalId":322228,"journal":{"name":"2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)","volume":"Five 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129741653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0