Proceedings of the 2nd International Conference on Graphics and Signal Processing最新文献

Improving Event Resolution in Cricket Videos 提高板球视频的事件分辨率

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3282293

S. Premaratne, K. Jayaratne, P. Sellapan

引用次数: 3

Implementation of Genetic Algorithm (GA) for Hyperparameter Optimization in a Termite Detection System 白蚁检测系统超参数优化遗传算法的实现

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3282289

M. A. Nanda, K. Seminar, M. Solahudin, A. Maddu, D. Nandika

引用次数: 4

Fabric Defects Detection based on SSD 基于SSD的Fabric缺陷检测

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3282300

Zhoufeng Liu, Shanliang Liu, Chunlei Li, S. Ding, Yan Dong

引用次数: 26

Improving the Discrete Cosine Transform by the Sign Symbol Feature in H.265/HEVC H.265/HEVC中符号特征对离散余弦变换的改进

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3284958

S. Im, Ka‐Hou Chan

引用次数: 2

Development of Communication Aid Device for Disabled Persons Using Corneal Surface Reflection Image 基于角膜表面反射图像的残疾人通信辅助装置的研制

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3282298

N. Nakazawa, Shinnosuke Aikawa, T. Matsui

引用次数: 2

Towards Tobacco Leaf Detection Using Haar Cascade Classifier and Image Processing Techniques 基于Haar级联分类器和图像处理技术的烟叶检测

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3282292

Charlie S. Marzan, N. Marcos

{"title":"Towards Tobacco Leaf Detection Using Haar Cascade Classifier and Image Processing Techniques","authors":"Charlie S. Marzan, N. Marcos","doi":"10.1145/3282286.3282292","DOIUrl":"https://doi.org/10.1145/3282286.3282292","url":null,"abstract":"Tobacco grading needs an effective leaf detection algorithm to ensure accurate results in segmentation and feature extraction. Leaf detection in this research used Haar cascade classifier and image processing techniques to automatically detect tobacco leaves in images. The proposed detection algorithm was implemented through OpenCV Python. The Haar cascade classifier was trained with 1,000 images and tested with 150 images. To improve the detection results of the classifier and ultimately detecting tobacco leaves, image processing techniques such as converting RGB to grayscale, blurring, thresholding, and finding connected components were applied. The experimental results show that the classifier can successfully distinguish tobacco leaves from other objects even those having resemblance to the characteristics of tobacco leaves in terms of color and shape. The accuracy rate of at least 91.33% proves the capability of the Haar cascade classifier to detect single and multiple tobacco leaves posed at different angles and taken at different distances from the camera. After applying some image processing techniques, the detection rate reached 100.00% and took 62 ms on average.","PeriodicalId":324982,"journal":{"name":"Proceedings of the 2nd International Conference on Graphics and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128801595","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: 5

CNN-Based CAD for Breast Cancer Classification in Digital Breast Tomosynthesis 基于cnn的CAD在数字乳腺断层合成中的乳腺癌分类

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3282305

J. Yeh, Siwa Chan

{"title":"CNN-Based CAD for Breast Cancer Classification in Digital Breast Tomosynthesis","authors":"J. Yeh, Siwa Chan","doi":"10.1145/3282286.3282305","DOIUrl":"https://doi.org/10.1145/3282286.3282305","url":null,"abstract":"Digital breast tomosynthesis (DBT) is a promising new technique for breast cancer diagnosis. DBT has the potential to overcome the tissue superimposition problems that occur on traditional mammograms for tumor detection. However, DBT generates numerous images, thereby creating a heavy workload for radiologists. Therefore, constructing an automatic computer-aided diagnosis (CAD) system for DBT image analysis is necessary. This study compared feature-based CAD and convolutional neural network (CNN)-based CAD for breast cancer classification from DBT images. The research methods included image preprocessing, candidate tumor identification, three-dimensional feature generation, classification, image cropping, augmentation, CNN model design, and deep learning. The accuracy rates (standard deviation) of the CNN- and feature-based CAD for breast cancer classification were 74.85% (0.122) and 87.12% (0.035), respectively. The T value was -6.229, and the P value was 0.00 < 0.05, which indicated that the CNN-based CAD significantly outperformed feature-based CAD. The results can be applied to clinical medicine and assist radiologists in breast cancer identification.","PeriodicalId":324982,"journal":{"name":"Proceedings of the 2nd International Conference on Graphics and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115878383","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: 2

Speech Based Deception Detection Using Bispectral Analysis 基于双谱分析的语音欺骗检测

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3282287

Md. Saiful Islam, Nursadul Mamun, M. S. Ullah

{"title":"Speech Based Deception Detection Using Bispectral Analysis","authors":"Md. Saiful Islam, Nursadul Mamun, M. S. Ullah","doi":"10.1145/3282286.3282287","DOIUrl":"https://doi.org/10.1145/3282286.3282287","url":null,"abstract":"Speech is considered as one of the most efficient and effective way to communicate with each other. However, a deception is a very common phenomenon in speech communication. It is difficult to detect if anyone is actually telling the truth or not. This study proposes a neural response based novel technique to identify the true or false from speech. In this study, the speech signal is used as the input to the auditory nerve model. This technique applies the higher order statistics called bispectrum to the auditory neurogram to distinguish the true and false from speech. Different parameters of the bispectrum are used as a feature to detect a deception from speech. Deceptive speech can be detected accurately by using the 'normalized bispectral entropy' of the bispectrum feature parameters for the envelope information (ENV) data and the 'maximum bispectrum' of the bispectrum feature parameter for the temporal fine structure (TFS) data. Speech based deception detection is a speech processing method which provides better accuracy to detect deception than many other deception detection techniques. This technique could be applied effectively for the national security systems.","PeriodicalId":324982,"journal":{"name":"Proceedings of the 2nd International Conference on Graphics and Signal Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131949061","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: 1

Analysis of High Hydrostatic Pressure HHP of Liquid Using Computational Fluid Dynamics CFD 基于计算流体力学CFD的高静水压力流体高压分析

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3282296

Ghani Albaali, Ruba Al-Mahasneh

引用次数: 0

SRCNN: Cardiovascular Vulnerable Plaque Recognition with Salient Region Proposal Networks 心血管易损斑块识别与突出区域建议网络

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI: 10.1145/3282286.3282297

Sijie Liu, Yangyang Deng, J. Xin, Weiliang Zuo, Peiwen Shi, Nanning Zheng

{"title":"SRCNN: Cardiovascular Vulnerable Plaque Recognition with Salient Region Proposal Networks","authors":"Sijie Liu, Yangyang Deng, J. Xin, Weiliang Zuo, Peiwen Shi, Nanning Zheng","doi":"10.1145/3282286.3282297","DOIUrl":"https://doi.org/10.1145/3282286.3282297","url":null,"abstract":"Vulnerable plaques recognition from IVOCT images is a valuable yet challenging task for computer-aided diagnosis and treatment of cardiovascular diseases. However, most existing supervised methods only used one kind of annotation information, and so they didn't fully and effectively utilize biomedical image information. In this paper, we propose a single, unified salient-regions-based convolutional neural network (SRCNN) to address this challenging task. The proposed SRCNN takes advantage of multi-annotation information (i.e., classification labels and segmentation labels) and combines prior knowledge of cardiologists. Our contributions in this paper are as follows: (i) We employ a bi-branch network combining the annotation information of classification and segmentation to recognize vulnerable plaques in IVOCT images. (2) According to prior knowledge of cardiologists, we construct a salient region proposal network (SRPN) that can propose irregular salient regions different from bounding boxes. (3) We embed SRPN in the bi-branch network through an appropriate merging strategy, and call this new bi-branch network SRCNN. Our proposed SRCNN is evaluated on the 2017 CCCV-IVOCT Challenge dataset. And ablation experiments demonstrate that compared to separate networks, the bi-branch network can improve the performance of classification and segmentation simultaneously. Furthermore, they also show SRPN contributes to extracting more discriminative features and boosting the whole performance of recognizing vulnerable plaques in IVOCT images greatly.","PeriodicalId":324982,"journal":{"name":"Proceedings of the 2nd International Conference on Graphics and Signal Processing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125588176","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: 2