2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)最新文献_第4页

Parasite Detection in Thick Blood Smears Based on Customized Faster-RCNN on Smartphones 基于智能手机定制Faster-RCNN的厚血涂片寄生虫检测

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174565

Feng Yang, Hang Yu, K. Silamut, R. Maude, Stefan Jaeger, Sameer Kiran Antani

{"title":"Parasite Detection in Thick Blood Smears Based on Customized Faster-RCNN on Smartphones","authors":"Feng Yang, Hang Yu, K. Silamut, R. Maude, Stefan Jaeger, Sameer Kiran Antani","doi":"10.1109/AIPR47015.2019.9174565","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174565","url":null,"abstract":"Malaria is a worldwide life-threatening disease. The gold standard for malaria diagnosis is microscopy examination, which includes thick blood smears to detect the presence of parasites and thin blood smears to differentiate the development stages of parasites. Microscopy examination is of low cost but is time consuming and error-prone. Therefore, the development of an automated parasite detection system for malaria diagnosis in thick blood smears is an important research goal, especially in resource-limited areas. In this paper, based on a customized Faster-RCNN model, we develop a machine-learning system that can automatically detect parasites in thick blood smear images on smartphones. To make Faster-RCNN more efficient for small object detection, we split an input image of $4032 times 3024 times3$ pixels into small blocks of $252 times 189 times3$ pixels, and then train the FasterRCNN model with the small blocks and corresponding parasite annotations. Moreover, we customize the convolutional layers of Faster-RCNN with four convolutional layers and two maxpooling layers to extract features according to the input image size and characteristics. We perform experiments on 2967 thick blood smear images from 200 patients, including 1819 images from 150 patients who are infected with parasites. The customized FasterRCNN model is first trained on small image blocks from 120 patients, including 90 infected patients and 30 normal patients, and then tested on the remaining 80 patients. For testing, we also split each input image into small blocks of $252 times 189 times3$ pixels that are screened by our trained Faster-RCNN model to detect parasite coordinates, which are then re-projected into the original image space. Detection rates of our system on image level and patient level are 96.84% and 96.81%, respectively.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127181167","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: 8

What’s the Point? Using Extended Feature Sets For Semantic Segmentation in Point Clouds 有什么意义?基于扩展特征集的点云语义分割

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174600

Nina M. Varney, V. Asari

引用次数: 0

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning 利用物理模拟和机器学习探索碳纳米管森林合成-结构关系

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9316542

T. Hajilounezhad, Zakariya A. Oraibi, Ramakrishna Surya, F. Bunyak, M. Maschmann, P. Calyam, K. Palaniappan

{"title":"Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning","authors":"T. Hajilounezhad, Zakariya A. Oraibi, Ramakrishna Surya, F. Bunyak, M. Maschmann, P. Calyam, K. Palaniappan","doi":"10.1109/AIPR47015.2019.9316542","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9316542","url":null,"abstract":"The parameter space of CNT forest synthesis is vast and multidimensional, making experimental and/or numerical exploration of the synthesis prohibitive. We propose a more practical approach to explore the synthesis-process relationships of CNT forests using machine learning (ML) algorithms to infer the underlying complex physical processes. Currently, no such ML model linking CNT forest morphology to synthesis parameters has been demonstrated. In the current work, we use a physics-based numerical model to generate CNT forest morphology images with known synthesis parameters to train such a ML algorithm. The CNT forest synthesis variables of CNT diameter and CNT number densities are varied to generate a total of 12 distinct CNT forest classes. Images of the resultant CNT forests at different time steps during the growth and self-assembly process are then used as the training dataset. Based on the CNT forest structural morphology, multiple single and combined histogram-based texture descriptors are used as features to build a random forest (RF) classifier to predict class labels based on correlation of CNT forest physical attributes with the growth parameters. The machine learning model achieved an accuracy of up to 83.5% on predicting the synthesis conditions of CNT number density and diameter. These results are the first step towards rapidly characterizing CNT forest attributes using machine learning. Identifying the relevant process-structure interactions for the CNT forests using physics-based simulations and machine learning could rapidly advance the design, development, and adoption of CNT forest applications with varied morphologies and properties.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132731155","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: 14

Improving Industrial Safety Gear Detection through Re-ID conditioned Detector 利用Re-ID条件检测器改进工业安全齿轮检测

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174597

Manikandan Ravikiran, Shibashish Sen

{"title":"Improving Industrial Safety Gear Detection through Re-ID conditioned Detector","authors":"Manikandan Ravikiran, Shibashish Sen","doi":"10.1109/AIPR47015.2019.9174597","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174597","url":null,"abstract":"Industrial safety gears such as hardhats, vests, gloves and goggles are vital in safety of workers. With the advancement of vision technologies, most industries are moving towards automatic safety monitoring systems for its enforcement. However, most of the industrial safety monitoring systems are plagued by the following problems. To begin with, object detection which is the principal component of this system suffers from the problem of false detections and missed detections which are extremely costly resulting in wrong safety monitoring alerts and safety hazards. Further, while video object detection has seen a large traction through ImagenetDet and MOT17Det challenges, to the best of our knowledge there is no work till date in the context of industrial safety. Finally, unlike existing areas of object detection where there is the availability of large datasets, best of existing research works in detecting industrial safety gears is restricted to mostly hardhats due to lack of large datasets. In this work, we address these previously mentioned challenges by presenting a unified industrial safety system. As part of this developed system, we firstly introduce safety gear detection dataset consisting of 5k images with the previously mentioned classes of safety gears and present exhaustive benchmark on state-of-the-art single frame object detection. Secondly, to address wrong/missed detections we propose to exploit temporal information from contiguous frames by conditioning the object detection in the current frame on results of re-identification of objects computed in prior frames. Finally, we conduct extensive experiments using the developed Re-ID conditioned object detection system with various state-of-the-art object detectors to show that the proposed system produces mAP of 85%, 87%, 92% and 78% with average improvements of 5% mAP across the previously mentioned safety gears under complex conditions of illumination, posture and occlusions.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114434259","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

Surgery Task Classification Using Procrustes Analysis 基于Procrustes分析的手术任务分类

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174566

Safaa Albasri, M. Popescu, James Keller

{"title":"Surgery Task Classification Using Procrustes Analysis","authors":"Safaa Albasri, M. Popescu, James Keller","doi":"10.1109/AIPR47015.2019.9174566","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174566","url":null,"abstract":"Recognizing surgical tasks is a crucial step toward automatic surgical training in robotic surgery training. In this work, we proposed and developed a classification framework for surgical task recognition. This approach is based on using three components: Dynamic Time Warping (DTW), Procrustes analysis (PA), and Fuzzy k- nearest neighbor (FkNN). First, the DTW method processes multi-channel motion trajectories with different lengths by stretching and compressing both signals such that their lengths become identical. Second, Procrustes analysis is used as a distance measure between two sequences based on shape similarity transformations: rotations, reflection, scaling, and translation. Finally, a Fuzzy k-nearest neighbor algorithm is applied to distinguish between different tasks by assigning a fuzzy class membership based on their distances. We evaluated our framework on a real raw kinematic surgical robotic dataset. Then, we validated the proposed model using Leave One Supertrial Out (LOSO) and Leave One User Out (LOUO) cross-validation schemes. Our results show improvements in the classification of the three different Robot-assisted minimally invasive surgery (RMIS) tasks: suturing, needle-passing, and knot-tying.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132313727","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

Adaptive Online Learning for Human-Robot Teaming in Dynamic Environments 动态环境下人机团队的自适应在线学习

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174572

Alexander D. Wissner-Gross, Noah Weston, Manuel M. Vindiola

引用次数: 0

4-D Scene Alignment in Surveillance Video 监控视频中的4d场景对齐

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-06-04 DOI: 10.1109/AIPR47015.2019.9174582

R. Wagner, Daniel E. Crispell, Patrick Feeney, J. Mundy

引用次数: 2