2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)最新文献_第3页

Bioinspired Algorithms used in Material Modeling and Numerical Simulation of Metal Processing: Plenary Talk 生物启发算法在材料建模和金属加工数值模拟中的应用:全体会议

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-07-01 DOI: 10.1109/iwobi47054.2019.9114389

Wei Shi

引用次数: 0

AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach AF-DBSCAN:一种基于DBSCAN方法的无监督自动模糊聚类方法

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-07-01 DOI: 10.1109/IWOBI47054.2019.9114411

S. Jebari, A. Smiti, Aymen Louati

{"title":"AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach","authors":"S. Jebari, A. Smiti, Aymen Louati","doi":"10.1109/IWOBI47054.2019.9114411","DOIUrl":"https://doi.org/10.1109/IWOBI47054.2019.9114411","url":null,"abstract":"Automatic clustering problems play an important role to ameliorate the goodness of the data set's partitioning. Actually, the requirement to detect the suitable clustering solution without need for user-given parameters still remain challenging in unsupervised learning. This paper proposes an efficient and effective clustering method, named AF-DBSCAN (Automatic Fuzzy DBSCAN) based on the fuzzy clustering method FN-DBSCAN (Fuzzy Neighborhood Density-Based Spatial Clustering of Applications with Noise). The main idea of the proposed method is to cover the limitations of FN-DBSCAN by exploiting the benefits of k-neighbors plot, in purpose to determine the input parameter values. In fact, AF-DBSCAN avoids the manual intervention of non-experimental users in estimating the input parameters, the minimal threshold of neighborhood membership degree ∊1 and the minimal neighborhood set cardinality ∊2, which are hard to guess, and so permits to determine them more reasonably. In such way, the whole clustering process can be fully automated. Simulation experiments, carried out on a real medical data set, highlighted the AF-DBSCAN's effectiveness even for high-dimensions data sets, and showed that the proposed method outperformed the classical method since it provides a better clustering accuracy.","PeriodicalId":427695,"journal":{"name":"2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122176075","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: 4

A Brief Analysis of U-Net and Mask R-CNN for Skin Lesion Segmentation 浅析U-Net和Mask R-CNN在皮肤损伤分割中的应用

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-07-01 DOI: 10.1109/IWOBI47054.2019.9114436

Erick Alfaro, Ximena Bolaños Fonseca, E. M. Albornoz, Cesar E. Martínez, S. C. Ramírez

引用次数: 11

Design, Modeling and Control of a Biologically-Inspired Bat Robot: Plenary Talk 生物启发蝙蝠机器人的设计、建模与控制:全体会议

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-07-01 DOI: 10.1109/IWOBI47054.2019.9114480

S. Hutchinson

引用次数: 0

MedAR Medical Augmented Reality MedAR医疗增强现实

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-07-01 DOI: 10.1109/IWOBI47054.2019.9114462

László Szücs, Muluye Yared Yaregal, M. Kozlovszky

引用次数: 1

Eye corners tracking for head movement estimation 眼部角跟踪头部运动估计

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-07-01 DOI: 10.1109/IWOBI47054.2019.9114393

Agostina J. Larrazabal, Cecilia E. Garcia Cena, Cesar E. Martínez

{"title":"Eye corners tracking for head movement estimation","authors":"Agostina J. Larrazabal, Cecilia E. Garcia Cena, Cesar E. Martínez","doi":"10.1109/IWOBI47054.2019.9114393","DOIUrl":"https://doi.org/10.1109/IWOBI47054.2019.9114393","url":null,"abstract":"Recently, video-oculographic gaze tracking has begun to be used in the diagnosis of a wide variety of neurological diseases, such as Parkinson and Alzheimer. For this application, the so-called feature-based methods are used, more precisely, 2D regression-based methods. They use geometrically derived eye features from high-resolution eye images captured by zooming into the user's eyes. The main weakness of these methods is that the head of the user must remain motionless to avoid estimation errors. In some patients, some involuntary movements cannot be avoided and it is necessary to measure them. In this paper, we tackle the measurement of head position as a way to improve the gaze tracking on these precision demanding medical applications. As a first stage, we propose to obtain the eye corners coordinates as a reference point, since they are the most stable points in front of the eyeball and eyelids movements. The problem was handled as a regression problem using a coarse-to-fine cascaded convolutional neural network in order to accurately regress the coordinates of the eye corner. Particularly, with the aim of achieving high precision we cascade two levels of convolutional networks. Finally, we added temporal information to increase accuracy and decrease computation time. The accuracy of the estimation was calculated from the mean square error between the predictions and the ground truth. Subjective performance was also evaluated through video inspection. In both cases, satisfactory results were obtained.","PeriodicalId":427695,"journal":{"name":"2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133247959","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

Estimation of wind intensity data from reanalysis data using a shallow neural network 利用浅层神经网络从再分析数据估计风强度数据

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-07-01 DOI: 10.1109/IWOBI47054.2019.9114455

Dionisio Rodríguez-Esparragón, J. Marcello, N. M. Betancort, C. Gonzalo-Martín

引用次数: 0

Automated TMA-Core-Detection Algorithm 自动tma核心检测算法

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-07-01 DOI: 10.1109/IWOBI47054.2019.9114446

Lilla Élo, Róbert Paulik, G. Kiszler, T. Micsik, Tamás Székely, H. Hajdú, M. Kozlovszky, B. Molnár

引用次数: 0

Gesture Recognition with 3D Sensors using Hidden Markov Models and Clustering 基于隐马尔可夫模型和聚类的三维传感器手势识别

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-07-01 DOI: 10.1109/IWOBI47054.2019.9114513

Tobias Steinmetzer, Simon Piatraschk, Ingrid Bönninger, C. Travieso-González, Barbara Priwitzer

引用次数: 0

Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study 利用深度学习从距离图像中恢复超二次曲线的初步研究

2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2019-04-13 DOI: 10.1109/IWOBI47054.2019.9114452

T. Oblak, Klemen Grm, A. Jaklič, P. Peer, V. Štruc, F. Solina

{"title":"Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study","authors":"T. Oblak, Klemen Grm, A. Jaklič, P. Peer, V. Štruc, F. Solina","doi":"10.1109/IWOBI47054.2019.9114452","DOIUrl":"https://doi.org/10.1109/IWOBI47054.2019.9114452","url":null,"abstract":"It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand and interact with their surroundings. Such models are typically motivated by human visual perception and aim to represents all elements of the physical word ranging from individual objects to complex scenes using a small set of parameters. One of the de facto stadards to approach this problem are superquadrics - volumetric models that define various 3D shape primitives and can be fitted to actual 3D data (either in the form of point clouds or range images). However, existing solutions to superquadric recovery involve costly iterative fitting procedures, which limit the applicability of such techniques in practice. To alleviate this problem, we explore in this paper the possibility to recover superquadrics from range images without time consuming iterative parameter estimation techniques by using contemporary deep-learning models, more specifically, convolutional neural networks (CNNs). We pose the superquadric recovery problem as a regression task and develop a CNN regressor that is able to estimate the parameters of a superquadric model from a given range image. We train the regressor on a large set of synthetic range images, each containing a single (unrotated) superquadric shape and evaluate the learned model in comparaitve experiments with the current state-of-the-art. Additionally, we also present a qualitative analysis involving a dataset of real-world objects. The results of our experiments show that the proposed regressor not only outperforms the existing state-of-the-art, but also ensures a $270times$ faster execution time.","PeriodicalId":427695,"journal":{"name":"2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115665626","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