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World Congress on Intelligent Control and Automation (WCICA)

作者： Wenhao Xu Yan Zhuang Huosheng Hu Yiwen Zhao School of Control Science and Engineering Dalian University of Technology Dalian China School of Computer Science and Electronic Engineering University of Essex Colchester U.K. State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang China

This paper investigates real-time road detection for an unmanned ground vehicle (UGV) to navigate in a campus environment. A novel vision system with two monocular cameras at different heights and angles is utilized to accomplish both road region detection and direction estimation tasks simultaneously. An Adaboost-based classifier is used in the road region detection in order to handle the road surface's diversity, and a vanishing point tracking technique is used to estimate the road direction. To describe the unstructured road spaces with an accurate model, a RANSAC Spline Fitting algorithm is adopted to delineate the road borders based on the data fusion results from vision and lasers. Extensive experiments are carried out by using a real UGV platform in a campus environment and results show the feasibility and performance of the proposed approach.

关键词： Roads Cameras Navigation Splines (mathematics) Laser fusion Three-dimensional displays Fitting

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Comparative study of leaf image recognition with a novel learning-based approach

Comparative study of leaf image recognition with a novel lea...

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science and Information Conference (SAI)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chih-Yang Lin Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse coding-based framework can perform better.

关键词： Image recognition Dictionaries Training Feature extraction Shape Vectors Image coding

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The upregulation of Myb and Peg3 may mediate EGCG inhibition effect on mouse lung adenocarcinoma

The upregulation of Myb and Peg3 may mediate EGCG inhibition...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Hong Zhou Joseph Manthey Ekaterina Lioutikova William Yang Kenji Yoshigoe Mary Qu Yang Hong Wang Department of Mathematical Sciences School of Health and Natural Sciences University of Saint Joseph Department of Computer Science George Washington Donaghey College of Engineering and Information Technology University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Middle South Bioinformatics Center and Joint Bioinformatics Ph.D. Program George Washington Donaghey College of Engineering and Information Technology University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Susan L. Cullman Laboratory for Cancer Research Department of Chemical Biology and Center for Cancer Prevention Research Ernest Mario School of Pharmacy Rutgers The State University of New Jersey

ISBN: (纸本)9781467368001

The antioxidant activity of green tea polyphenol epigallocatechin-3-gallate (EGCG) has been found to be critical in inhibiting carcinogenesis. In our previous study, we identified a set of protein coding genes and microRNAs whose expressions were significantly modulated in response to the EGCG treatment in tobacco carcinogen-induced lung adenocarcinoma in A/J mice. In this study, we further conducted some statistical analysis on our microarray data and employed The Cancer Genome Atlas (TCGA) lung adenocarcinoma datasets as additional control. We postulated that if a gene mediates EGCG's cancer inhibition, its expression level change caused by EGCG should be opposite to what occurred in the carcinogenesis. With this assumption, we identified Myb and Peg3 as the primary genes involved in the cancer inhibitory activities of EGCG.

关键词： EGCG Myb Peg3 Lung adenocarcinoma

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Multi-scale bio-inspired place recognition

Multi-scale bio-inspired place recognition

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zetao Chen Adam Jacobson Uğur M. Erdem Michael E. Hasselmo Michael Milford School of Electrical Engineering Computer Science at the Queensland University of Technology Center for Memory and Brain and Graduate Program for Neuroscience at Boston University

This paper presents a novel place recognition algorithm inspired by the recent discovery of overlapping and multi-scale spatial maps in the rodent brain. We mimic this hierarchical framework by training arrays of Support Vector Machines to recognize places at multiple spatial scales. Place match hypotheses are then cross-validated across all spatial scales, a process which combines the spatial specificity of the finest spatial map with the consensus provided by broader mapping scales. Experiments on three real-world datasets including a large robotics benchmark demonstrate that mapping over multiple scales uniformly improves place recognition performance over a single scale approach without sacrificing localization accuracy. We present analysis that illustrates how matching over multiple scales leads to better place recognition performance and discuss several promising areas for future investigation.

关键词： Training Support vector machines Feature extraction Rodents Image segmentation Robot sensing systems

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Determining mood via emotions observed in face by induction

Determining mood via emotions observed in face by induction

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Mojgan Hashemian Hadi Moradi Maryam S Mirian Mehdi Tehrani-doost Advanced Robotics and Intelligent Systems Lab University of Tehran Iran School of Electrical and Computer Engineering University of Tehran Iran Department of Psychiatry Tehran University of Medical Science Iran

Determining the mood of a person is an important step in the Human-Robot interaction. In this paper, we propose a human-inspired approach in which the changes in emotions, done using emotion induction, can be used to determine the mood of a person. The emotion induction, which can be done through robot actions or through showing video clips, stimulates changes in the emotions of the person, reducing the observation time needed to estimate mood. Consequently, the changes in the emotions, which are biased by his/her mood can be used by a robot to determine the mood of a person. To do so, we induced happy emotions by showing a comical clip and measured the intensity of each happy and sad emotions, and also the intensity of the neutral state. Then we extracted a feature set, including both time and frequency domain features, which is used to determine the mood of the person. The approach has been implemented and compared to no-emotion-induction approach and shows better results. Based on the classification results, the approach is able to distinguish between good vs. bad moods with the accuracy of 91.5% with 0.1 mean absolute error. But the neutral state was not distinguishable well.

关键词： Mood Robots Accuracy Feature extraction Face Educational institutions Human-robot interaction

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Learning sparse representation for leaf image recognition

Learning sparse representation for leaf image recognition

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chao-Yung Hsu Chia-Yen Chen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Institute of Information Science Taipei Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. For each test leaf image, we calculate the correlation between the image and each learned dictionary of leaf species to achieve the identification of the leaf image. As a result, efficient leaf recognition can be achieved on public leaf dataset based on the proposed framework leading to a more compact and richer representation of leaf images compared to traditional clustering approaches. Moreover, our method is also adapted to newly added leaf species without retraining classifiers and suitable to be highly parallelized as well as integrated with any leaf image descriptors/features.

关键词： Image recognition Dictionaries Training Feature extraction Shape Educational institutions computer vision

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Determining mood using emotional features

Determining mood using emotional features

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International Symposium on Telecommunications, IST

作者： Mojgan Hashemian Amin Nikoukaran Hadi Moradi Maryam S Mirian Mehdi Tehrani-doost Advanced Robotics and Intelligent Systems Lab University of Tehran Iran School of Electrical and Computer Engineering University of Tehran Iran Department of Psychiatry Tehran University of Medical Science Iran

The ability to determine mood is one of fundamental challenges in affective computing. In this paper, we present a novel approach for mood detection via emotional variations. In this approach, the mood is considered as a low magnitude and more stable, i.e. low frequency, emotion that can be detected using emotion detection approaches. A Bayes classification is applied on a feature vector composed of statistical aspects of the intensity of the emotions. The approach has been implemented in which two emotions, i.e. happiness and sadness, and also neutral state, have been targeted to determine the good, bad, and neutral, mood of subjects respectively. A Bayes classification is applied on a feature vector containing statistical aspects of the intensity of the emotions. The obtained Correct Classification Rate (CCR) is 91.1, with 0.09 mean error and variance of 4.9 discriminating good mood vs. neutral.

关键词： Mood Educational institutions Face Videos computers Mice Accuracy

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Multi-camera based human localization for room utilization monitoring system

Multi-camera based human localization for room utilization m...

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International Joint Conference on computer science and Software engineering (JCSSE)

作者： Issariyapol Siriwat Thanaruk Theeramankong Ithipan Methasate Hiroaki Kunieda ICTES Program Thammasat University Thailand National Electronics and Computer Technology Center Thailand School of Science and Engineering Tokyo Institute of Technology Japan

ISBN: (纸本)9781479958238

Room utilization monitoring system is an interesting application towards optimized facility usage. Due to its cost effectiveness and robustness, a camera-based system for room utilization is focused. This paper presents a multiple camera based process for room utilization monitoring system. The system is composed with three parts of processes that are single-camera processing, multi-camera processing and room event detection. In single camera processing, we have presented the object detection and tracking and transform the detected position on the image to the room map using Homography. The results of single camera processing are the location on the room map of detected object which are the input for multiple camera data fusion. We have purposed three different methods on multiple camera data fusion that are Uniform Bias Weighting, Best Camera Selection and Error Bias Weighting. And then we use the best detected object location on the map for room event detection. The experiment verified the performance of the different data fusion method on multiple cameras and showed that Error Bias weighting provide the best result.

关键词： Component Room utilization Multiple camera data fusion Object detection and tracking multiple camera Object detection Process cameras room utilization monitoring systems Data fusion data integration maps Event detection Cameras THREE-PART

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Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

Robust multiple-sensing-modality data fusion using Gaussian ...

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International Conference on Information Fusion

作者： Marcos P. Gerardo-Castro Thierry Peynot Fabio Ramos Robert Fitch Australian Centre for Field Robotics (ACFR) The University of Sydney 2006 NSW Australia School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane QLD Australia School of Information Technologies The University of Sydney NSW Australia

ISBN: (纸本)9781479916344

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.

关键词： Robot sensing systems Laser radar Data integration Surface treatment Uncertainty Robustness

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Corrigendum to "Target-specific near-IR induced drug release and photothermal therapy with accumulated Au/Ag hollow nanoshells on pulmonary cancer cell membranes" [Biomaterials 45 (2015) 81-92]

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Biomaterials 2015年 65卷 124-125页

作者： Mi Suk Noh Somin Lee Homan Kang Jin-Kyoung Yang Hyunmi Lee Doyk Hwang Jong Woo Lee Sinyoung Jeong Yoonjeong Jang Bong-Hyun Jun Dae Hong Jeong Seong Keun Kim Yoon-Sik Lee Myung-Haing Cho Laboratory of Toxicology College of Veterinary Medicine Seoul National University Seoul 151-742 Republic of Korea Program in Nano Science and Technology Graduate School of Convergence Science and Technology Seoul National University Seoul 151-742 Republic of Korea Medical Supplies Evaluation Center Korea Testing Certification Gunpo 435-862 Republic of Korea. Graduate Group of Tumor Biology Seoul National University Seoul 151-742 Republic of Korea. Interdisciplinary Program in Nano-Science and Technology Seoul National University Seoul 151-742 Republic of Korea School of Electrical Engineering and Computer Science Seoul National University Seoul 151-742 Republic of Korea. School of Chemical and Biological Engineering Seoul National University Seoul 151-742 Republic of Korea. Department of Biophysics and Chemical Biology and Department of Chemistry Seoul National University Seoul 151-742 Republic of Korea. Department of Chemistry Education Seoul National University Seoul 151-742 Republic of Korea. Laboratory of Toxicology College of Veterinary Medicine Seoul National University Seoul 151-742 Republic of Korea. Department of Bioscience and Biotechnology Konkuk University Seoul 143-701 Republic of Korea. Department of Chemistry Education Seoul National University Seoul 151-742 Republic of Korea. School of Chemical and Biological Engineering Seoul National University Seoul 151-742 Republic of Korea. Electronic address: yslee@snu.ac.kr. Graduate Group of Tumor Biology Seoul National University Seoul 151-742 Republic of Korea Advanced Institute of Convergence Technology Seoul National University Suwon 443-270 Republic of Korea. Electronic address: mchotox@snu.ac.kr.

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