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EIP-VIO: Edge-Induced Points Based Monocular Visual-Inertial Odometry

EIP-VIO: Edge-Induced Points Based Monocular Visual-Inertial...

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ieee International symposium on Parallel and Distributed processing with Applications and ieee International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Luoying Hao Hongjian Li Jun Cheng Qieshi Zhang Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China The Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)9781728152103

Edge and line features boost the performance of simultaneous localization and mapping (SLAM) systems while bring more additional computational cost. In this paper, we propose an improved and practical monocular visual-inertial odometry method based on selective edge points. Focus on solving the issues of mismatching and tracking loss, the auxiliary edge is utilized to supervise the selection of features instead of using rough features directly. Features along edges are employed as stronger keypoints to steer point-selection away from less distinctive points. By combining matched edge-guided feature based method, selective sampling keypoints along edges based direct method and inertial measurement unit (IMU), the performance of absolute pose estimation is more accurate and efficient. In addition, a weight adjustment factor scheme before IMU error term is introduced in our optimization model to further reduce the influence of motion blur and less texture. Experimental results on public benchmark dataset demonstrate that the proposed method is more efficient and effective compared with the state-of-the-art methods.

关键词： distance measurement edge detection feature extraction image matching pose estimation SLAM (robots) pose estimation distance measurement image matching Edge detection Feature extraction motion blur odometry MISMATCH simultaneous localization and mapping edges Edge of a figure or solid

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Robust Estimator based Adaptive Multi-Task Learning

Robust Estimator based Adaptive Multi-Task Learning

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ieee symposium Series on computational intelligence (SSCI)

作者： Peiyuan Zhu Cailian Chen Jianping He Shanying Zhu Key Laboratory of System Control and Signal Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Multi-Task Learning(MTL) algorithm leverages valuable information across similar tasks to improve regression accuracy. However, applications in practice often encounter scenes that cannot give an accurate relationship between the large-scale number of multiple tasks. When task clustering based method is employed in those cases, excessive connections between dissimilar tasks and meaningless dimensions would cause a poorly predictive performance, which cannot be perfectly interpreted by existing MTL methods. In this paper, a novel MTL method is proposed to make more accurate use of valuable shared information between multiple tasks, where a redescending robust estimator is utilized to adaptively unify the continuous clustering of a large-scale number of tasks and dynamically selecting valuable features of few-shot tasks. To enable a better description of relationships between multiple tasks, we formulate a multi-convex objective function that can be optimized alternatively. After analyzing the complexity and convexity of the problem, we provide a scalable solving approach which can converge to the optimum with approximately linear time complexity. Compared with state-of-the-art models, the proposed approach performs better RMSE score and time efficiency both in synthetic and realistic datasets. Meanwhile, with similar computational overhead, the experiment demonstrates that our method has better regression accuracy than clustering tasks alone or selecting valuable features individually.

关键词： Task analysis Robustness Linear programming Feature extraction Optimization Time complexity Clustering algorithms

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A Proposal of a Multi-Objective Compact Particle Swarm Optimizer

A Proposal of a Multi-Objective Compact Particle Swarm Optim...

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ieee symposium Series on computational intelligence (SSCI)

作者： Jorge Jiménez Montiel Carlos A. Coello Coello Ma. Guadalupe Castillo Tapia Departamento de Computación CINVESTAV-IPN Mexico City MEXICO Departamento de Administración UAM Azcapotzalco Mexico City MEXICO

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Throughout the years, several bio-inspired meta-heuristics have been proposed to solve multi-objective problems. Nevertheless, most of the current metaheuristics are not suitable for applications having limited resources (e.g., limited available memory or computationally expensive objective function evaluations). In recent years, a wide variety of metaheuristics have been proposed that employ a statistical representation of the population through a probabilities vector. These are the so-called compact metaheuristics. Several metaheuristics of the state of the art have used a statistical representation to reduce the amount of memory required to be implemented in devices with limited computing resources. This paper presents a compact metaheuristic based on a particle swarm optimizer (PSO) for solving continuous and unconstrained multi-objective optimization problems. Our proposed approach is compared with respect to two multi-objective particle swarm optimizers (MOPSOs) and one compact multi-objective evolutionary algorithm (MOEA). The results indicate that our proposed approach is competitive with respect to the other MOPSOs and is able to outperform the compact MOEA used in our comparative study in most of the test problems adopted.

关键词： Particle swarm optimization Genetic algorithms Optimization Proposals Memory management signal processing algorithms Urban areas

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An Improved Ant Colony Optimization Algorithm Based on Fractional Order Memory for Traveling Salesman Problems

An Improved Ant Colony Optimization Algorithm Based on Fract...

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ieee symposium Series on computational intelligence (SSCI)

作者： Xiaoling Gong Ziheng Rong Tao Gao Yifei Pu Jian Wang College of Control Science and Engineering China University of Petroleum (East China) Qingdao China College of Science China University of Petroleum (East China) Qingdao China College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Ant Colony Optimization (ACO) algorithm has a wide array of applications to solve combinatorial optimization problems, especially Traveling Salesman Problems (TSPs). The major limitations of ACO algorithm are premature convergence, the possibility that trapped in the local optima. In this paper, an improved Ant Colony Optimization algorithm is proposed which uses fractional order difference for pheromone updating and a weighted combined transition probability. The fractional order difference with the characteristic of long-term memory helps the algorithm make full use of the historical information, and the combined transition probability enhances the exploration ability of the algorithm by using the information of a few steps forward. The performance of the proposed algorithm is tested on various data sets from the standard TSP Library compared with the corresponding integer order algorithm and some evolutionary algorithms. According to the empirical results, our algorithm based on fractional order difference overcomes the classic integer order. Furthermore, the results on a number of TSP instances demonstrate that compared with other evolutionary algorithms, the proposed method can obtain the better solutions on most instances with stronger robustness.

关键词： Urban areas Optimization signal processing algorithms Fractional calculus Heuristic algorithms Ant colony optimization Traveling salesman problems

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Method to Obtain Neuromorphic Reservoir Networks from images of in Vitro Cortical Networks

Method to Obtain Neuromorphic Reservoir Networks from Images...

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ieee symposium Series on computational intelligence (SSCI)

作者： Gustavo Borges Moreno e Mello Sidney Pontes-Filho Ioanna Sandvig Vibeke Devold Valderhaug Evi Zouganeli Ola Huse Ramstad Axel Sandvig Stefano Nichele Dept. of Mech. Elec. and Chem. Engineering Oslo Metropolitan University Oslo Norway Dept. of Information Technology Oslo Metropolitan University Oslo Norway Dept. of Neuromedicine and Movement Science Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

In the brain, the structure of a network of neurons defines how these neurons implement the computations that underlie the mind and the behavior of animals and humans. Provided that we can describe the network of neurons as a graph. We can employ methods from graph theory to investigate its structure or use cellular automata to mathematically assess its function. Additionally, these graphs can provide biologically plausible designs for networks, which can be integrated as reservoirs to support computing. Although, software for the analysis of graphs and cellular automata are widely available. Graph extraction from the image of networks of brain cells remains difficult. Nervous tissue is heterogeneous, and differences in anatomy may reflect relevant differences in function. Here we introduce a deep learning based toolbox to extracts graphs from images of brain tissue. This toolbox provides an easy- to-use framework allowing system neuroscientists to generate graphs based on images of brain tissue by combining methods from image processing, deep learning, and graph theory. The goals are to simplify the training and usage of deep learning methods for computer vision and facilitate its integration into graph extraction pipelines. In this way, the toolbox provides an alternative to the required laborious manual process of tracing, sorting and classifying. We expect to democratize the machine learning methods to a wider community of users beyond the computer vision experts and improve the time-efficiency of graph extraction from large brain image datasets, which may lead to further understanding of the human mind.

关键词： Pipelines Neurons Machine learning image segmentation Training Computer vision

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Three-dimensional Segmentation of Blood Vessels from Intensity In-homogeneous Medical images 8

Three-dimensional Segmentation of Blood Vessels from Intensi...

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8th ieee symposium Series on computational intelligence (ieee SSCI)

作者： Bukenya, Faiza Awwad, Amir Duan, Jinming Ehling, Josef Faas, Henryk Bai, Li Univ Nottingham Sch Comp Sci Nottingham England Univ Nottingham Sch Med Nottingham England Imperial Coll London Sch Comp London England Rhein Westfal TH Aachen Dept Expt Mol Imaging Aachen Germany

ISBN: (纸本)9781538692769

Blood vessel segmentation helps to know the progress of the disease during diagnosis. The presence of intensity in-homogeneity, conglutination tissue and noise in medical images has led to difficulty in extraction of different sizes of blood vessels, difficulty in separating vessels for further analysis (such as quantification of angiogenesis), difficulty in distinguishing vessels from non vessels. Most of the available techniques are 2D-based. Despite the fact that 2D-based segmentation is easy, it doesn't provide full information about anatomic structure. Most of the available 3D vessel segmentation techniques require human intervention and fail to segment different sizes of vessels. In this paper, a 3D hybrid approach for segmentation has been developed, based on white top hat scale space bilateral hessian vessel enhancement filter and hysteresis threshold method combined with multi-threshold Otsu method. The hybrid method can address noise and intensity in-homogeneity problem, as a result, more vessels of different sizes are detected. The method can also incorporate spatial information, abnormalities in the vessels are detected. Vessels of different sizes are separated to ease further analysis. Conglutination tissue (that obstruct segmentation process) is eliminated to ease extraction of different sizes of vessels.

关键词： three-dimension segmentation medical imaging blood vessels image processing

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Investigation of EEG Noise and Artifact Removal by Patch-Based and Kernel Adaptive Filtering Techniques 13

Investigation of EEG Noise and Artifact Removal by Patch-Bas...

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13th ieee International symposium on Medical Measurements and Applications (MeMeA)

作者： Ghanem, Noha H. Eltrass, Ahmed S. Ismail, Nour H. Alexandria Univ Elect Engn Dept Fac Engn Alexandria Egypt

ISBN: (纸本)9781538633922

Electroencephalography (EEG) signals are usually corrupted with several unwanted noise and artifact sources, which lead to poor signal quality and wrong clinical diagnosis. This paper investigates, for the first time, a 1-D implementation of the patch-based NonLocal Means (NLM) algorithm, which was used for 2-D image processing, in removing (1) the Additive White Gaussian Noise (AWGN) and (2) artifacts from EEG signals. A critical comparison between the NLM approach and one of the most effective adaptive filtering techniques, Kernel Recursive Least Squares (KRLS), is made for both removing the AWGN noise and artifact sources. This comparison is demonstrated by investigating several EEG datasets using different evaluation metrics such as the signal Noise Ratio (SNR), Mean Square Error (MSE), cross correlation, and computational time. The results show that the NLM-based approach more effectively reduces the AWGN/colored noise than the KRLS adaptive filtering. The results also reveal that the NLM approach fails to remove the Electrocardiogram (ECG) artifact in an artifacts contaminated EEG signal, while the KRLS adaptive filter significantly removes most of this ECG component. This reveals that the patch-based techniques are efficient and promising in removing the AWGN/colored noise sources but they are less successful in suppressing interference artifacts.

关键词： Electroencephalography Electrocardiography Kernel AWGN signal to noise ratio Adaptive filters Filtering"

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MULTIPLE INSTANCE LEARNING OF DEEP CONVOLUTIONAL NEURAL NETWORKS FOR BREAST HISTOPATHOLOGY WHOLE SLIDE CLASSIFICATION 15

MULTIPLE INSTANCE LEARNING OF DEEP CONVOLUTIONAL NEURAL NETW...

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15th ieee International symposium on Biomedical Imaging (ISBI)

作者： Das, Kausik Conjeti, Sailesh Roy, Abhijit Guha Chatterjee, Jyotirmoy Sheet, Debdoot IIT Kharagpur Dept Elect Engn Kharagpur W Bengal India IIT Kharagpur Sch Med Sci & Technol Kharagpur W Bengal India Tech Univ Munich Chair Comp Aided Med Procedures Munich Germany Ludwig Maximilians Univ Munchen KJP Artificial Intelligence Med Imaging Munich Germany

ISBN: (纸本)9781538636367

Whole slide histopathology images are the digitised version of physical slides that are acquired by stitching multiple overlapping image patches acquired at different optical magnifications. Typically sized at 2 GB per slide, computer aided analysis for whole slide pathology classification is a challenging task. Since local image patches generally provide more discriminative information as compared to the whole slide, challenges are associated with handling pathological heteregeneity varying across the whole slide. Given this challenge to classify the slide pathology using local image patches, in this paper we propose a multiple instance learning (MIL) framework for convolutional neural network (CNN). We introduce a new pooling layer that helps to aggregate most informative features from patches constituting a whole slide, without necessitating inter-patch overlap or global slide coverage. This helps our method to jointly learn to discover informative features locally as well as learn the classification margin globally;without the explicit need for individually annotating each local image patch in the training data. We have experimentally evaluated performance using a patient level 5-folded cross-validation with 58 malignant and 24 benign cases of breast tumors to obtain best performance of 89.52%, 89.06%, 88.84%, 87.67% accuracy at 40x, 100x, 200x and 400x magnifications respectively while processing each slide in under 40 ms.

关键词： Convolutional neural network multiple instance learning histopathology image analysis multi-scale analysis whole slide image analysis

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DEEP CONTEXTUAL RESIDUAL NETWORK FOR ELECTRON MICROSCOPY image SEGMENTATION IN CONNECTOMICS 15

DEEP CONTEXTUAL RESIDUAL NETWORK FOR ELECTRON MICROSCOPY IMA...

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15th ieee International symposium on Biomedical Imaging (ISBI)

作者： Xiao, Chi Liu, Jing Chen, Xi Han, Hua Shu, Chang Xie, Qiwei Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai Peoples R China

ISBN: (纸本)9781538636367

The goal of connectomics research is to manifest the mechanisms and functions of neural system by using electron microscopy (EM). One of the biggest challenges in connectomic reconstruction is developing reliable neuronal membranes segmentation method to reduce the burden on manual neurite labeling and validation. In this paper, we put forward an effective deep learning approach to realize neuronal membranes segmentation in EM image stacks, which utilizes spatially efficient residual network and multilevel representations of contextual cues to achieve accurate segmentation performance. Furthermore, multicut is used as post-processing to optimize the outputs of network. Experimental results on the public dataset of ISBI 2012 EM Segmentation Challenge demonstrate the effectiveness of our approach in neuronal membranes segmentation. Our method now ranks top 3 among 88 teams and yields 0.98356 Rand Score as well as 0.99063 Information Score, which outperforms most of state-of-the-art methods.

关键词： Connectomics Deep Learning image Segmentation Electron Microscopy

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ISCIIA and ITCA 2018 - 8th International symposium on computational intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications

ISCIIA and ITCA 2018 - 8th International Symposium on Comput...

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8th International symposium on computational intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018

The proceedings contain 83 papers. The topics discussed include: face recognition algorithm for person identification based on multi-layer information fusion convolutional neural network;multi-focus image fusion based on neural network;fault detection of networked singularly perturbed systems under the stochastic communication protocol;application of CAN transmission technology in fault diagnosis system;study on pump intelligent diagnosis technology of oil field pumpjack;network intrusion detection based on deep belief network and PSO-SVM;model verification and validation strategies and methods: an application case study;design and implementation of tobacco growth evaluation system base on image processing;identification of low frequency oscillation mode shape based on VMD method and ambient signal;and wind farm sub-synchronous oscillation identification based on improved variational mode decomposition.

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