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vision and inertial sensor cooperation using gravity as a vertical reference

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2003年第12期25卷 1597-1608页

作者： Lobo, J Dias, J Univ Coimbra ISR P-3030290 Coimbra Portugal Univ Coimbra Dept Elect & Comp Engn P-3030290 Coimbra Portugal

This paper explores the combination of inertial sensor data with vision. Visual and inertial sensing are two sensory modalities that can be explored to give robust solutions on image segmentation and recovery of 3D structure from images, increasing the capabilities of autonomous robots and enlarging the application potential of vision systems. In biological systems, the information provided by the vestibular system is fused at a very early processing stage with vision, playing a key role on the execution of visual movements such as gaze holding and tracking, and the visual cues aid the spatial orientation and body equilibrium. In this paper, we set a framework for using inertial sensor data in vision systems, and describe some results obtained. The unit sphere projection camera model is used, providing a simple model for inertial data integration. Using the vertical reference provided by the inertial sensors, the image horizon line can be determined. Using just one vanishing point and the vertical, we can recover the camera's focal distance and provide an external bearing for the system's navigation frame of reference. Knowing the geometry of a stereo rig and its pose from the inertial sensors, the collineation of level planes can be recovered, providing enough restrictions to segment and reconstruct vertical features and leveled planar patches.

关键词： image processing and computer vision edge and feature detection sensor fusion

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A framework for the evaluation of volume rendering techniques on a task specific basis using neural networks

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computerS & GRAPHICS-UK 2001年第4期25卷 643-663页

作者： Braines, SA Cant, RJ Nottingham Trent Univ Dept Comp Simulat & Modelling Grp Nottingham NG1 4BU England

This paper tests the use of neural networks to evaluate the effectiveness of volume rendering techniques. The advantage of using neural networks is that they are able to give a task-specific evaluation of an algorithm. In a practical application a specific training and testing regime would be constructed. This could then be used to decide on the appropriate algorithm for the application. In order that this methodology itself can be evaluated, a number of volume rendering algorithms are implemented and several different "tasks" are defined. For each task/algorithm combination a neural network is trained and tested. These results provide an evaluation of the algorithm for that task. Because the purpose of this paper is to investigate the neural network evaluation methodology itself, not the algorithms, the techniques chosen include some simplistic ones that have well understood deficiencies, to provide an objective test. It was found that the neural net methodology clearly detected the deficiencies, and so could be considered an effective framework for calibrated testing of problem-specific algorithms. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： fundamentals of computer graphics image processing and computer vision artificial intelligence

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A unified framework for local visual descriptors evaluation

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PATTERN RECOGNITION 2015年第4期48卷 1174-1184页

作者： Kihl, Olivier Picard, David Gosselin, Philippe-Henri Univ Cergy Pontoise ETIS ENSEA CNRS UMR 8051 F-95014 Cergy Pontoise France INRIA Rennes Bretagne Atlantique F-35042 Rennes France

Local descriptors are the ground layer of recognition feature based systems for still images and video. We propose a new framework for the design of local descriptors and their evaluation. This framework is based on the descriptors decomposition in three levels: primitive extraction, primitive coding and code aggregation. With this framework, we are able to explain most of the popular descriptors in the literature such as HOG, HOF or SURF. This framework provides an efficient and rigorous approach for the evaluation of local descriptors, and allows us to uncover the best parameters for each descriptor family. Moreover, we are able to extend usual descriptors by changing the code aggregation or adding new primitive coding method. The experiments are carried out on images (VOC 2007) and videos datasets (KTH, Hollywood2, UCF11 and UCF101), and achieve equal or better performances than the literature. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： image processing and computer vision vision and scene understanding Video analysis image/video retrieval Object recognition Feature representation

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Keypoint recognition using randomized trees

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2006年第9期28卷 1465-1479页

作者： Lepetit, Vincent Fua, Pascal Ecole Polytech Fed Lausanne Comp Vis Lab CH-1015 Lausanne Switzerland

In many 3D object-detection and pose-estimation problems, runtime performance is of critical importance. However, there usually is time to train the system, which we will show to be very useful. Assuming that several registered images of the target object are available, we developed a keypoint-based approach that is effective in this context by formulating wide-baseline matching of keypoints extracted from the input images to those found in the model images as a classification problem. This shifts much of the computational burden to a training phase, without sacrificing recognition performance. As a results, the resulting algorithm is robust, accurate, and fast-enough for frame-rate performance. This reduction in runtime computational complexity is our first contribution. Our second contribution is to show that, in this context, a simple and fast keypoint detector suffices to support detection and tracking even under large perspective and scale variations. While earlier methods require a detector that can be expected to produce very repeatable results, in general, which usually is very time-consuming, we simply find the most repeatable object keypoints for the specific target object during the training phase. We have incorporated these ideas into a real-time system that detects planar, nonplanar, and deformable objects. It then estimates the pose of the rigid ones and the deformations of the others.

关键词： image processing and computer vision object recognition tracking statistical classifier design and evaluation edge and feature detection

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Detecting Fall Risk Factors for Toddlers

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IEEE PERVASIVE COMPUTING 2011年第1期10卷 82-89页

作者： Na, Hana Qin, Sheng-Feng Wright, David K. Brunel Univ Sch Engn & Design Uxbridge UB8 3PH Middx England

Preventing accidental injuries of toddlers requires thorough, consistent supervision, but this isn't always practical. A proposed vision-based system detects three fall risk factors in the home environment to help caregivers supervise nearby toddlers when they can't give continuous attention to the toddlers. The crucial technical challenge is to differentiate a human from other foreground objects in the images. Unlike previous systems, this one uses multiple dynamic motion cues for human detection, employing cues related to human appearance.

关键词： Real Time Systems Implementation Pattern Recognition Computing Methodologies image processing and computer vision Computing Methodologies Health Life And Medical Sciences computer Applications Pervasive Computing

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A General Decoupled Learning Framework for Parameterized image Operators

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第1期43卷 33-47页

作者： Fan, Qingnan Chen, Dongdong Yuan, Lu Hua, Gang Yu, Nenghai Chen, Baoquan Stanford Univ Comp Sci Dept Stanford CA 94305 USA Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230026 Anhui Peoples R China Microsoft Res Redmond WA 98052 USA Wormpex AI Res Bellevue WA 98004 USA Peking Univ Beijing 100871 Peoples R China

Many different deep networks have been used to approximate, accelerate or improve traditional image operators. Among these traditional operators, many contain parameters which need to be tweaked to obtain the satisfactory results, which we refer to as "parameterized image operators". However, most existing deep networks trained for these operators are only designed for one specific parameter configuration, which does not meet the needs of real scenarios that usually require flexible parameters settings. To overcome this limitation, we propose a new decoupled learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network. The learned algorithm is formed as another network, namely the weight learning network, which can be end-to-end jointly trained with the base network. Experiments demonstrate that the proposed framework can be successfully applied to many traditional parameterized image operators. To accelerate the parameter tuning for practical scenarios, the proposed framework can be further extended to dynamically change the weights of only one single layer of the base network while sharing most computation cost. We demonstrate that this cheap parameter-tuning extension of the proposed decoupled learning framework even outperforms the state-of-the-art alternative approaches.

关键词： image processing and computer vision filtering restoration smoothing

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Infinitely divisible cascades to model the statistics of natural images

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2007年第12期29卷 2105-2119页

作者： Chainais, Pierre Univ Blaise Pascal Clermont Ferrand II CNRS UMR LIMOS F-63173 Aubiere France

We propose to model the statistics of natural images, thanks to the large class of stochastic processes called Infinitely Divisible Cascades ( IDCs). IDCs were first introduced in one dimension to provide multifractal time series to model the so- called intermittency phenomenon in hydrodynamical turbulence. We have extended the definition of scalar IDCs from one to N dimensions and commented on the relevance of such a model in fully developed turbulence in [ 1]. In this paper, we focus on the particular 2D case. IDCs appear as good candidates to model the statistics of natural images. They share most of their usual properties and appear to be consistent with several independent theoretical and experimental approaches of the literature. We point out the interest of IDCs for applications to procedural texture synthesis.

关键词： stochastic processes picture/image generation fractals image processing and computer vision statistical image models

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Building a Globally Optimized Computational Intelligent image processing Algorithm for On-Site Inference of Nitrogen in Plants

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IEEE INTELLIGENT SYSTEMS 2018年第3期33卷 15-26页

作者： Sulistyo, Susanto B. Woo, W. L. Dlay, S. S. Gao, Bin Newcastle Univ Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Intelligent Signal Proc Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Signal Proc Anal Newcastle Upon Tyne Tyne & Wear England Univ Elect Sci & Technol China Nondestruct Testing Chengdu Sichuan Peoples R China

Estimating nutrient content in plants is a crucial task in the application of precision farming. This work will be more challenging if it is conducted nondestructively based on plant images captured in the field due to the variation of lighting conditions. This paper proposes a computational intelligence image processing to analyze nitrogen status in wheat plants. We developed an ensemble of deep learning multilayer perceptron-using committee machines for color normalization and image segmentation. This paper also focuses on building a genetic-algorithm-based global optimization to fine tune the color normalization and nitrogen estimation results. We discovered that the proposed method can successfully normalize plant images by reducing color variabilities compared to other color normalization techniques. Furthermore, this algorithm is able to enhance the nitrogen estimation results compared to other non-global optimization methods as well as the most renowned SPAD meter based nitrogen measurement.

关键词： Crops Genetic Algorithms image Capture image Colour Analysis image Segmentation Learning Artificial Intelligence Multilayer Perceptrons Nitrogen Nondestructive Testing Lighting Conditions Computational Intelligence image processing Wheat Plants Deep Learning Multilayer Perceptron Using Committee Machines image Segmentation Genetic Algorithm Based Global Optimization Color Variabilities Color Normalization Techniques Nonglobal Optimization Methods Renowned SPAD Meter Based Nitrogen Measurement Precision Farming Nondestructive Testing Plant image Capture SPAD Meter Nitrogen Measurement image Color Analysis Nitrogen Feature Extraction image Segmentation Estimation Machine Learning Neural Networks computer vision Agricultural Engineering Plants Biology Diagnostics System Architectures Integration And Modeling image processing and computer vision

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Robust Visual Tracking Using an Adaptive Coupled-Layer Visual Model

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013年第4期35卷 941-953页

作者： Cehovin, Luka Kristan, Matej Leonardis, Ales Univ Ljubljana Fac Comp & Informat Sci SI-1001 Ljubljana Slovenia Univ Ljubljana Fac Elect Engn SI-1001 Ljubljana Slovenia Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Univ Birmingham Ctr Computat Neurosci & Cognit Robot Birmingham B15 2TT W Midlands England

This paper addresses the problem of tracking objects which undergo rapid and significant appearance changes. We propose a novel coupled-layer visual model that combines the target's global and local appearance by interlacing two layers. The local layer in this model is a set of local patches that geometrically constrain the changes in the target's appearance. This layer probabilistically adapts to the target's geometric deformation, while its structure is updated by removing and adding the local patches. The addition of these patches is constrained by the global layer that probabilistically models the target's global visual properties, such as color, shape, and apparent local motion. The global visual properties are updated during tracking using the stable patches from the local layer. By this coupled constraint paradigm between the adaptation of the global and the local layer, we achieve a more robust tracking through significant appearance changes. We experimentally compare our tracker to 11 state-of-the-art trackers. The experimental results on challenging sequences confirm that our tracker outperforms the related trackers in many cases by having a smaller failure rate as well as better accuracy. Furthermore, the parameter analysis shows that our tracker is stable over a range of parameter values.

关键词： image processing and computer vision tracking

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Three-Dimensional Residual Neural Architecture Search for Ultrasonic Defect Detection

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2024年第3期71卷 423-436页

作者： McKnight, Shaun MacKinnon, Christopher Pierce, S. Gareth Mohseni, Ehsan Tunukovic, Vedran MacLeod, Charles N. Vithanage, Randika K. W. O'Hare, Tom Univ Strathclyde Ctr Ultrason Engn CUE Elect & Elect Engn Dept Sensor Enabled Automat Robot & Control Hub SEARCH Glasgow G1 1XQ Scotland Spirit AeroSyst Belfast BT3 9DZ North Ireland

This study presents a deep-learning (DL) methodology using 3-D convolutional neural networks (CNNs) to detect defects in carbon fiber-reinforced polymer (CFRP) composites through volumetric ultrasonic testing (UT) data. Acquiring large amounts of ultrasonic training data experimentally is expensive and time-consuming. To address this issue, a synthetic data generation method was extended to incorporate volumetric data. By preserving the complete volumetric data, complex preprocessing is reduced, and the model can utilize spatial and temporal information that is lost during imaging. This enables the model to utilize important features that might be overlooked otherwise. The performance of three architectures was compared. The first architecture is prevalent in the literature for the classification of volumetric datasets. The second demonstrated a hand-designed approach to architecture design, with modifications to the first architecture to address the challenges of this specific task. A key modification was the use of cuboidal kernels to account for the large aspect ratios seen in ultrasonic data. The third architecture was discovered through neural architecture search (NAS) from a modified 3-D residual neural network (ResNet) search space. In addition, domain-specific augmentation methods were incorporated during training, resulting in significant improvements in model performance, with a mean accuracy improvement of 22.4% on the discovered architecture. The discovered architecture demonstrated the best performance with a mean accuracy increase of 7.9% over the second-best model. It was able to consistently detect all defects while maintaining a model size smaller than most 2-D ResNets. Each model had an inference time of less than 0.5 s, making them efficient for the interpretation of large amounts of data. [GRAPHICS]

关键词： image processing and computer vision neural architecture search (NAS) nondestructive evaluation (NDE) synthetic data ultrasonic testing (UT) volumetric

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