检索结果-内蒙古大学图书馆

Three-Dimensional Residual Neural Architecture Search for Ultrasonic Defect Detection

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

来源：评论

学校读者我要写书评

暂无评论

Robust Visual Tracking Using an Adaptive Coupled-Layer Visual Model

引用

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

来源：评论

学校读者我要写书评

暂无评论

Robust structure and motion from outlines of smooth curved surfaces

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2006年第2期28卷 302-315页

作者： Furukawa, Y Sethi, A Ponce, J Kriegman, DJ Univ Illinois Beckman Inst Urbana IL 61801 USA Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA

This paper addresses the problem of estimating the motion of a camera as it observes the outline ( or apparent contour) of a solid bounded by a smooth surface in successive image frames. In this context, the surface points that project onto the outline of an object depend on the viewpoint and the only true correspondences between two outlines of the same object are the projections of frontier points where the viewing rays intersect in the tangent plane of the surface. In turn, the epipolar geometry is easily estimated once these correspondences have been identified. Given the apparent contours detected in an image sequence, a robust procedure based on RANSAC and a voting strategy is proposed to simultaneously estimate the camera configurations and a consistent set of frontier point projections by enforcing the redundancy of multiview epipolar geometry. The proposed approach is, in principle, applicable to orthographic, weak-perspective, and affine projection models. Experiments with nine real image sequences are presented for the orthographic projection case, including a quantitative comparison with the ground-truth data for the six data sets for which the latter information is available. Sample visual hulls have been computed from all image sequences for qualitative evaluation.

关键词： image processing and computer vision motion shape

来源：评论

学校读者我要写书评

暂无评论

Video Compression for Screen Recorded Sequences Following Eye Movements

引用

JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2021年第12期93卷 1457-1465页

作者： Jesus Serrano-Carrasco, Diego Jesus Diaz-Honrubia, Antonio Cuenca, Pedro Univ Castilla La Mancha Grp Redes & Arquitecturas Altas Prestn Castilla La Mancha Spain Univ Politecn Madrid Ctr Tecnol Biomed ETS Ingn Informat Madrid Spain

With the advent of smartphones and tablets, video traffic on the Internet has increased enormously. With this in mind, in 2013 the High Efficiency Video Coding (HEVC) standard was released with the aim of reducing the bit rate (at the same quality) by 50% with respect to its predecessor. However, new contents with greater resolutions and requirements appear every day, making it necessary to further reduce the bit rate. Perceptual video coding has recently been recognized as a promising approach to achieving high-performance video compression and eye tracking data can be used to create and verify these models. In this paper, we present a new algorithm for the bit rate reduction of screen recorded sequences based on the visual perception of videos. An eye tracking system is used during the recording to locate the fixation point of the viewer. Then, the area around that point is encoded with the base quantization parameter (QP) value, which increases when moving away from it. The results show that up to 31.3% of the bit rate may be saved when compared with the original HEVC-encoded sequence, without a significant impact on the perceived quality.

关键词： Signal image and Speech processing Circuits and Systems Electrical Engineering image processing and computer vision Pattern Recognition computer Imaging vision Pattern Recognition and Graphics

来源：评论

学校读者我要写书评

暂无评论

Underwater Single image Color Restoration Using Haze-Lines and a New Quantitative Dataset

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第8期43卷 2822-2837页

作者： Berman, Dana Levy, Deborah Avidan, Shai Treibitz, Tali Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel Univ Haifa Charney Sch Marine Sci Hatter Dept Marine Technol IL-3498838 Haifa Israel

Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We also contribute a dataset of 57 images taken in different locations. To obtain ground truth, we placed multiple color charts in the scenes and calculated its 3D structure using stereo imaging. This dataset enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time.

关键词： image color analysis Attenuation image restoration Channel estimation Three-dimensional displays Cameras Optical attenuators image processing and computer vision image enhancement computational photography image restoration image color analysis

来源：评论

学校读者我要写书评

暂无评论

Evaluating clustering quality using features salience: a promising approach

引用

NEURAL COMPUTING & APPLICATIONS 2021年第19期33卷 12939-12956页

作者： Dugue, Nicolas Lamirel, Jean-Charles Chen, Yue Univ Maine LIUM Lab Informat EA 4023 IC2Inst Informat Claude Chappe Ave Olivier Messiaen F-72000 Le Mans France LORIA Equipe Synalp Batiment B F-54506 Vandoeuvre Les Nancy France Dalian Univ Technol WiseLAB Dalian 116024 Liaoning Peoples R China

This paper focuses on using feature salience to evaluate the quality of a partition when dealing with hard clustering. It is based on the hypothesis that a good partition is an easy to label partition, i.e. a partition for which each cluster is made of salient features. This approach is mostly compared to usual approaches relying on distances between data, but also to more recent approaches based on entropy or stability. We show that our feature-based approach outperforms the compared indexes for optimal model selection: they are more efficient from low- to high-dimensional range as well as they are more robust to noise. To show the efficiency of our indexes on a real-life application, we consider the task of diachronic analysis on a textual dataset. We demonstrate that our approach allows to get some interesting and relevant results in that context, while other approaches mostly lead to unusable results.

关键词： Artificial Intelligence Data Mining and Knowledge Discovery Probability and Statistics in computer Science Computational Science and Engineering image processing and computer vision Computational Biology/Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Review of chart image detection and classification

引用

INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2023年第4期26卷 453-474页

作者： Bajic, Filip Job, Josip Univ Zagreb Univ Comp Ctr Zagreb 10000 Croatia Fac Elect Engn Comp Sci & Informat Technol Osijek Osijek 31000 Croatia

This paper presents a complete review of different approaches across all components of the chart image detection and classification up to date. A set of 89 scientific papers is collected, analyzed, and enlisted into four categories: chart-type classification, chart text processing, chart data extraction, and chart description generation. Detailed information about problem formulation and a research field is provided, and an overview of used methods in each category. Each paper's contribution is noted, including the essential information for authors in this research field. In the end, a comparison is made between the reported results. The state-of-the-art methods in each category are described, and a research direction is given. We have also analyzed the open challenges that still exist and require the author's attention.

关键词： Chart classification Chart image processing image processing and computer vision Information visualization Review and evaluation

来源：评论

学校读者我要写书评

暂无评论

RASHT: A Partially Reconfigurable Architecture for Efficient Implementation of CNNs

引用

IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2022年第7期30卷 860-868页

作者： Darbani, Paria Rohbani, Nezam Beitollahi, Hakem Lotfi-Kamran, Pejman Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran 193955531 Iran

Convolutional neural networks (CNNs) are widely used in machine learning (ML) applications such as image processing. CNN requires heavy computations to provide significant accuracy for many ML tasks. Therefore, the efficient implementations of CNNs to improve performance using limited resources without accuracy reduction is a challenge for ML systems. One of the architectures for the efficient execution of CNNs is the array-based accelerator, that consists of an array of similar processing elements (PEs). The array accelerators are popular as high-performance architecture using the features of parallel computing and data reuse. These accelerators are optimized for a set of CNN layers, not for individual layers. Using the same accelerator dimension size to compute all CNN layers with varying shapes and sizes leads to the resource underutilization problem. We propose a flexible and scalable architecture for array-based accelerator that increases resource utilization by resizing PEs to better match the different shapes of CNN layers. The low-cost partial reconfiguration improves resource utilization and performance, resulting in a 23.2% reduction in computational times of GoogLeNet compared to the state-of-the-art accelerators. The proposed architecture decreases the on-chip memory access rate by 26.5% with no accuracy loss.

关键词： computer architecture Convolutional neural networks Arrays Resource management System-on-chip Computational modeling Very large scale integration Array accelerator convolutional neural network (CNN) image processing and computer vision machine learning (ML) reconfigurable hardware

来源：评论

学校读者我要写书评

暂无评论

CyCoSeg: A Cyclic Collaborative Framework for Automated Medical image Segmentation

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第11期44卷 8167-8182页

作者： Medley, Daniela Santiago, Carlos Nascimento, Jacinto C. Inst Super Tecn Inst Sistemas & Robot P-1049001 Lisbon Portugal

Deep neural networks have been tremendously successful at segmenting objects in images. However, it has been shown they still have limitations on challenging problems such as the segmentation of medical images. The main reason behind this lower success resides in the reduced size of the object in the image. In this paper we overcome this limitation through a cyclic collaborative framework, CyCoSeg. The proposed framework is based on a deep active shape model (D-ASM), which provides prior information about the shape of the object, and a semantic segmentation network (SSN). These two models collaborate to reach the desired segmentation by influencing each other: SSN helps D-ASM identify relevant keypoints in the image through an Expectation Maximization formulation, while D-ASM provides a segmentation proposal that guides the SSN. This cycle is repeated until both models converge. Extensive experimental evaluation shows CyCoSeg boosts the performance of the baseline models, including several popular SSNs, while avoiding major architectural modifications. The effectiveness of our method is demonstrated on the left ventricle segmentation on two benchmark datasets, where our approach achieves one of the most competitive results in segmentation accuracy. Furthermore, its generalization is demonstrated for lungs and kidneys segmentation in CT scans.

关键词： image segmentation Collaboration Shape Deformable models Semantics Three-dimensional displays Standards Segmentation image processing and computer vision semantic networks machine learning

来源：评论

学校读者我要写书评

暂无评论

Local Geometric Indexing of High Resolution Data for Facial Reconstruction From Sparse Markers

引用

IEEE TRANSACTIONS ON VISUALIZATION AND computer GRAPHICS 2024年第8期30卷 5289-5298页

作者： Cong, Matthew Lan, Lana Fedkiw, Ronald Ind Light & Mag San Francisco CA 94129 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints. With the current trend towards using more and more data, our aim is not to fit the motion capture markers with a parameterized (blendshape) model or to smoothly interpolate a surface through the marker positions, but rather to find an instance in the high resolution dataset that contains local geometry to fit each marker. Just as is true for typical machine learning applications, this approach benefits from a plethora of data, and thus we also consider augmenting the dataset via specially designed physical simulations that target the high resolution dataset such that the simulation output lies on the same so-called manifold as the data targeted.

关键词： Shape Faces Geometry Surface reconstruction Cameras Point cloud compression Deformation computer graphics image processing and computer vision interpolation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：