Intelligent vehicle modeling design based on image processing

With the urgent demand of consumers for diversified automobile modeling, simple, efficient, and intelligent automobile modeling analysis and modeling method is an urgent problem to be solved in current automobile modeling design. The purpose of this article is to analyze the modeling preference and trend of the current automobile market in time, which can assist the modeling design of new models of automobile main engine factories and strengthen their branding family. Intelligent rapid modeling shortens the current modeling design cycle, so that the product rapid iteration is to occupy an active position in the automotive market. In this article, aiming at the family analysis of automobile front face, the image database of automobile front face modeling analysis was created. The database included two data sets of vehicle signs and no vehicle signs, and the image data of vehicle front face modeling of most models of 22 domestic mainstream brands were collected. Then, this article adopts the image classification processing method in computer vision to conduct car brand classification training on the database. Based on ResNet-8 and other model architectures, it trains and classifies the intelligent vehicle brand classification database with and without vehicle label. Finally, based on the shape coefficient, a 3D wireframe model and a curved surface model are obtained. The experimental results show that the 3D curve model can be obtained based on a single image from any angle, which greatly shortens the modeling period by 92%.

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Spatio-Temporal Change Monitoring of Outside Manure Piles Using Unmanned Aerial Vehicle Images

Drones ◽

10.3390/drones5010001 ◽

2020 ◽

Vol 5 (1) ◽

pp. 1

Author(s):

Geonung Park ◽

Kyunghun Park ◽

Bonggeun Song

Keyword(s):

Point Source ◽

Laser Scanning ◽

Image Data ◽

Surface Model ◽

Point Source Pollution ◽

Efficient Management ◽

Aerial Vehicle ◽

Spatio Temporal ◽

Agricultural Areas ◽

Water Quality Deterioration

Water quality deterioration due to outdoor loading of livestock manure requires efficient management of outside manure piles (OMPs). This study was designed to investigate OMPs using unmanned aerial vehicles (UAVs) for efficient management of non-point source pollution in agricultural areas. A UAV was used to acquire image data, and the distribution and cover installation status of OMPs were identified through ortho-images; the volumes of OMP were calculated using digital surface model (DSM). UAV- and terrestrial laser scanning (TLS)-derived DSMs were compared for identifying the accuracy of calculated volumes. The average volume accuracy was 92.45%. From April to October, excluding July, the monthly average volumes of OMPs in the study site ranged from 64.89 m3 to 149.69 m3. Among the 28 OMPs investigated, 18 were located near streams or agricultural waterways. Establishing priority management areas among the OMP sites distributed in a basin is possible using spatial analysis, and it is expected that the application of UAV technology will contribute to the efficient management of OMPs and other non-point source pollutants.

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The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren ◽

10.1055/a-1244-2775 ◽

2020 ◽

Author(s):

Daniel Overhoff ◽

Peter Kohlmann ◽

Alex Frydrychowicz ◽

Sergios Gatidis ◽

Christian Loewe ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Image Data ◽

Quality Parameters ◽

Image Features ◽

Performance Criteria ◽

Data Sets ◽

Proof Of Concept ◽

Concept Study ◽

Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points: Citation Format

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Evaluation of Unsupervised Clustering Methods on Hyperspectral Image Data Sets

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) ◽

10.1109/pic.2018.8706315 ◽

2018 ◽

Author(s):

Wei Zhang ◽

Zhichao Lian ◽

Chanying Huang

Keyword(s):

Hyperspectral Image ◽

Image Data ◽

Unsupervised Clustering ◽

Data Sets ◽

Clustering Methods ◽

Hyperspectral Image Data

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Fast Similarity Search in High Dimensional Image Data Sets

Proceedings of the 2nd international Conference on Big Data, Cloud and Applications ◽

10.1145/3090354.3090426 ◽

2017 ◽

Author(s):

Y. Hanyf ◽

H. Silkan

Keyword(s):

Similarity Search ◽

Image Data ◽

High Dimensional ◽

Data Sets ◽

Dimensional Image

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Integrating Geophysical and Photographic Data to Visualize the Quarried Structures of the Roman Town of Bassianae

Remote Sensing ◽

10.3390/rs13122384 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2384

Author(s):

Roland Filzwieser ◽

Vujadin Ivanišević ◽

Geert J. Verhoeven ◽

Christian Gugl ◽

Klaus Löcker ◽

...

Keyword(s):

Ground Penetrating Radar ◽

Urban Infrastructure ◽

Aerial Photographs ◽

Surface Model ◽

Data Sets ◽

Buried Structures ◽

Archaeological Prospection ◽

Ground Penetrating ◽

Geophysical Prospection ◽

Strong Agreement

Large parts of the urban layout of the abandoned Roman town of Bassianae (in present-day Serbia) are still discernible on the surface today due to the deliberate and targeted quarrying of the Roman foundations. In 2014, all of the town's intramural (and some extramural) areas were surveyed using aerial photography, ground-penetrating radar, and magnetometry to analyze the site's topography and to map remaining buried structures. The surveys showed a strong agreement between the digital surface model derived from the aerial photographs and the geophysical prospection data. However, many structures could only be detected by one method, underlining the benefits of a complementary archaeological prospection approach using multiple methods. This article presents the results of the extensive surveys and their comprehensive integrative interpretation, discussing Bassianae's ground plan and urban infrastructure. Starting with an overview of this Roman town's research history, we present the details of the triple prospection approach, followed by the processing, integrative analysis, and interpretation of the acquired data sets. Finally, this newly gained information is contrasted with a plan of Roman Bassianae compiled in 1935.

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Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

Microscopy and Microanalysis ◽

10.1017/s1431927603030058 ◽

2003 ◽

Vol 9 (1) ◽

pp. 1-17 ◽

Cited By ~ 217

Author(s):

Paul G. Kotula ◽

Michael R. Keenan ◽

Joseph R. Michael

Keyword(s):

Image Data ◽

Large Data ◽

Automated Analysis ◽

Phase Identification ◽

Data Sets ◽

Multivariate Statistical ◽

X Ray ◽

Analysis Technique ◽

Statistical Analysis Technique ◽

Multi Phase

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

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Measuring the Morphology and Dynamics of the Snake River by Remote Sensing

The UW National Parks Service Research Station Annual Reports ◽

10.13001/uwnpsrc.2011.3773 ◽

2011 ◽

Vol 33 ◽

pp. 3-15

Author(s):

Carl Legleiter

Keyword(s):

Remote Sensing ◽

Image Data ◽

River System ◽

Field Work ◽

Hyperspectral Data ◽

Snake River ◽

Central Component ◽

Data Sets ◽

Channel Form ◽

And Behavior

The Snake River is a central component of Grand Teton National Park, and this dynamic fluvial system plays a key role in shaping the landscape and creating diverse aquatic and terrestrial habitat. The riverâs complexity and propensity for change make effective characterization of this resource difficult, however, and conventional, ground-based methods are simply inadequate. Remote sensing provides an appealing alternative approach that could facilitate resource management while providing novel insight on the factors controlling channel form and behavior. In this study, we evaluate the potential to measure the morphology and dynamics of a large, complex river system such as the Snake using optical image data. Initially, we made use of existing, publicly available images and basic digital aerial photography acquired in August 2010. Analysis to date has focused on estimating flow depths from these data, and preliminary results indicate that remote bathymetric mapping is feasible but not highly accurate, with important constraints related to the limited radiometric resolution of these data sets. Additional, more sophisticated hyperspectral data are scheduled for collection in 2011, along with further field work.

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An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

10.26686/wgtn.13158281.v1 ◽

2020 ◽

Author(s):

Ying Bi ◽

Bing Xue ◽

Mengjie Zhang

Keyword(s):

Feature Extraction ◽

Genetic Programming ◽

Image Classification ◽

Image Data ◽

Data Sets ◽

Automatic Feature Extraction ◽

Terminal Set ◽

Comparable Performance ◽

International Publishing ◽

High Level

© Springer International Publishing AG, part of Springer Nature 2018. Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.

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DETECTION OF CRACKS IN PAVED ROAD SURFACE USING LASER SCAN IMAGE DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b1-559-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 559-562 ◽

Cited By ~ 3

Author(s):

J. Choi ◽

L. Zhu ◽

H. Kurosu

Keyword(s):

Image Segmentation ◽

Background Subtraction ◽

Three Dimensional ◽

Laser Scanner ◽

Image Data ◽

Road Surface ◽

Surface Model ◽

Rolling Ball ◽

The Difference ◽

Paved Road

In the current study, we developed a methodology for detecting cracks in the surface of paved road using 3D digital surface model of road created by measuring with three-dimensional laser scanner which works on the basis of the light-section method automatically. For the detection of cracks from the imagery data of the model, the background subtraction method (Rolling Ball Background Subtraction Algorithm) was applied to the data for filtering out the background noise originating from the undulation and gradual slope and also for filtering the ruts that were caused by wearing, aging and excessive use of road and other reasons. We confirmed the influence from the difference in height (depth) caused by forgoing reasons included in a data can be reduced significantly at this stage. Various parameters of ball radius were applied for checking how the result of data obtained with this process vary according to the change of parameter and it becomes clear that there are not important differences by the change of parameters if they are in a certain range radius. And then, image segmentation was performed by multi-resolution segmentation based on the object-based image analysis technique. The parameters for the image segmentation, scale, pixel value (height/depth) and the compactness of objects were used. For the classification of cracks in the database, the height, length and other geometric property are used and we confirmed the method is useful for the detection of cracks in a paved road surface.

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