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Bibliografias temáticas / Surface anomaly detection

Literatura científica selecionada sobre o tema "Surface anomaly detection"

Autor: Grafiati

Publicado: 23 de junho de 2021

Última modificação: 3 de fevereiro de 2022

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Artigos de revistas
Teses / dissertações
Capítulos de livros
Trabalhos de conferências

Artigos de revistas sobre o assunto "Surface anomaly detection":

1

Schwenk, J. Tyler, Steven D. Sloan, Julian Ivanov e Richard D. Miller. "Surface-wave methods for anomaly detection". GEOPHYSICS 81, n.º 4 (julho de 2016): EN29—EN42. http://dx.doi.org/10.1190/geo2015-0356.1.

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Perimeter-defense operations, geohazard assessment, and engineering characterization require the detection and localization of subsurface anomalies. Seismic waves incident upon these discontinuities generate a scattered wavefield. We have developed various surface-wave techniques, currently being fielded, that have consistently delivered accurate and precise results across a wide range of survey parameters and geographical locations. We use the multichannel analysis of surface waves approach to study the multimode Rayleigh wave, the backscatter analysis of surface waves (BASW) method to detect anomalies, 3D visualization for efficient seismic interpretation, BASW correlation for attribute analysis, and instantaneous-amplitude integration in the complex BASW method. Discrete linear moveout functions and [Formula: see text]-[Formula: see text] filter designs are optimized for BASW considering the fundamental and higher mode dispersion trends of the Rayleigh wave. Synthetic and field data were used to demonstrate multimode BASW and mode separation, which accentuated individual scatter events, and ultimately increased confidence in points of interest. Simple correlation algorithms between fundamental and higher-mode BASW data offer attribute analysis that limits the subjective interpretation of BASW images. Domain sorting and Hilbert transforms allow for 3D visualization and rapid interpretation of an anomaly’s wavefield phenomena within an amplitude cube. Furthermore, instantaneous-amplitude analysis can be incorporated into a more robust complex BASW method that forgives velocity-estimation inaccuracies, while requiring less rigorous preprocessing. Our investigations have suggested that a multifaceted surface-wave analysis provides a valuable tool for today’s geophysicists to fulfill anomaly-detection survey requirements.

2

Putri, A. R. D., P. Sidiropoulos e J. P. Muller. "ANOMALY DETECTION PERFORMANCE COMPARISON ON ANOMALY-DETECTION BASED CHANGE DETECTION ON MARTIAN IMAGE PAIRS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (5 de junho de 2019): 1437–41. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1437-2019.

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<p><strong>Abstract.</strong> The surface of Mars has been imaged in visible wavelengths for more than 40 years since the first flyby image taken by Mariner 4 in 1964. With higher resolution from orbit from MOC-NA, HRSC, CTX, THEMIS, and HiRISE, changes can now be observed on high-resolution images from different instruments, including spiders (Piqueux et al., 2003) near the south pole and Recurring Slope Lineae (McEwen et al., 2011) observable in HiRISE resolution. With the huge amount of data and the small number of datasets available on Martian changes, semi-automatic or automatic methods are preferred to help narrow down surface change candidates over a large area.</p><p>To detect changes automatically in Martian images, we propose a method based on a denoising autoencoder to map the first Martian image to the second Martian image. Both images have been automatically coregistered and orthorectified using ACRO (Autocoregistration and Orthorectification) (Sidiropoulos and Muller, 2018) to the same base image, HRSC (High-Resolution Stereo Camera) (Neukum and Jaumann, 2004; Putri et al., 2018) and CTX (Context Camera) (Tao et al., 2018) orthorectified using their DTMs (Digital Terrain Models) to reduce the number of false positives caused by the difference in instruments and viewing conditions. Subtraction of the codes of the images are then inputted to an anomaly detector to look for change candidates. We compare different anomaly detection methods in our change detection pipeline: OneClassSVM, Isolation Forest, and, Gaussian Mixture Models in known areas of changes such as Nicholson Crater (dark slope streak), using image pairs from the same and different instruments.</p>

3

Stolz, Bernadette J., Jared Tanner, Heather A. Harrington e Vidit Nanda. "Geometric anomaly detection in data". Proceedings of the National Academy of Sciences 117, n.º 33 (3 de agosto de 2020): 19664–69. http://dx.doi.org/10.1073/pnas.2001741117.

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The quest for low-dimensional models which approximate high-dimensional data is pervasive across the physical, natural, and social sciences. The dominant paradigm underlying most standard modeling techniques assumes that the data are concentrated near a single unknown manifold of relatively small intrinsic dimension. Here, we present a systematic framework for detecting interfaces and related anomalies in data which may fail to satisfy the manifold hypothesis. By computing the local topology of small regions around each data point, we are able to partition a given dataset into disjoint classes, each of which can be individually approximated by a single manifold. Since these manifolds may have different intrinsic dimensions, local topology discovers singular regions in data even when none of the points have been sampled precisely from the singularities. We showcase this method by identifying the intersection of two surfaces in the 24-dimensional space of cyclo-octane conformations and by locating all of the self-intersections of a Henneberg minimal surface immersed in 3-dimensional space. Due to the local nature of the topological computations, the algorithmic burden of performing such data stratification is readily distributable across several processors.

4

Tsai, Du-Ming, e Po-Hao Jen. "Autoencoder-based anomaly detection for surface defect inspection". Advanced Engineering Informatics 48 (abril de 2021): 101272. http://dx.doi.org/10.1016/j.aei.2021.101272.

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Sattar, Shahram, Songnian Li e Michael Chapman. "Road Surface Monitoring Using Smartphone Sensors: A Review". Sensors 18, n.º 11 (9 de novembro de 2018): 3845. http://dx.doi.org/10.3390/s18113845.

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Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

6

Liu, Gaokai, Ning Yang e Lei Guo. "An Attention-Based Network for Textured Surface Anomaly Detection". Applied Sciences 10, n.º 18 (8 de setembro de 2020): 6215. http://dx.doi.org/10.3390/app10186215.

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Textured surface anomaly detection is a significant task in industrial scenarios. In order to further improve the detection performance, we proposed a novel two-stage approach with an attention mechanism. Firstly, in the segmentation network, the feature extraction and anomaly attention modules are designed to capture the detail information as much as possible and focus on the anomalies, respectively. To strike dynamic balances between these two parts, an adaptive scheme where learnable parameters are gradually optimized is introduced. Subsequently, the weights of the segmentation network are frozen, and the outputs are fed into the classification network, which is trained independently in this stage. Finally, we evaluate the proposed approach on DAGM 2007 dataset which consists of diverse textured surfaces with weakly-labeled anomalies, and the experiments demonstrate that our method can achieve 100% detection rates in terms of TPR (True Positive Rate) and TNR (True Negative Rate).

7

Rasul, Azad, e Luqman W. Omar. "Land Surface Temperature Anomalies Detection for the Strong Earthquakes in 2018". ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 8, n.º 2 (1 de setembro de 2020): 15–21. http://dx.doi.org/10.14500/aro.10591.

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Earthquake every year leads to human and material losses and unpredictability of it by now makes this natural disaster worsen. The objective of the current study was to determine the anomalies in land surface temperature (LST) in areas affected by earthquakes. In this research, three earthquakes (M >6) were studied. Moderate Resolution Imaging Spectroradiometer Aqua and Terra day and night LST data used from 2003 to 2018. The interquartile range (IQR) and mean ± 2σ methods utilized to select anomalies. As a result, based on the IQR method, no prior and after anomaly detected in selected cases and data. Based on mean ± 2σ, usually positive anomaly occurred during daytime. However, negative (or positive) anomaly occurred during the nighttime before the Mexico and Bolivia earthquakes. During 10 days after the earthquake, sometimes a negative anomaly detected.

8

Ouyang Haoyi, 欧阳浩艺, 陈婉钧 Chen Wanjun, 李海 Li Hai e 杨初平 Yang Chuping. "平整表面反射率异常的单像素检测理论". Laser & Optoelectronics Progress 58, n.º 12 (2021): 1212003. http://dx.doi.org/10.3788/lop202158.1212003.

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Wong, Ze-Hao, C. M. Thong, W. M. Edmund Loh e C. J. Wong. "Surface Defect Detection using Novel Histogram Distance-based Multiple Template Anomalies Detection Algorithm". International Journal of Engineering & Technology 7, n.º 4.14 (24 de dezembro de 2019): 401. http://dx.doi.org/10.14419/ijet.v7i4.14.27693.

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Surface defects in manufacturing are top challenges in various manufacturing field including LED manufacturing, die manufacturing and printing industry. Quality control through automated surface defect detection has been an emphasis to speed up the production without jeopardizing the quality of the product. However, complexity and flexibility in product design, specification and dataset availability posted challenges in existing referential-based algorithm. Golden template-based algorithms are sensitive to misalignment and product variations. Deep learning and its variant can be used as non-linear filter to segment anomalies area. However, deep learning requires huge labelled database and consume long learning time. Similarly, maximum likelihood-based algorithms require large database for learning. This research proposes a novel histogram distance based multiple templates anomalies detection (MTAD) algorithm to segment surface defect. Histogram distance based on kernel-wise histograms stacked across illumination normalized database of similar size can describe the degree of anomaly intuitively across the image. Then, surface defect can be justified intuitively according to anomaly heat map generated. The algorithm is tested against industrial samples and it can handle texture and design variation existed in the product while catching anomaly in real time. This research suggests future studies on extending dimensionality of the histogram. Suggested algorithm has wide range of application other than surface defect detection. For examples, video motion detection, decolorization detection on industrial lighting.

10

Nazir, Sajid, Shushma Patel e Dilip Patel. "Autoencoder Based Anomaly Detection for SCADA Networks". International Journal of Artificial Intelligence and Machine Learning 11, n.º 2 (julho de 2021): 83–99. http://dx.doi.org/10.4018/ijaiml.20210701.oa6.

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Supervisory control and data acquisition (SCADA) systems are industrial control systems that are used to monitor critical infrastructures such as airports, transport, health, and public services of national importance. These are cyber physical systems, which are increasingly integrated with networks and internet of things devices. However, this results in a larger attack surface for cyber threats, making it important to identify and thwart cyber-attacks by detecting anomalous network traffic patterns. Compared to other techniques, as well as detecting known attack patterns, machine learning can also detect new and evolving threats. Autoencoders are a type of neural network that generates a compressed representation of its input data and through reconstruction loss of inputs can help identify anomalous data. This paper proposes the use of autoencoders for unsupervised anomaly-based intrusion detection using an appropriate differentiating threshold from the loss distribution and demonstrate improvements in results compared to other techniques for SCADA gas pipeline dataset.

Mais fontes

Teses / dissertações sobre o assunto "Surface anomaly detection":

1

Le, Jiahui. "Application of Deep-learning Method to Surface Anomaly Detection". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105240.

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In traditional industrial manufacturing, due to the limitations of science and technology, manual inspection methods are still used to detect product surface defects. This method is slow and inefficient due to manual limitations and backward technology. The aim of this thesis is to research whether it is possible to automate this using modern computer hardware and image classification of defects using different deep learning methods. The report concludes, based on results from controlled experiments, that it is possible to achieve a dice coefficient of more than 81%.

2

Tufekci, Sinan. "Combined Surface-Wave and Resistivity Imaging for Shallow Subsurface Characterization". Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1250891786.

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3

Benmoussat, Mohammed Seghir. "Hyperspectral imagery algorithms for the processing of multimodal data : application for metal surface inspection in an industrial context by means of multispectral imagery, infrared thermography and stripe projection techniques". Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4347/document.

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Le travail présenté dans cette thèse porte sur l'inspection de surfaces métalliques industrielles. Nous proposons de généraliser des méthodes de l'imagerie hyperspectrale à des données multimodales comme des images optiques multi-canales, et des images thermographiques multi-temporelles. Dans la première application, les cubes de données sont construits à partir d'images multi-composantes pour détecter des défauts de surface. Les meilleures performances sont obtenues avec les éclairages multi-longueurs d'ondes dans le visible et le proche IR, et la détection du défaut en utilisant l'angle spectral, avec le spectre moyen comme référence. La deuxième application concerne l'utilisation de l'imagerie thermique pour l'inspection de pièces métalliques nucléaires afin de détecter des défauts de surface et sub-surface. Une approche 1D est proposée, basée sur l'utilisation du kurtosis pour sélectionner la composante principale parmi les premières obtenues après réduction des données avec l’ACP. La méthode proposée donne de bonnes performances avec des données non-bruitées et homogènes, cependant la SVD avec les algorithmes de détection d'anomalies est très robuste aux perturbations. Finalement, une approche, basée sur les techniques d'analyse de franges et la lumière structurée est présentée, dans le but d'inspecter des surfaces métalliques à forme libre. Après avoir déterminé les paramètres décrivant les modèles de franges sinusoïdaux, l'approche proposée consiste à projeter une liste de motifs déphasés et à calculer l'image de phase des motifs enregistrés. La localisation des défauts est basée sur la détection et l'analyse des franges dans les images de phase
The work presented in this thesis deals with the quality control and inspection of industrial metallic surfaces. The purpose is the generalization and application of hyperspectral imagery methods for multimodal data such as multi-channel optical images and multi-temporal thermographic images. In the first application, data cubes are built from multi-component images to detect surface defects within flat metallic parts. The best performances are obtained with multi-wavelength illuminations in the visible and near infrared ranges, and detection using spectral angle mapper with mean spectrum as a reference. The second application turns on the use of thermography imaging for the inspection of nuclear metal components to detect surface and subsurface defects. A 1D approach is proposed based on using the kurtosis to select 1 principal component (PC) from the first PCs obtained after reducing the original data cube with the principal component analysis (PCA) algorithm. The proposed PCA-1PC method gives good performances with non-noisy and homogeneous data, and SVD with anomaly detection algorithms gives the most consistent results and is quite robust to perturbations such as inhomogeneous background. Finally, an approach based on fringe analysis and structured light techniques in case of deflectometric recordings is presented for the inspection of free-form metal surfaces. After determining the parameters describing the sinusoidal stripe patterns, the proposed approach consists in projecting a list of phase-shifted patterns and calculating the corresponding phase-images. Defect location is based on detecting and analyzing the stripes within the phase-images

4

Täuber, Daniela. "Characterization of heterogeneous diffusion in confined soft matter". Doctoral thesis, Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-77658.

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A new method, probability distribution of diffusivities (time scaled square displacements between succeeding video frames), was developed to analyze single molecule tracking (SMT) experiments. This method was then applied to SMT experiments on ultrathin liquid tetrakis(2-ethylhexoxy)silane (TEHOS) films on Si wafer with 100 nm thermally grown oxide, and on thin semectic liquid crystal films. Spatial maps of diffusivities from SMT experiments on 220 nm thick semectic liquid crystal films reveal structure related dynamics. The SMT experiments on ultrathin TEHOS films were complemented by fluorescence correlation spectroscopy (FCS). The observed strongly heterogeneous single molecule dynamics within those films can be explained by a three-layer model consisting of (i) dye molecules adsorbed to the substrate, (ii) slowly diffusing molecules in the laterally heterogeneous near-surface region of 1 - 2 molecular diameters, and (iii) freely diffusing dye molecules in the upper region of the film. FCS and SMT experiments reveal a strong influence of substrate heterogeneity on SM dynamics. Thereby chemisorption to substrate surface silanols plays an important role. Vertical mean first passage times (mfpt) in those films are below 1 µs. This appears as fast component in FCS autocorrelation curves, which further contain a contribution from lateral diffusion and from adsorption events. Therefore, the FCS curves are approximated by a tri-component function, which contains an exponential term related to the mfpt, the correlation function for translational diffusion and a stretched exponential term for the broad distribution of adsorption events. Lateral diffusion coefficients obtained by FCS on 10 nm thick TEHOS films, thereby, are effective diffusion coefficients from dye transients in the focal area. They strongly depend on the substrate heterogeneity. Variation of the frame times for the acquisition of SMT experiments in steps of 20 ms from 20 ms to 200 ms revealed a strong dependence of the corresponding probability distributions of diffusivities on time, in particular in the range between 20 ms and 100 ms. This points to average dwell times of the dye molecules in at least one type of the heterogeneous regions (e.g. on and above silanol clusters) in the range of few tens of milliseconds. Furthermore, time series of SM spectra from Nile Red in 25 nm thick poly-n-alkyl-methacrylate (PnAMA) films were studied. In analogy to translational diffusion, spectral diffusion (shifts in energetic positions of SM spectra) can be studied by probability distributions of spectral diffusivities, i.e. time scaled square energetic displacements. Simulations were run and analyzed to study contributions from noise and fitting uncertainty to spectral diffusion. Furthermore the effect of spectral jumps during acquisition of a SM spectrum was investigated. Probability distributions of spectral diffusivites of Nile Red probing vitreous PnAMA films reveal a two-level system. In contrast, such probability distributions obtained from Nile Red within a 25 nm thick poly-n-butylmethacrylate film around glass transition and in the melt state, display larger spectral jumps. Moreover, for longer alkyl side chains a solvent shift to higher energies is observed, which supports the idea of nanophase separation within those polymers.

5

Hsieh, Chao-Liang, e 謝兆糧. "An anomaly detection system for roadway surface monitoring based on IoT and machine learning technologies". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9nj8re.

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碩士
國立臺灣大學
生物產業機電工程學研究所
106
Roads connecting buildings, villages and even cities play a very important role in our life. The values derived from them are considerable, and they are undoubtedly one of the most important infrastructures in society. In Taiwan, the total length of the roads is 43,365 kilometers. The overall road network links Taiwan''s economy, trade, people, and transportation, reducing the spatial scale of Taiwan as a whole, and shortening the travel time to and from all places. If the road quality is not good, there are many potholes or roads that are sloping down the road on one road. This can cause problems such as uncomfortable rides, driving and passenger safety concerns, vehicle suspension system wear, and traffic accidents. Therefore, road quality and maintenance repairs are extremely important. At present, the maintenance of roads in Taiwan is mainly based on inspections of construction vehicles, returns from the public, and regular repairs. It takes a lot of manpower and time to find the correct road sections that need maintenance. In order to maintain road quality and improve the efficiency of government repairs, an anomaly detection system for roadway surface monitoring based on IoT and machine learning technologies is proposed in this study. The front-end sensing node of this system is equipped with a vibration sensor, a GPS module, and a 4G transmission module. When the vibration amplitude exceeds the set threshold, continuous measurement is performed for a period of time to record the vibration waveform, latitude and longitude, and vehicle speed at the time through 4G transmission module, back to the back-end database. In addition, the back-end computing system analyzes the waveforms of various road surface types (such as regular roads, potholes, manholes, and depressions) and uses machine learning methods to identify road surface types. And these classification results can be displayed on Google Map, and then provide reference for the public and government agencies. Government agencies can choose to repair road sections according to the severity of the road. As a result, the manpower and time costs which are required to examine the surface conditions of the roads can be greatly reduced, and the efficiency of road repairs can be improved.

6

Okyay, Ünal. "Evaluation of thermal remote sensing for detection of thermal anomalies as earthquake precursors: a case study for Malatya-Pütürge-Doganyol (Turkey) Earthquake, July 13, 2003". Master's thesis, 2012. http://hdl.handle.net/10362/8318.

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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
Several studies in last two decades indicated that presence of positive thermal anomalies associated with seismic activities can be detected by satellite thermal sensing methods. This study evaluates the potential of thermal remote sensing for detection of thermal anomalies prior to Malatya-Pütürge-Doğanyol (Turkey) earthquake using MODIS/Terra V5 LST/E (MOD11A1) data. In the previous studies, different methods based on different approaches have been suggested. In this particular study, four of the suggested methods were selected for evaluation as well as for comparison of different approaches. The analyses were carried out for fortnight before and after the earthquake. Depending on the method 4 to 7 years of daily daytime and nighttime MOD11A1 data were utilized. Furthermore, same set of analyses carried out for non-earthquake years as well as the earthquake year for the area. The results show that when only the earthquake year considered, all the methods used for the analyses detected the LST changes successfully and consistently not only before but also after the earthquake. However, thermal anomalies were not unique for the earthquake year and were also observed in the absence of seismic activity within defined time interval. Therefore, there exist no coherent evidence that indicates a direct link between the occurrence of seismic activity and the land surface temperature anomaly for Malatya-Pütürge-Doğanyol earthquake. Based on the information extracted, it can be said that, the reason for observing LST changes even in the absence of the seismic activity is the effect of environmental factors which have considerable influence on the methods and thus the detection of LST anomalies. Therefore, it can be said that since the effect of the Sun’s irradiation is minimal during night nighttime images would be more appropriate for thermal anomaly detection purpose. The findings support the argument that not every earthquake is preceded by detectable thermal precursor (Freund 2007; Saraf et al. 2009). On the other hand, not every LST anomaly is followed by an earthquake. Additionally, since the mechanism is not very well understood yet, it is not possible to identify earthquakes which would have thermal precursor prior to the incident. Therefore, it is concluded that utilizing LST anomalies based on satellite imagery for monitoring impending earthquake would not be adequate and feasible unless the mechanism of thermal precursors are very well understood.

7

"Advanced Processing of Multispectral Satellite Data for Detecting and Learning Knowledge-based Features of Planetary Surface Anomalies". Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.55700.

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abstract: The marked increase in the inflow of remotely sensed data from satellites have trans- formed the Earth and Space Sciences to a data rich domain creating a rich repository for domain experts to analyze. These observations shed light on a diverse array of disciplines ranging from monitoring Earth system components to planetary explo- ration by highlighting the expected trend and patterns in the data. However, the complexity of these patterns from local to global scales, coupled with the volume of this ever-growing repository necessitates advanced techniques to sequentially process the datasets to determine the underlying trends. Such techniques essentially model the observations to learn characteristic parameters of data-generating processes and highlight anomalous planetary surface observations to help domain scientists for making informed decisions. The primary challenge in defining such models arises due to the spatio-temporal variability of these processes. This dissertation introduces models of multispectral satellite observations that sequentially learn the expected trend from the data by extracting salient features of planetary surface observations. The main objectives are to learn the temporal variability for modeling dynamic processes and to build representations of features of interest that is learned over the lifespan of an instrument. The estimated model parameters are then exploited in detecting anomalies due to changes in land surface reflectance as well as novelties in planetary surface landforms. A model switching approach is proposed that allows the selection of the best matched representation given the observations that is designed to account for rate of time-variability in land surface. The estimated parameters are exploited to design a change detector, analyze the separability of change events, and form an expert-guided representation of planetary landforms for prioritizing the retrieval of scientifically relevant observations with both onboard and post-downlink applications.
Dissertation/Thesis
Doctoral Dissertation Computer Engineering 2019

Capítulos de livros sobre o assunto "Surface anomaly detection":

1

Pitard, Gilles, Gaëtan Le Goïc, Alamin Mansouri, Hugues Favrelière, Maurice Pillet, Sony George e Jon Yngve Hardeberg. "Robust Anomaly Detection Using Reflectance Transformation Imaging for Surface Quality Inspection". In Image Analysis, 550–61. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_46.

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2

Hung, Tzu-Yi, Sriram Vaikundam, Vidhya Natarajan e Liang-Tien Chia. "Phase Fourier Reconstruction for Anomaly Detection on Metal Surface Using Salient Irregularity". In MultiMedia Modeling, 290–302. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51811-4_24.

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Ogbechie, Alberto, Javier Díaz-Rozo, Pedro Larrañaga e Concha Bielza. "Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment". In Machine Learning for Cyber Physical Systems, 17–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-53806-7_3.

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An, Xueli, e Luoping Pan. "Vibration Adaptive Anomaly Detection of Hydropower Unit in Variable Condition Based on Moving Least Square Response Surface". In Lecture Notes in Computer Science, 146–54. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11897-0_17.

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Salem, Ahmed, Dhananjay Ravat, T. Jeffrey Gamey e Keisuke Ushijima. "16. Detection of Buried Steel Drums from Magnetic Anomaly Data Using an Artificial Intelligence Technique". In Near-Surface Geophysics, 513–24. Society of Exploration Geophysicists, 2005. http://dx.doi.org/10.1190/1.9781560801719.ch16.

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Wackernagel, Hans, e Henri Sanguinetti. "Gold Prospecting With Factorial Cokriging In The Limousin, France". In Computers in Geology - 25 Years of Progress. Oxford University Press, 1994. http://dx.doi.org/10.1093/oso/9780195085938.003.0008.

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In geochemical prospecting for gold a major difficulty is that many values are below the chemical detection limit. Tracers for gold thus play an important role in the evaluation of multivariate geochemical data. In this case study we apply geostatistical methods presented in Wackernagel (1988) to multielement exploration data from a prospect near Limoges, France. The analysis relies upon a metallogenetic model by Bonnemaison and Marcoux (1987, 1990) describing auriferous mineralization in shear zones of the Limousin. The aim of geochemical exploration is to find deposits of raw materials. What is a deposit? It is a geological anomaly which has a significant average content of a given raw material and enough spatial extension to have economic value. The geological body denned by an anomaly is generally buried at a specific depth and may be detectable at the surface through indices. These indices, which we shall call superficial anomalies, are disposed in three manners: at isolated locations, along faults, and as dispersion halos. These two definitions of the word "anomaly" correspond to a vision of the geological phenomenon in its full continuity. Yet in exploration geochemistry only a discrete perception of the phenomenon is possible through samples taken along a regularly meshed grid. A superficial anomaly thus can be apprehended by one or several samples or it can escape the grip of the geochemist when it is located between the nodes of the mesh. A geochemical anomaly, in the strict sense, only exists at the nodes of the sampling grid and we shall distinguish between: a pointwise anomaly defined on a single sample, and a groupwise anomaly defined on several neighboring samples. This distinction is important both upstream, for the geological interpretation of geochemical measurements, and downstream, at the level of geostatistical manipulation of the data. It will condition an exploration strategy on the basis of the data representations used in this case study. A pointwise anomaly, i.e., a high, isolated value of the material being sought, will correspond either to a geological phenomenon of limited extent or to a well hidden deposit.

Trabalhos de conferências sobre o assunto "Surface anomaly detection":

1

Schwenk*, J. Tyler, e Steven D. Sloan. "Anomaly detection using surface waves". In SEG Technical Program Expanded Abstracts 2015. Society of Exploration Geophysicists, 2015. http://dx.doi.org/10.1190/segam2015-5852010.1.

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2

Schwenk, J. Tyler, e Steven Sloan. "Surface-wave methods for anomaly detection: A review". In SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/segam2017-17793257.1.

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3

Chai, Woon Huei, Shen-Shyang Ho e Chi-Keong Goh. "Exploiting sparsity for image-based object surface anomaly detection". In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472024.

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4

Vaikundam, Sriram, Tzu-Yi Hung e Liang Tien Chia. "Anomaly region detection and localization in metal surface inspection". In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532459.

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5

Racki, Domen, Dejan Tomazevic e Danijel Skocaj. "A Compact Convolutional Neural Network for Textured Surface Anomaly Detection". In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. http://dx.doi.org/10.1109/wacv.2018.00150.

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6

Mori, Naoyuki, Noriko Takemura e Yasushi Yagi. "Pseudo normal image generation for anomaly detection on road surface". In Fifteenth International Conference on Quality Control by Artificial Vision, editado por Christophe Cudel, Stéphane Bazeille e Nicolas Verrier. SPIE, 2019. http://dx.doi.org/10.1117/12.2522245.

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7

Mesonero, Javier, Concha Bielza e Pedro Larranaga. "Architecture for anomaly detection in a laser heating surface process". In 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2017. http://dx.doi.org/10.1109/etfa.2017.8247777.

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8

Cooper, Eric G., Sharon M. Jones, Plesent W. Goode e Sixto L. Vazquez. "Automated anomaly detection for orbiter high-temperature reusable surface insulation". In Applications in Optical Science and Engineering, editado por Jon D. Erickson. SPIE, 1992. http://dx.doi.org/10.1117/12.131710.

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9

Li, Mingyang, Hanling Wang, Yue Zhang, Shao-Lun Huang e Lin Zhang. "Anomaly detection in surface mount technology process using multi-modal data". In SenSys '19: The 17th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356250.3361942.

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10

Sloan, Steven D. "Role of depth in anomaly detection using near-surface seismic methods". In International Conference on Engineering Geophysics, Al Ain, United Arab Emirates, 9-12 October 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/iceg2017-023.

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