Machine learning: Assisted multivariate detection and visual image matching to build broad-specificity immunosensor

Visual image, as a kind of rich content and performance of multimedia information, has been tremendously popular for a long time. Using text-based Image Retrieval TBIR (Text-based Image Retrieval, TBIR) during retrieval will provide keywords and description of the image text matching, operation simple and quick. The defects of TBIR also, however, there are the following: (1) image library image all the need for manual annotation, time-consuming and laborious with subjective factors; (2) image semantics is rich, simple key words cannot fully express its meaning and accurate. Image Retrieval Based on regional RBIR (Region-based Image Retrieval, RBIR) first of all, by using image segmentation method, divides an image into several different regions. At last image matching is converted to match between the regions. We just need to user submits a retrieval image, greatly reducing the user's retrieval burden.

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Stereo visual image matching in remote welding

2011 International Conference on Electric Information and Control Engineering ◽

10.1109/iceice.2011.5778069 ◽

2011 ◽

Author(s):

Qiao Xi ◽

Jiao Xiang Dong

Keyword(s):

Image Matching ◽

Visual Image ◽

Remote Welding

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A Machine Learning Approach on Image Matching in the Modern Environment

Helix ◽

10.29042/2019-4808-4812 ◽

2019 ◽

Vol 9 (1) ◽

pp. 4808-4812

Author(s):

S. Vidya Sagar Appaji ◽

Lakshmi P.V

Keyword(s):

Machine Learning ◽

Image Matching ◽

Learning Approach ◽

Machine Learning Approach

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SUPERVISED CLASSIFICATION AND ITS REPEATABILITY FOR POINT CLOUDS FROM DENSE VHR TRI-STEREO SATELLITE IMAGE MATCHING USING MACHINE LEARNING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-525-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 525-532

Author(s):

A-M. Loghin ◽

N. Pfeifer ◽

J. Otepka-Schremmer

Keyword(s):

Machine Learning ◽

Satellite Imagery ◽

Image Matching ◽

Laser Scanning ◽

Satellite Image ◽

Point Clouds ◽

Supervised Machine Learning ◽

Ground Sample ◽

Machine Learning Classification ◽

3D Point Clouds

Abstract. Image matching of aerial or satellite images and Airborne Laser Scanning (ALS) are the two main techniques for the acquisition of geospatial information (3D point clouds), used for mapping and 3D modelling of large surface areas. While ALS point cloud classification is a widely investigated topic, there are fewer studies related to the image-derived point clouds, even less for point clouds derived from stereo satellite imagery. Therefore, the main focus of this contribution is a comparative analysis and evaluation of a supervised machine learning classification method that exploits the full 3D content of point clouds generated by dense image matching of tri-stereo Very High Resolution (VHR) satellite imagery. The images were collected with two different sensors (Pléiades and WorldView-3) at different timestamps for a study area covering a surface of 24 km2, located in Waldviertel, Lower Austria. In particular, we evaluate the performance and precision of the classifier by analysing the variation of the results obtained after multiple scenarios using different training and test data sets. The temporal difference of the two Pléiades acquisitions (7 days) allowed us to calculate the repeatability of the adopted machine learning algorithm for the classification. Additionally, we investigate how the different acquisition geometries (ground sample distance, viewing and convergence angles) influence the performance of classifying the satellite image-derived point clouds into five object classes: ground, trees, roads, buildings, and vehicles. Our experimental results indicate that, in overall the classifier performs very similar in all situations, with values for the F1-score between 0.63 and 0.65 and overall accuracies beyond 93%. As a measure of repeatability, stable classes such as buildings and roads show a variation below 3% for the F1-score between the two Pléiades acquisitions, proving the stability of the model.

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The Initial Retrieval Based on Image Segmentation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.201 ◽

2014 ◽

Vol 536-537 ◽

pp. 201-204

Author(s):

Qing Hua Zhan

Keyword(s):

Image Segmentation ◽

Image Retrieval ◽

Image Matching ◽

Visual Image ◽

Segmentation Method ◽

Image Library ◽

Long Time ◽

And Performance ◽

Rich Content ◽

Initial Retrieval

Visual image, as a kind of rich content and performance of multimedia information, has been tremendously popular for a long time. Using text-based Image Retrieval TBIR (Text-based Image Retrieval, TBIR) during retrieval will provide keywords and description of the image text matching, operation simple and quick. The defects of TBIR also, however, there are the following: (1) image library image all the need for manual annotation, time-consuming and laborious with subjective factors; (2) image semantics is rich, simple key words cannot fully express its meaning and accurate. Image Retrieval Based on regional RBIR (Region-based Image Retrieval, RBIR) first of all, by using image segmentation method, divides an image into several different regions. At last image matching is converted to match between the regions. We just need to user submits a retrieval image, greatly reducing the user's retrieval burden.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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Compositional Analysis of III-V Interface Lattice Images

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s1431927600001331 ◽

1980 ◽

Vol 38 ◽

pp. 318-319

Author(s):

A. Olsen ◽

J.C.H. Spence ◽

P. Petroff

Keyword(s):

Crystal Structure ◽

Image Matching ◽

Compositional Analysis ◽

Depth Of Field ◽

Limit Set ◽

High Contrast ◽

Resolution Limit ◽

Fourier Image ◽

Lattice Images ◽

True Structure

Since the point resolution of the JEOL 200CX electron microscope is up = 2.6Å it is not possible to obtain a true structure image of any of the III-V or elemental semiconductors with this machine. Since the information resolution limit set by electronic instability (1) u0 = (2/πλΔ)½ = 1.4Å for Δ = 50Å, it is however possible to obtain, by choice of focus and thickness, clear lattice images both resembling (see figure 2(b)), and not resembling, the true crystal structure (see (2) for an example of a Fourier image which is structurally incorrect). The crucial difficulty in using the information between Up and u0 is the fractional accuracy with which Af and Cs must be determined, and these accuracies Δff/4Δf = (2λu2Δf)-1 and ΔCS/CS = (λ3u4Cs)-1 (for a π/4 phase change, Δff the Fourier image period) are strongly dependent on spatial frequency u. Note that ΔCs(up)/Cs ≈ 10%, independent of CS and λ. Note also that the number n of identical high contrast spurious Fourier images within the depth of field Δz = (αu)-1 (α beam divergence) decreases with increasing high voltage, since n = 2Δz/Δff = θ/α = λu/α (θ the scattering angle). Thus image matching becomes easier in semiconductors at higher voltage because there are fewer high contrast identical images in any focal series.

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