Automatic Information Positioning Scheme in AR-assisted Maintenance Based on Visual Saliency

A method to measure pores and fissures in geologic materials under S.E.M. by digital image processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100108209 ◽

1978 ◽

Vol 36 (1) ◽

pp. 212-213

Author(s):

L. Montoto ◽

M. Montoto ◽

A. Bel-Lan

Keyword(s):

Image Processing ◽

Optical Microscopy ◽

Digital Image Processing ◽

Digital Image ◽

Digital Processing ◽

Free Surfaces ◽

Geologic Materials ◽

Automatic Information ◽

Detector Output ◽

Rock Specimens

INTRODUCTION.- The physical properties of rock masses are greatly influenced by their internal discontinuities, like pores and fissures. So, these need to be measured as a basis for interpretation. To avoid the basic difficulties of measurement under optical microscopy and analogic image systems, the authors use S.E.M. and multiband digital image processing. In S.E.M., analog signal processing has been used to further image enhancement (1), but automatic information extraction can be achieved by simple digital processing of S.E.M. images (2). The use of multiband image would overcome difficulties such as artifacts introduced by the relative positions of sample and detector or the typicals encountered in optical microscopy.DIGITAL IMAGE PROCESSING.- The studied rock specimens were in the form of flat deformation-free surfaces observed under a Phillips SEM model 500. The SEM detector output signal was recorded in picture form in b&w negatives and digitized using a Perkin Elmer 1010 MP flat microdensitometer.

Download Full-text

Automatic Information Processing and High Performance Skills: Acquisition, Transfer, and Retention

PsycEXTRA Dataset ◽

10.1037/e530292006-001 ◽

1990 ◽

Cited By ~ 1

Author(s):

Arthur D. Fisk ◽

Kevin A. Hodge ◽

Mark D. Lee ◽

Wendy A. Rogers

Keyword(s):

Information Processing ◽

High Performance ◽

Skills Acquisition ◽

Performance Skills ◽

Automatic Information Processing ◽

Automatic Information ◽

Transfer And Retention

Download Full-text

Visual Saliency Detection: An Information Theoretic Algorithm Combined Long-term with Short-term Features

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2012.01251 ◽

2014 ◽

Vol 35 (7) ◽

pp. 1636-1643

Author(s):

Xiao-liang Qian ◽

Lei Guo ◽

Jun-wei Han ◽

Xin-tao Hu ◽

Gong Cheng

Keyword(s):

Saliency Detection ◽

Visual Saliency ◽

Short Term ◽

Information Theoretic ◽

Visual Saliency Detection

Download Full-text

Guided Spatial Transformers for Facial Expression Recognition

Applied Sciences ◽

10.3390/app11167217 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7217

Author(s):

Cristina Luna-Jiménez ◽

Jorge Cristóbal-Martín ◽

Ricardo Kleinlein ◽

Manuel Gil-Martín ◽

José M. Moya ◽

...

Keyword(s):

Facial Expression ◽

Facial Expression Recognition ◽

Expert Knowledge ◽

Visual Saliency ◽

Expression Recognition ◽

Spatial Transformations ◽

Facial Landmarks ◽

Saliency Maps ◽

Best Fit

Spatial Transformer Networks are considered a powerful algorithm to learn the main areas of an image, but still, they could be more efficient by receiving images with embedded expert knowledge. This paper aims to improve the performance of conventional Spatial Transformers when applied to Facial Expression Recognition. Based on the Spatial Transformers’ capacity of spatial manipulation within networks, we propose different extensions to these models where effective attentional regions are captured employing facial landmarks or facial visual saliency maps. This specific attentional information is then hardcoded to guide the Spatial Transformers to learn the spatial transformations that best fit the proposed regions for better recognition results. For this study, we use two datasets: AffectNet and FER-2013. For AffectNet, we achieve a 0.35% point absolute improvement relative to the traditional Spatial Transformer, whereas for FER-2013, our solution gets an increase of 1.49% when models are fine-tuned with the Affectnet pre-trained weights.

Download Full-text