273,881 research outputs found
Shape and deformation measurement using heterodyne range imaging technology
Range imaging is emerging as a promising alternative technology for applications that require non-contact visual inspection of object deformation and shape. Previously, we presented a solid-state full-field heterodyne range imaging device capable of capturing three-dimensional images with sub-millimetre range resolution. Using a heterodyne indirect time-of-flight configuration, this system simultaneously measures distance (and intensity), for each pixel in a cameras field of view. In this paper we briefly describe our range imaging system, and its principle of operation. By performing measurements on several metal objects, we demonstrate the potential capabilities of this technology for surface profiling and deformation measurement. In addition to verifying system performance, the reported examples highlight some important system limitations. With these in mind we subsequently discuss the further developments required to enable the use of this device as a robust and practical tool in non-destructive testing and measurement applications
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
In this paper we develop a Quality Assessment approach for face recognition
based on deep learning. The method consists of a Convolutional Neural Network,
FaceQnet, that is used to predict the suitability of a specific input image for
face recognition purposes. The training of FaceQnet is done using the VGGFace2
database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images
with quality information related to their ICAO compliance level. The
groundtruth quality labels are obtained using FaceNet to generate comparison
scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making
it capable of returning a numerical quality measure for each input image.
Finally, we verify if the FaceQnet scores are suitable to predict the expected
performance when employing a specific image for face recognition with a COTS
face recognition system. Several conclusions can be drawn from this work, most
notably: 1) we managed to employ an existing ICAO compliance framework and a
pretrained CNN to automatically label data with quality information, 2) we
trained FaceQnet for quality estimation by fine-tuning a pre-trained face
recognition network (ResNet-50), and 3) we have shown that the predictions from
FaceQnet are highly correlated with the face recognition accuracy of a
state-of-the-art commercial system not used during development. FaceQnet is
publicly available in GitHub.Comment: Preprint version of a paper accepted at ICB 201
A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines
The present work is intended for residual oxide scale detection and classification through the application of image processing
techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a
production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of
algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear
regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was
installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be
compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Development of a Computer Vision-Based Three-Dimensional Reconstruction Method for Volume-Change Measurement of Unsaturated Soils during Triaxial Testing
Problems associated with unsaturated soils are ubiquitous in the U.S., where expansive and collapsible soils are some of the most widely distributed and costly geologic hazards. Solving these widespread geohazards requires a fundamental understanding of the constitutive behavior of unsaturated soils. In the past six decades, the suction-controlled triaxial test has been established as a standard approach to characterizing constitutive behavior for unsaturated soils. However, this type of test requires costly test equipment and time-consuming testing processes. To overcome these limitations, a photogrammetry-based method has been developed recently to measure the global and localized volume-changes of unsaturated soils during triaxial test. However, this method relies on software to detect coded targets, which often requires tedious manual correction of incorrectly coded target detection information. To address the limitation of the photogrammetry-based method, this study developed a photogrammetric computer vision-based approach for automatic target recognition and 3D reconstruction for volume-changes measurement of unsaturated soils in triaxial tests. Deep learning method was used to improve the accuracy and efficiency of coded target recognition. A photogrammetric computer vision method and ray tracing technique were then developed and validated to reconstruct the three-dimensional models of soil specimen
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