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Santos-Villalobos, Hector J

Publications and source records attributed to Santos-Villalobos, Hector J.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Progress Implementing a Model-Based Iterative Reconstruction Algorithm for Ultrasound Imaging of Thick Concrete

All commercial nuclear power plants (NPPs) in the United States contain concrete structures. These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and the degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Concrete structures in NPPs are often inaccessible and contain large volumes of massively thick concrete. While acoustic imaging using the synthetic aperture focusing technique (SAFT) works adequately well for thin specimens of concrete such as concrete transportation structures, enhancements are needed for heavily reinforced, thick concrete. We argue that image reconstruction quality for acoustic imaging in thick concrete could be improved with Model-Based Iterative Reconstruction (MBIR) techniques. MBIR works by designing a probabilistic model for the measurements (forward model) and a probabilistic model for the object (prior model). Both models are used to formulate an objective function (cost function). The final step in MBIR is to optimize the cost function. Previously, we have demonstrated a first implementation of MBIR for an ultrasonic transducer array system. The original forward model has been upgraded to account for direct arrival signal. Updates to the forward model will be documented and the new algorithm will be assessed with synthetic and empirical samples.

concrete NDE↗
DOE OSTI2017

An HDF5-Based Framework for the Distribution and Analysis of Ultrasonic Concrete Data

There are many commercial ultrasonic tomography devices (UTDs) available for use in nondestructive evaluation (NDE) of reinforced concrete structures. These devices emit, measure, and store ultrasonic signals typically in the 25 kHz to 5 MHz frequency range. UTDs are characterized by a composition of multiple transducers, also known as a transducer array or phased array. Often, UTDs data are in a proprietary format. Consequently, NDE research data is limited to those who have prior non-disclosure agreements or the appropriate licenses. Thus, there is a need for a proper universal data framework to exist such that proprietary file datasets for different concrete specimens can be converted, organized, and stored with relative metadata for individual or collaborative NDE research. Building upon the Hierarchical Data Format (HDF5) model, we have developed a UTD data management framework and Graphic User Interface (GUI) to promote the algorithmic reconstruction of ultrasonic data in a controlled environment for easily reproducible and publishable results.

concrete NDE↗
DOE OSTI2017

Automated Detection of Alkali-silica Reaction in Concrete using Linear Array Ultrasound Data

Alkali-silica reaction (ASR) is a chemical reaction in either concrete or mortar between hydroxyl ions of the alkalis (sodium and potassium) from hydraulic cement (or other sources), and certain siliceous minerals present in some aggregates. The reaction product, an alkali-silica gel, is hygroscopic having a tendency to absorb water and swell, which under certain circumstances, leads to abnormal expansion and cracking of the concrete. This phenomenon affects the durability and performance of concrete structures severely since it can cause significant loss of mechanical properties. Developing reliable methods and tools that can evaluate the degree of the ASR damage in existing structures, so that informed decisions can be made toward mitigating ASR progression and damage, is important to the long term operation of nuclear power plants especially if licenses are extended beyond 60 years. This paper examines an automated method of determining the extent of ASR damage in fabricated concrete specimens.

Santos-Villalobos, Hector J↗
DOE OSTI2016

Off-Angle Iris Correction Methods

In many real world iris recognition systems obtaining consistent frontal images is problematic do to inexperienced or uncooperative users, untrained operators, or distracting environments. As a result many collected images are unusable by modern iris matchers. In this chapter we present four methods for correcting off-angle iris images to appear frontal which makes them compatible with existing iris matchers. The methods include an affine correction, a retraced model of the human eye, measured displacements, and a genetic algorithm optimized correction. The affine correction represents a simple way to create an iris image that appears frontal but it does not account for refractive distortions of the cornea. The other method account for refraction. The retraced model simulates the optical properties of the cornea. The other two methods are data driven. The first uses optical flow to measure the displacements of the iris texture when compared to frontal images of the same subject. The second uses a genetic algorithm to learn a mapping that optimizes the Hamming Distance scores between off-angle and frontal images. In this paper we hypothesize that the biological model presented in our earlier work does not adequately account for all variations in eye anatomy and therefore the two data-driven approaches should yield better performance. Results are presented using the commercial VeriEye matcher that show that the genetic algorithm method clearly improves over prior work and makes iris recognition possible up to 50 degrees off-angle.

iris recognition↗
DOE OSTI2015

Frontal view reconstruction for iris recognition

Iris recognition can be accomplished for a wide variety of eye images by correcting input images with an off-angle gaze. A variety of techniques, from limbus modeling, corneal refraction modeling, optical flows, and genetic algorithms can be used. A variety of techniques, including aspherical eye modeling, corneal refraction modeling, ray tracing, and the like can be employed. Precomputed transforms can enhance performance for use in commercial applications. With application of the technologies, images with significantly unfavorable gaze angles can be successfully recognized.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well s health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.

Almansouri, Hani↗
DOE OSTI2014

Reflective echo tomographic imaging using acoustic beams

An inspection system includes a plurality of acoustic beamformers, where each of the plurality of acoustic beamformers including a plurality of acoustic transmitter elements. The system also includes at least one controller configured for causing each of the plurality of acoustic beamformers to generate an acoustic beam directed to a point in a volume of interest during a first time. Based on a reflected wave intensity detected at a plurality of acoustic receiver elements, an image of the volume of interest can be generated.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Model-Based Least Squares Reconstruction of Coded Source Neutron Radiographs: Integrating the ORNL HFIR CG1D Source Model

At the present, neutron sources cannot be fabricated small and powerful enough in order to achieve high resolution radiography while maintaining an adequate flux. One solution is to employ computational imaging techniques such as a Magnified Coded Source Imaging (CSI) system. A coded-mask is placed between the neutron source and the object. The system resolution is increased by reducing the size of the mask holes and the flux is increased by increasing the size of the coded-mask and/or the number of holes. One limitation of such system is that the resolution of current state-of-the-art scintillator-based detectors caps around 50um. To overcome this challenge, the coded-mask and object are magnified by making the distance from the coded-mask to the object much smaller than the distance from object to detector. In previous work, we have shown via synthetic experiments that our least squares method outperforms other methods in image quality and reconstruction precision because of the modeling of the CSI system components. However, the validation experiments were limited to simplistic neutron sources. In this work, we aim to model the flux distribution of a real neutron source and incorporate such a model in our least squares computational system. We provide a full description of the methodology used to characterize the neutron source and validate the method with synthetic experiments.

Coded Apertures↗
DOE OSTI2013

Iterative Reconstruction of Coded Source Neutron Radiographs

Use of a coded source facilitates high-resolution neutron imaging through magnifications but requires that the radiographic data be deconvolved. A comparison of direct deconvolution with two different iterative algorithms has been performed. One iterative algorithm is based on a maximum likelihood estimation (MLE)-like framework and the second is based on a geometric model of the neutron beam within a least squares formulation of the inverse imaging problem. Simulated data for both uniform and Gaussian shaped source distributions was used for testing to understand the impact of non-uniformities present in neutron beam distributions on the reconstructed images. Results indicate that the model based reconstruction method will match resolution and improve on contrast over convolution methods in the presence of non-uniform sources. Additionally, the model based iterative algorithm provides direct calculation of quantitative transmission values while the convolution based methods must be normalized base on known values.

Santos-Villalobos, Hector J↗
DOE OSTI2013

Neutron imaging with coded sources: new challenges and the implementation of a simultaneous iterative reconstruction technique

The limitations in neutron flux and resolution (L/D) of current neutron imaging systems can be addressed with a Coded Source Imaging system with magnification (xCSI). More precisely, the multiple sources in an xCSI system can exceed the flux of a single pinhole system for several orders of magnitude, while maintaining a higher L/D with the small sources. Moreover, designing for an xCSI system reduces noise from neutron scattering, because the object is placed away from the detector to achieve magnification. However, xCSI systems are adversely affected by correlated noise such as non-uniform illumination of the neutron source, incorrect sampling of the coded radiograph, misalignment of the coded masks, mask transparency, and the imperfection of the system Point Spread Function (PSF). We argue that a model-based reconstruction algorithm can overcome these problems and describe the implementation of a Simultaneous Iterative Reconstruction Technique algorithm for coded sources. Design pitfalls that preclude a satisfactory reconstruction are documented.

Santos-Villalobos, Hector J↗
DOE OSTI2013

An Iris Segmentation Algorithm based on Edge Orientation for Off-angle Iris Recognition

Iris recognition is known as one of the most accurate and reliable biometrics. However, the accuracy of iris recognition systems depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. In this paper, we present a segmentation algorithm for off-angle iris images that uses edge detection, edge elimination, edge classification, and ellipse fitting techniques. In our approach, we first detect all candidate edges in the iris image by using the canny edge detector; this collection contains edges from the iris and pupil boundaries as well as eyelash, eyelids, iris texture etc. Edge orientation is used to eliminate the edges that cannot be part of the iris or pupil. Then, we classify the remaining edge points into two sets as pupil edges and iris edges. Finally, we randomly generate subsets of iris and pupil edge points, fit ellipses for each subset, select ellipses with similar parameters, and average to form the resultant ellipses. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.

Karakaya, Mahmut↗
DOE OSTI2013

Gaze Estimation for Off-Angle Iris Recognition Based on the Biometric Eye Model

Iris recognition is among the highest accuracy biometrics. However, its accuracy relies on controlled high quality capture data and is negatively affected by several factors such as angle, occlusion, and dilation. Non-ideal iris recognition is a new research focus in biometrics. In this paper, we present a gaze estimation method designed for use in an off-angle iris recognition framework based on the ANONYMIZED biometric eye model. Gaze estimation is an important prerequisite step to correct an off-angle iris images. To achieve the accurate frontal reconstruction of an off-angle iris image, we first need to estimate the eye gaze direction from elliptical features of an iris image. Typically additional information such as well-controlled light sources, head mounted equipment, and multiple cameras are not available. Our approach utilizes only the iris and pupil boundary segmentation allowing it to be applicable to all iris capture hardware. We compare the boundaries with a look-up-table generated by using our biologically inspired biometric eye model and find the closest feature point in the look-up-table to estimate the gaze. Based on the results from real images, the proposed method shows effectiveness in gaze estimation accuracy for our biometric eye model with an average error of approximately 3.5 degrees over a 50 degree range.

Karakaya, Mahmut↗
DOE OSTI2013

Limbus Impact on Off-angle Iris Degradation

The accuracy of iris recognition depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. Off-angle iris recognition is a new research focus in biometrics that tries to address several issues including corneal refraction, complex 3D iris texture, and blur. In this paper, we present an additional significant challenge that degrades the performance of the off-angle iris recognition systems, called the limbus effect . The limbus is the region at the border of the cornea where the cornea joins the sclera. The limbus is a semitransparent tissue that occludes a side portion of the iris plane. The amount of occluded iris texture on the side nearest the camera increases as the image acquisition angle increases. Without considering the role of the limbus effect, it is difficult to design an accurate off-angle iris recognition system. To the best of our knowledge, this is the first work that investigates the limbus effect in detail from a biometrics perspective. Based on results from real images and simulated experiments with real iris texture, the limbus effect increases the hamming distance score between frontal and off-angle iris images ranging from 0.05 to 0.2 depending upon the limbus height.

Karakaya, Mahmut↗
DOE OSTI2013

Effects of Iris Surface Curvature on Iris Recognition

To focus on objects at various distances, the lens of the eye must change shape to adjust its refractive power. This change in lens shape causes a change in the shape of the iris surface which can be measured by examining the curvature of the iris. This work isolates the variable of iris curvature in the recognition process and shows that differences in iris curvature degrade matching ability. To our knowledge, no other work has examined the effects of varying iris curvature on matching ability. To examine this degradation, we conduct a matching experiment across pairs of images with varying degrees of iris curvature differences. The results show a statistically signi cant degradation in matching ability. Finally, the real world impact of these ndings is discussed

Thompson, Joseph T↗