3D SEM Surface Reconstruction: An Optimized, Adaptive, and Intelligent Approach

Structural analysis of microscopic objects is a longstanding topic in several scientific disciplines, including biological, mechanical, and material sciences. The scanning electron microscope (SEM), as a promising imaging equipment has been around to determine the surface properties (e.g., compositions or geometries) of specimens by achieving increased magnification, contrast, and resolution greater than one nanometer. Whereas SEM micrographs still remain two-dimensional (2D), many research and educational questions truly require knowledge and information about their three-dimensional (3D) surface structures. Having 3D surfaces from SEM images would provide true anatomic shapes of micro samples which would allow for quantitative measurements and informative visualization of the systems being investigated. In this research project, we novel design and develop an optimized, adaptive, and intelligent multi-view approach named 3DSEM++ for 3D surface reconstruction of SEM images, making a 3D SEM dataset publicly and freely available to the research community. The work is expected to stimulate more interest and draw attention from the computer vision and multimedia communities to the fast-growing SEM application area

A Global Constraint for a Tractable Class of Temporal Optimization Problems

International audienceThis paper is originally motivated by an application where the objective is to generate a video summary, built using intervals extracted from a video source. In this application, the constraints used to select the relevant pieces of intervals are based on Allen's algebra. The best state-of-the-art results are obtained with a small set of ad hoc solution techniques, each specific to one combination of the 13 Allen's relations. Such techniques require some expertise in Constraint Programming. This is a critical issue for video specialists. In this paper, we design a generic constraint, dedicated to a class of temporal problems that covers this case study, among others. ExistAllen takes as arguments a vector of tasks, a set of disjoint intervals and any of the 2 13 combinations of Allen's relations. ExistAllen holds if and only if the tasks are ordered according to their indexes and for any task at least one relation is satisfied , between the task and at least one interval. We design a propagator that achieves bound-consistency in O(n + m), where n is the number of tasks and m the number of intervals. This propagator is suited to any combination of Allen's relations, without any specific tuning. Therefore, using our framework does not require a strong expertise in Constraint Programming. The experiments, performed on real data, confirm the relevance of our approach

Probabilistic correspondence analysis for neuroimaging problems

Establecer correspondencias de forma significativas entre los objetivos como en los problemas de neuroimagen es crucial para mejorar los procesos de correspondencia. Por ejemplo, el problema de correspondencia consiste en encontrar relaciones significativas entre cualquier par de estructuras cerebrales como en el problema de registro estático, o analizar cambios temporales de una enfermedad neurodegenerativa dada a través del tiempo para un análisis dinámico de la forma del cerebro..

Air Force Institute of Technology Research Report 2017

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)

Probabilistic correspondence analysis for neuroimaging problems

Establecer correspondencias de forma significativas entre los objetivos como en los problemas de neuroimagen es crucial para mejorar los procesos de correspondencia. Por ejemplo, el problema de correspondencia consiste en encontrar relaciones significativas entre cualquier par de estructuras cerebrales como en el problema de registro estático, o analizar cambios temporales de una enfermedad neurodegenerativa dada a través del tiempo para un análisis dinámico de la forma del cerebro..

Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture

Deep neural networks are applied to a wide range of problems in recent years. In this work, Convolutional Neural Network (CNN) is applied to the problem of determining the depth from a single camera image (monocular depth). Eight different networks are designed to perform depth estimation, each of them suitable for a feature level. Networks with different pooling sizes determine different feature levels. After designing a set of networks, these models may be combined into a single network topology using graph optimization techniques. This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common network layers, and can be further optimized by retraining to achieve an improved model compared to the individual topologies. In this study, four SPDNN models are trained and have been evaluated at 2 stages on the KITTI dataset. The ground truth images in the first part of the experiment are provided by the benchmark, and for the second part, the ground truth images are the depth map results from applying a state-of-the-art stereo matching method. The results of this evaluation demonstrate that using post-processing techniques to refine the target of the network increases the accuracy of depth estimation on individual mono images. The second evaluation shows that using segmentation data alongside the original data as the input can improve the depth estimation results to a point where performance is comparable with stereo depth estimation. The computational time is also discussed in this study.Comment: 44 pages, 25 figure

On the Robustness of Explanations of Deep Neural Network Models: A Survey

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.Comment: Under Review ACM Computing Surveys "Special Issue on Trustworthy AI

Object Tracking in Video Sequences

Tässä diplomityössä vertaillaan konenäössä käytettyjen SIFT-, SURF- ja ORB-algoritmia objektin seurannassa. Tutkimuksen tavoitteena on tarkastella algoritmien soveltuvuutta erityyppisille videoille ja erilaisiin käyttötarkoituksiin, kuten reaaliaikaisiin järjestelmiin, mutta myös järjestelmiin, joissa reaaliaikaisuus ei ole vaatimuksena. Algoritmeja on aikaisemmissa tutkimuksissa vertailtu kuvaparien avulla, mutta tutkimuksia objektin seurannasta SIFT-, SURF- ja ORB-algoritmeja käyttäen ei löytynyt. SIFT- ja SURF-algoritmien vertailu niitä uudemman ORB-algormitmin kanssa tuo lisäksi uutta tietoa sen suorituskyvystä. Tutkimukset ovat olleet ORB:n osalta vielä vähäisiä. Vertailu tehdään neljän eri testivideon avulla algoritmien vakioparametreilla ja optimoiduilla parametreilla. Vertailussa otetaan huomioon algoritmien tarkkuus, nopeus, sekä sietokyky skaalaus-, rotaatio- ja kuvakulmamuutoksille. Testiympäristössä käytettiin Python-ohjelmointikieltä ja konenäköön suunnattua OpenCV-kirjastoa. Tuloksista selviää, että kaikki kolme algoritmia soveltuvat objektin seuraamiseen. Algoritmin valinta kuitenkin riippuu käyttökohteesta ja videon ominaisuuksista. Erityisesti ORB:n kohdalla tarkkuus parani merkittävästi optimoiduilla parametreilla. SIFT:n ja SURF:n tarkkuutta ei optimoinnilla juurikaan saatu parannettua, mutta niiden laskenta-aika lyheni. Algoritmeistä ORB oli jokaisessa videossa nopein ja SIFT keskiarvollisesti tarkin. Laskenta-ajallisesti SURF oli algoritmeista hitain, mikä voi rajottaa sen käyttöä. Tulosten perusteella ORB:n käyttöä voidaan suositella käytettäväksi reaaliaikaisissa järjestelmissä optimoiduilla parametreilla ja SIFT:n käyttöä puolestaan tarkempaan seurantaan. SURF:n tarkkuus oli paras tapauksissa, joissa videokuva oli heilahtanut, joten sen käyttöä voidaan suositella kyseisissä tilanteissa

OptiqueVQS: A visual query system over ontologies for industry

An important application of semantic technologies in industry has been the formalisation of information models using OWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised as RDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important to provide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. In this work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil and Siemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of unique techniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniques to enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontology driven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built on well-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts and casual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation and exploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario