Determination of Maximum Accuracy of Concrete Textures as Natural Targets for Movement Tracking Through DIC

The use of natural targets is one of the obstacles to the extensive use of digital image cross-correlation for measuring movements in civil structures. Long distance measurement through image and without access to the structure itself, brings results in an improvement in accessibility, being the procedure cheaper and safer than common methods that require direct access to the measuring point. One of the most used materials in construction is concrete. Therefore, it is important to analyze its performance when using image cross-correlation. In this work, we have made a series of concrete probes with different production characteristics to have a representative variety of concrete surfaces. With them, we have studied their floor error in a cross-correlation procedure using different illumination and blur conditions, to evaluate the influence of those parameters on the results. All results are compared to those obtained using the conventional texture for image cross-correlation techniques, that is a pseudo-speckle target. All experiments are done in laboratory conditions to control all variables involved and to avoid the influence of other variables linked to open air conditions, such as atmospheric disturbances. As a result, we have determined the best conditions to use the natural concrete texture and we have quantified that using this texture leads to a decrease in the accuracy of the results from two to three times the one obtained with a typical pseudo-speckle texture.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was done with the financial support of the Spanish Ministry of Science and Innovation through the project PID2021-126485OB-I00 in which all authors are involved

A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide cytology images. The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification. Our novel focus selection step provides fast per-cell focus decisions at human-level accuracy. We demonstrate that the pipeline provides efficient cancer classification of whole slide cytology images, improving over previous results both in terms of accuracy and feasibility. The complete source code is available at https://github.com/MIDA-group/OralScreen.Comment: Accepted to ICIAR 202

Depth Acquisition from Digital Images

Introduction: Depth acquisition from digital images captured with a conventional camera, by analysing focus/defocus cues which are related to depth via an optical model of the camera, is a popular approach to depth-mapping a 3D scene. The majority of methods analyse the neighbourhood of a point in an image to infer its depth, which has disadvantages. A more elegant, but more difficult, solution is to evaluate only the single pixel displaying a point in order to infer its depth. This thesis investigates if a per-pixel method can be implemented without compromising accuracy and generality compared to window-based methods, whilst minimising the number of input images. Method: A geometric optical model of the camera was used to predict the relationship between focus/defocus and intensity at a pixel. Using input images with different focus settings, the relationship was used to identify the focal plane depth (i.e. focus setting) where a point is in best focus, from which the depth of the point can be resolved if camera parameters are known. Two metrics were implemented, one to identify the best focus setting for a point from the discrete input set, and one to fit a model to the input data to estimate the depth of perfect focus of the point on a continuous scale. Results: The method gave generally accurate results for a simple synthetic test scene, with a relatively low number of input images compared to similar methods. When tested on a more complex scene, the method achieved its objectives of separating complex objects from the background by depth, and produced a similar resolution of a complex 3D surface as a similar method which used significantly more input data. Conclusions: The method demonstrates that it is possible to resolve depth on a per-pixel basis without compromising accuracy and generality, and using a similar amount of input data, compared to more traditional window-based methods. In practice, the presented method offers a convenient new option for depth-based image processing applications, as the depth-map is per-pixel, but the process of capturing and preparing images for the method is not too practically cumbersome and could be easily automated unlike other per-pixel methods reviewed. However, the method still suffers from the general limitations of the depth acquisition approach using images from a conventional camera, which limits its use as a general depth acquisition solution beyond specifically depth-based image processing applications

Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation

Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, is a quantitative approach to defining information. AIT is mainly used to measure the amount of information present in the observations of a given phenomenon. In this dissertation we explore the applications of AIT in two case studies. The first examines bright field cell image segmentation and the second examines the information complexity of multicellular patterns. In the first study we demonstrate that our proposed AIT-based algorithm provides an accurate and robust bright field cell segmentation. Cell segmentation is the process of detecting cells in microscopy images, which is usually a challenging task for bright field microscopy due to the low contrast of the images. In the second study, which is the primary contribution of this dissertation, we employ an AIT-based algorithm to quantify the complexity of information content that arises during the development of multicellular organisms. We simulate multicellular organism development by coupling the Gene Regulatory Networks (GRN) within an epithelial field. Our results show that the configuration of GRNs influences the information complexity in the resultant multicellular patterns

Visual and Camera Sensors

This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors

Edge Detection and 3D Reconstruction Based on the Shape-from-Focus

Má práce vychází z průmyslového projektu, jehož cílem je postavit stroj pro přesnou manipulaci mikrokomponenty. Zmíněné mikrokomponenty jsou sledovány na základě hledání hran v obraze. Má práce popisuje přehled postupů používaných pro detekci hran v obraze a zároveň návrh algoritmu pro rekonstrukci povrchu mikrokomponent pomocí Shape-From-Focus v mikroskopickém prostředí. Použité obrázky byly pořízeny kamerou s telecentrickým objektivem s malou hloubkou ostrosti. Vyvinul jsem Shape-From-Focus algoritmus, který používá 3D konvoluční masku pro detekci hran a je schopný aproximovat povrchy bez struktury. Vyvinutá 3D konvoluční maska je založena na druhé derivaci obrazové funkce. V pokusech popisujících kalibraci kamery a pro opětovné zaostření optické soustavy byly použity rozličné metody pro detekci hran v obraze. V pokusech se také prezentují výsledky rekonstrukce povrchu pomocí navrženého Shape-From-Focus algoritmu.The work stems from the industrial project which aims to build the highly precise micro components assembly machine. The components are positioned via locating the edges in the image. The overview of the edge detection techniques and the design of the Shape-From-Focus algorithm in the microscopic environment are presented. The images used were captured with telecentric optics with a shallow Depth-of-Field. The Shape-From-Focus algorithm is developed together with the 3D convolutional mask and approximation of the surface in the textureless areas. The developed 3D convolutional filter is based on the seconds derivative of the image function. Various edge detection techniques are used in experiments to calibrate the camera and to refocus the optics. The experiments also show the surface reconstruction obtained by the Shape-From-Focus algorithm

Single Molecule and Nanoparticle Imaging in Biophysical, Surface, and Photocatalysis Studies

Single molecule and nanoparticle imaging has become a very important tool to investigate many biological and chemical processes. This dissertation presents the applications of single molecule and nanoparticle imaging in biophysical, surface, and photocatalysis studies using far-field optical microscopy. The work was mainly carried out under a differential interference contrast (DIC) microscope and a total internal reflection (TIR) microscope. First, a DIC microscopy-based single particle orientation and rotational tracking technique that allows for resolving the full three-dimensional (3D) orientation of single gold nanorod (AuNR) probes has been developed. The angular degeneracy was overcome by combining DIC polarization anisotropy with the image pattern recognition technique. The usefulness of this technique in biophysical studies was further verified by real time tracking of rotational motions of single AuNRs rotating on live cell membranes. Therefore, it is expected that this method will enable us to elucidate the comprehensive interaction mechanisms between the functionalized nanocargoes and the membrane receptors in live cells. Detailed in situ conformational information on how they bind on the cell membrane and how they move and rotate in live cells at single particle level would provide new avenues for the development of new generation of high efficient drug and gene delivery carriers. Second, a high-throughput focused orientation and position imaging (FOPI) technique with 3D orientation resolvability for single AuNRs deposited on a gold film has been developed for surface studies. The FOPI method presented in this dissertation provides a new approach using the interaction of AuNRs with their surrounding environment for resolving the 3D orientation of single AuNRs. Therefore, it is expectedthat this method can be used as a tool to study interactions of functionalized nanoparticles with functional gold surfaces. Last, single molecule TIRF imaging was successfully employed in photocatalysis study to reveal the nature and photocatalytic properties of the surface active sites on single Au-CdS hybrid nanocatalysts. Single-molecule photocatalysis with high-resolution super-localization imaging allowed us to reveal two distinct, incident energy-dependent charge separation mechanisms in single Au-CdS heterostructures. This finding will help us design and develop better metal-semiconductor heterostructures that are highly active for photocatalytic reactions under visible light. Furthermore, the finding will enable us to potentially engineer the direction of energy flows on the heterostructured nanomaterials at the nanoscale. Therefore, it is expected that the results presented in this dissertation have a potential impact on the development of better photocatalyst structures