USE OF IMAGE PROCESSING TECHNIQUES AND MACHINE LEARNING FOR BETTER UNDERSTANDING OF T GONDII BIOLOGY

Almost one in every three people worldwide is infected with Toxoplasma gondii (T. gondii). The biology and growth of the parasite’s bradyzoite form in host tissue cysts are not well understood. T. gondii’s metabolic state influences the morphology of its single mitochondrion, which can be visualized using fluorescence microscopy with specific dyes. Hence, fluorescence microscopy images of cysts purified from infected mouse brains carry biological information about bradyzoites, the poorly understood form of the parasite within them. With the help of fluorescence microscopy techniques, previous studies extracted images of the mitochondrion, nucleus, and the inner membrane complex (IMC) providing information on T. gondii’s cysts paving the way for image processing techniques and machine learning to analyze the bradyzoite form of the parasite. Previously, multivariate logistic regression (MLG) was used to classify shapes of mitochondrion. In the present study, in addition to the previously used MLG model, two other machine learning models, Support Vector Machine (SVM) and K Nearest Neighbors (KNN), were used to explore the possibility of better model selection for mitochondrial classification. A minimal model error was used to optimize the classification model performance. Error in any machine learning model is driven by bias, variance, and noise. Through trial and error, the optimal hyperparameters for each model were selected to minimize error. The dataset used consisted of 1940 labeled mitochondrial objects with 22 features, and consisted of five classes Blob, Tadpole, Donut, Arc, and Other. 50% of the dataset was used for training, and the other 50% was used for testing. The overall models’ accuracy of MLG, SVM, and KNN were 79.1%, 78.9%, and 80.3% respectively. Overall classification performance did not vary, but the F score for some classes like Tadpole and Donut showed improvement when using the two newer models. One of the 22 features used was an application of the Histogram of Oriented Gradients (HOG). The HOG feature was replaced with a novel feature that used linear regression of object boundary segments to extract the HOG for only the object’s boundary. The model that used Boundary HOG showed some improvement over the HOG feature. Finally, a new module including a graphical user interface was developed to process and extract shape and intensity information from TgIMC3 images which facilitate further investigations of the parasite biology

Invariant Representative Cocycles of Cohomology Generators using Irregular Graph Pyramids

Structural pattern recognition describes and classifies data based on the relationships of features and parts. Topological invariants, like the Euler number, characterize the structure of objects of any dimension. Cohomology can provide more refined algebraic invariants to a topological space than does homology. It assigns `quantities' to the chains used in homology to characterize holes of any dimension. Graph pyramids can be used to describe subdivisions of the same object at multiple levels of detail. This paper presents cohomology in the context of structural pattern recognition and introduces an algorithm to efficiently compute representative cocycles (the basic elements of cohomology) in 2D using a graph pyramid. An extension to obtain scanning and rotation invariant cocycles is given.Comment: Special issue on Graph-Based Representations in Computer Visio

1999 Flight Mechanics Symposium

This conference publication includes papers and abstracts presented at the Flight Mechanics Symposium held on May 18-20, 1999. Sponsored by the Guidance, Navigation and Control Center of Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to orbit-attitude prediction, determination, and control; attitude sensor calibration; attitude determination error analysis; attitude dynamics; and orbit decay and maneuver strategy. Government, industry, and the academic community participated in the preparation and presentation of these papers

Deep Retinal Optical Flow: From Synthetic Dataset Generation to Framework Creation and Evaluation

Sustained delivery of regenerative retinal therapies by robotic systems requires intra-operative tracking of the retinal fundus. This thesis presents a supervised convolutional neural network to densely predict optical flow of the retinal fundus, using semantic segmentation as an auxiliary task. Retinal flow information missing due to occlusion by surgical tools or other effects is implicitly inpainted, allowing for the robust tracking of surgical targets. As manual annotation of optical flow is infeasible, a flexible algorithm for the generation of large synthetic training datasets on the basis of given intra-operative retinal images and tool templates is developed. The compositing of synthetic images is approached as a layer-wise operation implementing a number of transforms at every level which can be extended as required, mimicking the various phenomena visible in real data. Optical flow ground truth is calculated from motion transforms with the help of oflib, an open-source optical flow library available from the Python Package Index. It enables the user to manipulate, evaluate, and combine flow fields. The PyTorch version of oflib is fully differentiable and therefore suitable for use in deep learning methods requiring back-propagation. The optical flow estimation from the network trained on synthetic data is evaluated using three performance metrics obtained from tracking a grid and sparsely annotated ground truth points. The evaluation benchmark consists of a series of challenging real intra-operative clips obtained from an extensive internally acquired dataset encompassing representative surgical cases. The deep learning approach clearly outperforms variational baseline methods and is shown to generalise well to real data showing scenarios routinely observed during vitreoretinal procedures. This indicates complex synthetic training datasets can be used to specifically guide optical flow estimation, laying the foundation for a robust system which can assist with intra-operative tracking of moving surgical targets even when occluded

Computer vision methods for unconstrained gesture recognition in the context of sign language annotation

Cette thèse porte sur l'étude des méthodes de vision par ordinateur pour la reconnaissance de gestes naturels dans le contexte de l'annotation de la Langue des Signes. La langue des signes (LS) est une langue gestuelle développée par les sourds pour communiquer. Un énoncé en LS consiste en une séquence de signes réalisés par les mains, accompagnés d'expressions du visage et de mouvements du haut du corps, permettant de transmettre des informations en parallèles dans le discours. Même si les signes sont définis dans des dictionnaires, on trouve une très grande variabilité liée au contexte lors de leur réalisation. De plus, les signes sont souvent séparés par des mouvements de co-articulation. Cette extrême variabilité et l'effet de co-articulation représentent un problème important dans les recherches en traitement automatique de la LS. Il est donc nécessaire d'avoir de nombreuses vidéos annotées en LS, si l'on veut étudier cette langue et utiliser des méthodes d'apprentissage automatique. Les annotations de vidéo en LS sont réalisées manuellement par des linguistes ou experts en LS, ce qui est source d'erreur, non reproductible et extrêmement chronophage. De plus, la qualité des annotations dépend des connaissances en LS de l'annotateur. L'association de l'expertise de l'annotateur aux traitements automatiques facilite cette tâche et représente un gain de temps et de robustesse. Le but de nos recherches est d'étudier des méthodes de traitement d'images afin d'assister l'annotation des corpus vidéo: suivi des composantes corporelles, segmentation des mains, segmentation temporelle, reconnaissance de gloses. Au cours de cette thèse nous avons étudié un ensemble de méthodes permettant de réaliser l'annotation en glose. Dans un premier temps, nous cherchons à détecter les limites de début et fin de signe. Cette méthode d'annotation nécessite plusieurs traitements de bas niveau afin de segmenter les signes et d'extraire les caractéristiques de mouvement et de forme de la main. D'abord nous proposons une méthode de suivi des composantes corporelles robuste aux occultations basée sur le filtrage particulaire. Ensuite, un algorithme de segmentation des mains est développé afin d'extraire la région des mains même quand elles se trouvent devant le visage. Puis, les caractéristiques de mouvement sont utilisées pour réaliser une première segmentation temporelle des signes qui est par la suite améliorée grâce à l'utilisation de caractéristiques de forme. En effet celles-ci permettent de supprimer les limites de segmentation détectées en milieu des signes. Une fois les signes segmentés, on procède à l'extraction de caractéristiques visuelles pour leur reconnaissance en termes de gloses à l'aide de modèles phonologiques. Nous avons évalué nos algorithmes à l'aide de corpus internationaux, afin de montrer leur avantages et limitations. L'évaluation montre la robustesse de nos méthodes par rapport à la dynamique et le grand nombre d'occultations entre les différents membres. L'annotation résultante est indépendante de l'annotateur et représente un gain de robustese important.This PhD thesis concerns the study of computer vision methods for the automatic recognition of unconstrained gestures in the context of sign language annotation. Sign Language (SL) is a visual-gestural language developed by deaf communities. Continuous SL consists on a sequence of signs performed one after another involving manual and non-manual features conveying simultaneous information. Even though standard signs are defined in dictionaries, we find a huge variability caused by the context-dependency of signs. In addition signs are often linked by movement epenthesis which consists on the meaningless gesture between signs. The huge variability and the co-articulation effect represent a challenging problem during automatic SL processing. It is necessary to have numerous annotated video corpus in order to train statistical machine translators and study this language. Generally the annotation of SL video corpus is manually performed by linguists or computer scientists experienced in SL. However manual annotation is error-prone, unreproducible and time consuming. In addition de quality of the results depends on the SL annotators knowledge. Associating annotator knowledge to image processing techniques facilitates the annotation task increasing robustness and speeding up the required time. The goal of this research concerns on the study and development of image processing technique in order to assist the annotation of SL video corpus: body tracking, hand segmentation, temporal segmentation, gloss recognition. Along this PhD thesis we address the problem of gloss annotation of SL video corpus. First of all we intend to detect the limits corresponding to the beginning and end of a sign. This annotation method requires several low level approaches for performing temporal segmentation and for extracting motion and hand shape features. First we propose a particle filter based approach for robustly tracking hand and face robust to occlusions. Then a segmentation method for extracting hand when it is in front of the face has been developed. Motion is used for segmenting signs and later hand shape is used to improve the results. Indeed hand shape allows to delete limits detected in the middle of a sign. Once signs have been segmented we proceed to the gloss recognition using lexical description of signs. We have evaluated our algorithms using international corpus, in order to show their advantages and limitations. The evaluation has shown the robustness of the proposed methods with respect to high dynamics and numerous occlusions between body parts. Resulting annotation is independent on the annotator and represents a gain on annotation consistency

Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application

International Congress of Mathematicians: 2022 July 6–14: Proceedings of the ICM 2022

Following the long and illustrious tradition of the International Congress of Mathematicians, these proceedings include contributions based on the invited talks that were presented at the Congress in 2022. Published with the support of the International Mathematical Union and edited by Dmitry Beliaev and Stanislav Smirnov, these seven volumes present the most important developments in all fields of mathematics and its applications in the past four years. In particular, they include laudations and presentations of the 2022 Fields Medal winners and of the other prestigious prizes awarded at the Congress. The proceedings of the International Congress of Mathematicians provide an authoritative documentation of contemporary research in all branches of mathematics, and are an indispensable part of every mathematical library