Characterization of brain development in preterm children using ultrasound images

El període més important per al desenvolupament del cervell humà és la fase fetal. Durant aquest període de quaranta setmanes, es produeixen canvis morfològics importants al cervell humà, incloent un enorme augment de la superfície cerebral després del desenvolupament dels solcs i circumvolucions. En els nadons prematurs, aquests canvis es produeixen en un entorn extrauterí i s’ha demostrat un deteriorament del desenvolupament cerebral en aquesta població a una edat equivalent al terme. Un atles normalitzat de maduració cerebral amb ultrasons cerebrals pot permetre als clínics avaluar aquests canvis setmanalment des del naixement fins a una edat equivalent al terme. Basat en les imatges dels diferents nadons proporcionats per dos investigadors clínics, aquest estudi proposa una aplicació web implementada amb Python i les seves diferents biblioteques, inclòs Dash, i accessible a través de Docker que permet accedir directament a l’aplicació dissenyada i a la seva base de dades. D’aquesta manera, es proporciona una eina que permet fer una primera definició de les diferents ranures manualment per passar-les finalment per un algorisme amb l’objectiu de millorar la precisió i poder exportar tant la imatge com les coordenades que se n’obtenen.El período más importante para el desarrollo del cerebro humano es la fase fetal. Durante este período de cuarenta semanas, se producen importantes cambios morfológicos en el cerebro humano, incluido un gran aumento en la superficie del cerebro a raíz del desarrollo de surcos y circunvoluciones. En los recién nacidos prematuros, estos cambios se producen en un entorno extrauterino y se ha demostrado un deterioro del desarrollo cerebral en esta población a la edad equivalente a término. Un atlas normalizado de maduración cerebral con ecografía cerebral puede permitir a los médicos evaluar estos cambios semanalmente desde el nacimiento hasta la edad equivalente a término. A partir de las imágenes de los diferentes bebés proporcionados por dos investigadores clínicos, este estudio propone una aplicación web implementada con Python y sus diferentes bibliotecas, incluida Dash, y accesible a través de Docker que permite el acceso directo a la aplicación diseñada y su base de datos. De esta forma, se proporciona una herramienta que permite realizar una primera definición de las diferentes ranuras de forma manual para finalmente pasarlas por un algoritmo con el objetivo de mejorar la precisión y poder exportar tanto la imagen como las coordenadas obtenidas de la misma.The most important period for human’s brain development is the fetal phase. During these forty weeks period, important morphological changes take place in the human brain, including a huge increase in the brain surface following the development of sulci and gyri. In preterm newborns these changes occur in an extrauterine environment, and an impaired brain development has been shown in this population at term equivalent age. A normalized atlas of brain maturation with cerebral ultrasound may allow the clinicians to assess these changes weekly from birth to term equivalent age. Based on the images of the different babies provided by two clinical researchers, this study proposes a web application implemented with python and its different libraries, including Dash, and accessible through docker that allows direct access to the designed app and its database. In this way, a tool is provided that allows a first definition of the different grooves to be made manually to finally pass them through an algorithm with the aim of improving precision and being able to export both the image and the coordinates obtained from it

Robotic Cameraman for Augmented Reality based Broadcast and Demonstration

In recent years, a number of large enterprises have gradually begun to use vari-ous Augmented Reality technologies to prominently improve the audiences’ view oftheir products. Among them, the creation of an immersive virtual interactive scenethrough the projection has received extensive attention, and this technique refers toprojection SAR, which is short for projection spatial augmented reality. However,as the existing projection-SAR systems have immobility and limited working range,they have a huge difficulty to be accepted and used in human daily life. Therefore,this thesis research has proposed a technically feasible optimization scheme so thatit can be practically applied to AR broadcasting and demonstrations. Based on three main techniques required by state-of-art projection SAR applica-tions, this thesis has created a novel mobile projection SAR cameraman for ARbroadcasting and demonstration. Firstly, by combining the CNN scene parsingmodel and multiple contour extractors, the proposed contour extraction pipelinecan always detect the optimal contour information in non-HD or blurred images.This algorithm reduces the dependency on high quality visual sensors and solves theproblems of low contour extraction accuracy in motion blurred images. Secondly, aplane-based visual mapping algorithm is introduced to solve the difficulties of visualmapping in these low-texture scenarios. Finally, a complete process of designing theprojection SAR cameraman robot is introduced. This part has solved three mainproblems in mobile projection-SAR applications: (i) a new method for marking con-tour on projection model is proposed to replace the model rendering process. Bycombining contour features and geometric features, users can identify objects oncolourless model easily. (ii) a camera initial pose estimation method is developedbased on visual tracking algorithms, which can register the start pose of robot to thewhole scene in Unity3D. (iii) a novel data transmission approach is introduced to establishes a link between external robot and the robot in Unity3D simulation work-space. This makes the robotic cameraman can simulate its trajectory in Unity3D simulation work-space and project correct virtual content. Our proposed mobile projection SAR system has made outstanding contributionsto the academic value and practicality of the existing projection SAR technique. Itfirstly solves the problem of limited working range. When the system is running ina large indoor scene, it can follow the user and project dynamic interactive virtualcontent automatically instead of increasing the number of visual sensors. Then,it creates a more immersive experience for audience since it supports the user hasmore body gestures and richer virtual-real interactive plays. Lastly, a mobile systemdoes not require up-front frameworks and cheaper and has provided the public aninnovative choice for indoor broadcasting and exhibitions

A robotic engine assembly pick-place system based on machine learning

Industrial revolution brought humans and machines together in building a better future. Where in one hand there is need to replace the repetitive jobs with machines to increase efficiency and volume of production, on the other hand intelligent and autonomous machines have still a long way to go to achieve dexterity of a human. The current scenario requires a system which can utilise best of both the human and the machine. This thesis studies a industrial use case scenario where human-machine combine their skills to build an autonomous pick place system. This study takes a small step towards the human-robot consortium primarily focusing on developing a vision based system for object detection followed by a manipulator pick place operation. This thesis can be divided into two parts : 1. Scene analysis, where a Convolutional Neural Network (CNN) is used for object detection followed by generation of grasping points using object edge image and an algorithm developed during this thesis. 2. Implementation, it focuses on motion generation while taking care of external disturbances to perform successful pick-place operation. In addition human involvement is required which includes teaching trajectory points for the robot to follow. This trajectory is used to generate image data-set for a new object type and thereafter generating new object detection model. The author primarily focuses on building a system framework where the complexities related to robot programming such as generating trajectory points and informing grasping position is not required. The system automatically detects object and performs a pick place operation, resulting in relieving user from robot programming. The system is composed of a depth camera and a manipulator. Camera is the only sensor available for scene analysis and the action is performed using a Franka manipulator. The two components work in request-response mode over ROS. This thesis introduces a newer approaches such as, dividing an workspace image into its constituent object images and performing object detection, creating training data, generating grasp points based on object shape along length of an object. The thesis also presents a case study where three different objects are chosen as test objects. The experiments are a demonstration of the methods applied and efficiency attained. The case study also provides a glimpse of the future research and development areas

Computational models for image contour grouping

Contours are one dimensional curves which may correspond to meaningful entities such as object boundaries. Accurate contour detection will simplify many vision tasks such as object detection and image recognition. Due to the large variety of image content and contour topology, contours are often detected as edge fragments at first, followed by a second step known as {u0300}{u0300}contour grouping'' to connect them. Due to ambiguities in local image patches, contour grouping is essential for constructing globally coherent contour representation. This thesis aims to group contours so that they are consistent with human perception. We draw inspirations from Gestalt principles, which describe perceptual grouping ability of human vision system. In particular, our work is most relevant to the principles of closure, similarity, and past experiences. The first part of our contribution is a new computational model for contour closure. Most of existing contour grouping methods have focused on pixel-wise detection accuracy and ignored the psychological evidences for topological correctness. This chapter proposes a higher-order CRF model to achieve contour closure in the contour domain. We also propose an efficient inference method which is guaranteed to find integer solutions. Tested on the BSDS benchmark, our method achieves a superior contour grouping performance, comparable precision-recall curves, and more visually pleasant results. Our work makes progresses towards a better computational model of human perceptual grouping. The second part is an energy minimization framework for salient contour detection problem. Region cues such as color/texture homogeneity, and contour cues such as local contrast, are both useful for this task. In order to capture both kinds of cues in a joint energy function, topological consistency between both region and contour labels must be satisfied. Our technique makes use of the topological concept of winding numbers. By using a fast method for winding number computation, we find that a small number of linear constraints are sufficient for label consistency. Our method is instantiated by ratio-based energy functions. Due to cue integration, our method obtains improved results. User interaction can also be incorporated to further improve the results. The third part of our contribution is an efficient category-level image contour detector. The objective is to detect contours which most likely belong to a prescribed category. Our method, which is based on three levels of shape representation and non-parametric Bayesian learning, shows flexibility in learning from either human labeled edge images or unlabelled raw images. In both cases, our experiments obtain better contour detection results than competing methods. In addition, our training process is robust even with a considerable size of training samples. In contrast, state-of-the-art methods require more training samples, and often human interventions are required for new category training. Last but not least, in Chapter 7 we also show how to leverage contour information for symmetry detection. Our method is simple yet effective for detecting the symmetric axes of bilaterally symmetric objects in unsegmented natural scene images. Compared with methods based on feature points, our model can often produce better results for the images containing limited texture

A topological solution to object segmentation and tracking

The world is composed of objects, the ground, and the sky. Visual perception of objects requires solving two fundamental challenges: segmenting visual input into discrete units, and tracking identities of these units despite appearance changes due to object deformation, changing perspective, and dynamic occlusion. Current computer vision approaches to segmentation and tracking that approach human performance all require learning, raising the question: can objects be segmented and tracked without learning? Here, we show that the mathematical structure of light rays reflected from environment surfaces yields a natural representation of persistent surfaces, and this surface representation provides a solution to both the segmentation and tracking problems. We describe how to generate this surface representation from continuous visual input, and demonstrate that our approach can segment and invariantly track objects in cluttered synthetic video despite severe appearance changes, without requiring learning.Comment: 21 pages, 6 main figures, 3 supplemental figures, and supplementary material containing mathematical proof

A Multistage Framework for Detection of Very Small Objects

Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our two-stage cascade classifier based on two ResNet50 models. The maximum size of the images used for training the proposed framework is 32×32 pixels. The experiments are conducted using rescaled German Traffic Sign Recognition Benchmark dataset (GTSRB) and downsampled German Traffic Sign Detection Benchmark dataset (GTSDB). Unlike MS COCO and DOTA datasets, the resulting GTSDB turns out to be very challenging for any small object detection algorithm due to not only the size of objects of interest but also the complex textures of the background. Our experimental results show that the proposed method detects small traffic signs with an average precision of 0.332 at the intersection over union of 0.3