A better, more optimized processing pipeline for functional connectivity (FC) data will likely accelerate practical advances within the field of neuroimaging. When using correlation-based measures of FC, researchers have recently employed a few data-driven methods to maximize its predictive power. In this study, we apply a few of these post-processing methods in both task, twin, and subject identification problems. First, we employ PCA reconstruction of the original dataset, which has been successfully used to maximize subject-level identifiability. We show there is dataset-dependent optimal PCA reconstruction for task and twin identification. Next, we analyze FCs in their native geometry using tangent space projection with various mean covariance reference matrices. We demonstrate that the tangent projection of the original FCs can drastically increase subject and twin identification rates. For example, the identification rate of 106 MZ twin pairs increased from 0.487 of the original FCs to 0.943 after tangent projection with the logarithmic Euclidean reference matrix. We also use Schaefer’s variable parcellation sizes to show that increasing parcellation granularity in general increases twin and subject identification rates. Finally, we show that our custom convolutional neural network classifier achieves an average task identification rate of 0.986, surpassing state-of-the-art results. These post-processing methods are promising for future research in functional connectome predictive modeling and, if optimized further, can likely be extended into clinical applications.

Batch normalization is a recently popularized method for accelerating the training of deep feed-forward neural networks. Apart from speed improvements, the technique reportedly enables the use of higher learning rates, less careful parameter initialization, and saturating nonlinearities. The authors note that the precise effect of batch normalization on neural networks remains an area of further study, especially regarding their gradient propagation. Our work compares the convergence behavior of batch normalized networks with ones that lack such normalization. We train both a small multi-layer perceptron and a deep convolutional neural network on four popular image datasets. By systematically altering critical hyperparameters, we isolate the effects of batch normalization both in general and with respect to these hyperparameters. Our experiments show that batch normalization indeed has positive effects on many aspects of neural networks but we cannot confirm significant convergence speed improvements, especially when wall time is taken into account. Overall, batch normalized models achieve higher validation and test accuracies on all datasets, which we attribute to its regularizing effect and more stable gradient propagation. Due to these results, the use of batch normalization is generally advised since it prevents model divergence and may increase convergence speeds through higher learning rates. Regardless of these properties, we still recommend the use of variance-preserving weight initialization, as well as rectifiers over saturating nonlinearities. / Batch normalization är en metod för att påskynda träning av djupa framåtmatande neuronnnätv som nyligt blivit populär. Förutom hastighetsförbättringar så tillåter metoden enligt uppgift högre träningshastigheter, mindre noggrann parameterinitiering och mättande olinjäriteter. Författarna noterar att den exakta effekten av batch normalization på neuronnät fortfarande är ett område som kräver ytterligare studier, särskilt när det gäller deras gradient-fortplantning. Vårt arbete jämför konvergensbeteende mellan nätverk med och utan batch normalization. Vi träner både en liten flerlagersperceptron och ett djupt faltningsneuronnät på fyra populära bilddatamängder. Genom att systematiskt ändra kritiska hyperparametrar isolerar vi effekterna från batch normalization både i allmänhet och med avseende på dessa hyperparametrar. Våra experiment visar att batch normalization har positiva effekter på många aspekter av neuronnät, men vi kan inte bekräfta att det ger betydelsefullt snabbare konvergens, speciellt när väggtiden beaktas. Allmänt så uppnår modeller med batch normalization högre validerings- och testträffsäkerhet på alla datamängder, vilket vi tillskriver till dess reglerande effekt och mer stabil gradientfortplantning. På grund av dessa resultat är användningen av batch normalization generellt rekommenderat eftersom det förhindrar modelldivergens och kan öka konvergenshastigheter genom högre träningshastigheter. Trots dessa egenskaper rekommenderar vi fortfarande användning av varians-bevarande viktinitiering samt likriktare istället för mättande olinjäriteter.

Bokeh is defined as a soft out of focus blur. An image with bokeh has a subject in focus and an artistically blurry background. To capture images with real bokeh, specific camera parameter choices need to be made. One essential choice is to use a big lens with a wide aperture. Because of smartphone cameras’ small size, it becomes impossible to achieve real bokeh. Commonly, new models of smartphones have artificial bokeh implemented when taking pictures, but it is uncommon to be able to capture videos with artificial bokeh. Video segmentation is more complicated than image segmentation because it puts a higher demand on performance. The result should also be temporally consistent. In this project, the aim is to create a method that can apply real-time video bokeh on a smartphone.   The project consists of two parts. The first part is to segment the subject of the video. This process is performed with convolutional neural networks. Three image segmentation networks were implemented for video, trained, and evaluated. The model that illustrated the most potential was the SINet model and was chosen as the most suitable architecture for the task.  The second part of the project is to manipulate the background to be aesthetically pleasing while at the same time mimicking real optics to some degree. This is achieved by creating a depth and contrast map. With the depth map, the background can be blurred based on depth. The shape of the bokeh shapes also varies with the depth. The contrast map is used to locate bokeh points. The main part of the project is the segmentation part.  The result for this project is a method that achieves an accurate segmentation and creates an artistic background. The different architectures illustrated similar results in terms of accuracy but different in terms of inference time. Situations existed where the segmentation failed and included too much of the background. This could potentially be counteracted with a bigger and more varied dataset. The method is performed in real-time on a computer but no conclusions could be made if it works in real-time on a smartphone.

We investigated the advantage of combining RGB images with depth data to get more robust object classifications and detections using pre-trained deep convolutional neural networks. We relied upon the raw images from publicly available datasets captured using Microsoft Kinect cameras. The raw images varied in size, and therefore required resizing to fit our network. We designed a resizing method called "bleeding edge" to avoid distorting the objects in the images. We present a novel method of interpolating the missing depth pixel values by comparing to similar RGB values. This method proved superior to the other methods tested. We showed that a simple colormap transformation of the depth image can provide close to state-of-art performance. Using our methods, we can present state-of-art performance on the Washington Object dataset and we provide some results on the Washington Scenes (V1) dataset. Specifically, for the detection, we used contours at different thresholds to find the likely object locations in the images. For the classification task we can report state-of-art results using only RGB and RGB-D images, depth data alone gave close to state-of-art results. For the detection task we found the RGB only detector to be superior to the other detectors.

Research on brain-computer interfaces (BCIs) has been around for decades and recently the inner speech paradigm was picked up in the area. The realization of a functioning BCI could improve the life quality of many people, especially persons affected by Locked-In-Syndrome or similar illnesses. Although implementing a working BCI is too large of a commitment for a master's thesis, this thesis will focus on investigating machine learning methods to decode inner speech using data collected from the non-invasive and portable method electroencephalography (EEG). Among the methods investigated are three CNN architectures and transfer learning. The results show that the EEGNet architecture consistently reaches high classification accuracies, with the best model achieving an accuracy of 29.05%.

This project studies the removal of non-anatomical objects from medical images. During tumor ablation procedures, the ablation probes appear in the image, hindering the performance of segmentation, registration, and dose computation algorithms. These algorithms can also be affected by artifacts and noise generated by body implants. Image inpainting methods allow the completion of the missing or distorted regions, generating realistic structures coherent with the rest of the image. During the last decade, the study of image inpainting methods has accelerated due to advances in deep learning and the increase in the consumption of multimedia content. Models applying generative adversarial networks have excelled at the task of image synthesis. However, there has not been much study done on medical image inpainting. In this project, a new inpainting method is proposed for recovering missing information from medical images. This method consists of a two-stage model, where a coarse network is followed by a refinement network, both of which are U-Nets. The refinement network is trained together with a discriminator, providing adversarial learning. The model is trained on a dataset of CT images of the liver and, in order the mimic the areas where information is missing, regular and irregular shaped masks are applied. The trained models are compared both quantitatively and qualitatively. Due to the lack of standards and accurate metrics in image inpainting tasks, results cannot be easily compared to current approaches. However, qualitative analysis of the inpainted images shows promising results. In addition, this project identifies the Frechet Inception Distance as a more valid metric than older metrics commonly used for evaluation of image inpainting models. In conclusion, this project provides an inpainting model for medical images, which could be used during tumor ablation procedures and for noise and artifact elimination. Future research could include implementing a 3D model to provide more coherent results for inpainting patients - a stack of images - instead of single images. / I detta projekt undersöks metoder för avlägsnande av icke-anatomiska föremål från medicinska bilder. Bilder tagna under ablationsbehandling av tumörer innehåller själva ablationsnålen, denna kan hindra segmenterings-, registrerings-och dosberäknings-algoritmer för att uppnå önska resultat. Dessa algoritmer kan också påverkas av artefakter och brus som genereras av olika metallimplantat. Bildifyllningsmetoder gör det möjligt att ersätta regioner som saknar eller innehåller inkorrekt bilddata, med realistiska strukturer som är sammanhängande med resten av bilden. Under det senaste decenniet har intresset för metoder för bildifyllning accelererat på grund av framsteg inom djupinlärning och ökad konsumtion av multimediainnehåll. Modeller som använder generative adversarial networks har utmärkt sig i bildsynteseringsuppgifter. Det har dock inte gjorts så många studier gällande bildifyllning av medicinska bilder. I detta projekt föreslås en ny bildifyllningsmetod för att återställa regioner med inkorrekt information i medicinska bilder. Denna metod består av ett tvåstegsnätverk, där ett första nätverk följs av ett förfiningsnätverk, båda av typen U-net. Förfiningsnätverk tränas tillsammans med ett diskriminatornätverk. Modellen tränas på ett dataset av CT-bilder av levern. För att efterlikna de områden där information saknas, applicerades masker av olika former. De färdigtränade modellerna jämfördes både kvantitativt och kvalitativt. På grund av bristen på standarder och noggranna mätvärden för bildifyllningsmetoder, kan resultaten inte enkelt jämföras med existerande metoder. Men kvalitativ analys av de målade bilderna visar ganska lovande resultat. Modellen presterar som bäst i områden inte innehåller komplexa strukturer. Sammanfattningsvis har en fungerande bildifyllningsmetod för medicinska bilder skapats och som kan användas vid tumörablation och för eliminering av bildartefakter. Framtida forskning kan inkludera implementering av en 3D-modell för att ge mer sammanhängande resultat.

Most ab initio simulation packages based on Density Functional Theory (DFT) use the Superposition of Atomic Densities (SAD) as a starting point of the self-consistent fi eld (SCF) iteration. However, this trial charge density without modeling atomic iterations nonlinearly may lead to a relatively slow or even failed convergence. This thesis proposes a machine learning-based scheme to improve the initial guess. We train a 3-Dimensional Convolutional Neural Network (3D CNN) to map the SAD initial guess to the corresponding converged charge density with simple structures. We show that the 3D CNN-processed charge density reduces the number of required SCF iterations at different unit cell complexity levels.

For some time now, the fashion industry has been a big contributor to humanity's carbon emissions. If we are to become a more sustainable society and cut down on our pollution, this industry needs to be reformed. The clothes we wear must be reused to a greater extent than today. Unfortunately, a big part of the Swedish population experiences a lack of available items on the second-hand market. This paper presents a proof-of-concept application that could be a possible solution. The application scans online second-hand websites and separates composite ads into new, separate, ads. This makes it easier for potential buyers to find the items they are looking for. The application uses a web scraper written in Java combined with a convolutional neural network for classification. The CNN is a modified version of the ResNet50 model which is trained on a dataset collected from a Swedish second-hand site. At the moment the network supports 5 types of clothing with an accuracy of 86%. Tests were performed to investigate the potential of scaling up the model. These experiments were made using a 3rd party dataset called deepFashion. This dataset consists of over 800,000 images of clothes in different settings. The tests indicate that given a larger dataset the model could handle up to 31 classes with an accuracy of at least 57% and possibly as high as 76%. This evolved model did not produce any meaning full results when tested on real second-hand images since the deepFashion network mostly consists of clothes worn by models. Further research could see this application evolve into one that could sort ads on not only type, but colour, material and other properties to provide even more exhaustive labels.

This work proposes a system that utilizes both infrared and visual imagery to create a more robust object detection and classification system. The system consists of two main parts: a moving object detector and a target classifier. The first stage detects moving objects in visible and infrared spectrum using background subtraction based on Gaussian Mixture Models. Low-level fusion is performed to combine the foreground regions in the respective domain. For the second stage, a Convolutional Neural Network (CNN), pre-trained on the ImageNet dataset is used to classify the detected targets into one of the pre-defined classes; human and vehicle. The performance of the proposed object detector is evaluated using multiple video streams recorded in different areas and under various weather conditions, which form a broad basis for testing the suggested method. The accuracy of the classifier is evaluated from experimentally generated images from the moving object detection stage supplemented with publicly available CIFAR-10 and CIFAR-100 datasets. The low-level fusion method shows to be more effective than using either domain separately in terms of detection results. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

Additive manufacturing (AM) allows building complex shapes with high accuracy. The X-ray Computed Tomography (XCT) is one of the promising non-destructive evaluation techniques for the evaluation of subsurface defects in an additively manufactured component. Automatic defect detection and segmentation methods can assist part inspection for quality control. However, automatic detection and segmentation of defects in XCT data of AM possess challenges due to contrast, size, and appearance of defects. In this research different deep learning techniques have been applied on publicly available XCT image datasets of additively manufactured cobalt chrome samples produced by the National Institute of Standards and Technology (NIST). To assist the data labeling image processing techniques were applied which are median filtering, auto local thresholding using Bernsen’s algorithm, and contour detection. A convolutional neural network (CNN) based state-of-art object algorithm YOLOv5 was applied for defect detection. Defect segmentation in XCT slices was successfully achieved applying U-Net, a CNN-based network originally developed for biomedical image segmentation. Three different variants of YOLOv5 which are YOLOv5s, YOLOv5m, and YOLOV5l were implemented in this study. YOLOv5s achieved defect detection mean average precision (mAP) of 88.45 % at an intersection over union (IoU) threshold of 0.5. And mAP of 57.78% at IoU threshold 0.5 to 0.95 using YOLOv5M was achieved. Additionally, defect detection recall of 87.65% was achieved using YOLOv5s, whereas a precision of 71.61 % was found using YOLOv5l. YOLOv5 and U-Net show promising results for defect detection and segmentation respectively. Thus, it is found that deep learning techniques can improve the automatic defect detection and segmentation in XCT data of AM.