document

Image Analysis for All-Optical Electrophysiology

Kostina, Polina (author)

The study of the electrophysiological properties of neurons has reached a new level thanks to recent techniques that combine knowledge from different fields of science. For a method such as all-optical electrophysiology, the quality of cell segmentation in the image has one of the critical roles since the accuracy of illumination and...

bachelor thesis 2022

document

Leverage Spatial Priors and Copy-Paste to Boost Contrastive Learning for Urban-Scene Segmentation

Zeng, Liang (author)

Self-supervised contrastive learning has achieved remarkable performance in computer vision. Its success relies on certain priors that vary from different tasks and data at hand, e.g, the object-centric prior implied by ImageNet. For segmentation on complex scenes, researchers have introduced salient objects or auxiliary labels as priors to...

master thesis 2022

document

Theory and Applications of Differential Equation Methods for Graph-based Learning

Budd, J.M. (author)

A large number of modern learning problems involve working with highly interrelated and interconnected data. Graph-based learning is an emerging technique for approaching such problems, by representing this data as a graph (a.k.a. a network). That is, the points of data are represented by the vertices of the graph, and then the edges linking...

doctoral thesis 2022

document

Neural Architecture Search for Medical Image Segmentation

Bosma, Martijn (author)

Deep Neural Networks (DNNs) have the potential to make various clinical procedures more time-efficient by automating medical image segmentation; largely due to their strong, in some cases human-level, performance. The design of the best possible medical image segmentation DNN, however, is task-specific. Neural Architecture Search (NAS), i.e.,...

master thesis 2022

document

Efficient MSPSO Sampling for Object Detection and 6D Pose Estimation in 3D Scenes

Xing, Xuejun (author), Guo, Jianwei (author), Nan, L. (author), Gu, Qingyi (author), Zhang, Xiaopeng (author), Yan, Dong Ming (author)

The point pair feature (PPF) is widely used in industrial applications for estimating 6D poses of known objects from unrecognized point clouds. The key to the success of PPF matching is to establish correct 3D correspondences between the object and the scene, i.e., finding as many valid similar point pairs as possible. Thus, a set of...

journal article 2022

document

Heed the noise in performance evaluations in neural architecture search

Dushatskiy, A. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)

Neural Architecture Search (NAS) has recently become a topic of great interest. However, there is a potentially impactful issue within NAS that remains largely unrecognized: noise. Due to stochastic factors in neural network initialization, training, and the chosen train/validation dataset split, the performance evaluation of a neural network...

conference paper 2022

document

Data variation-aware medical image segmentation

Dushatskiy, A. (author), Lowe, Gerry (author), Bosman, P.A.N. (author), Alderliesten, T. (author)

Deep learning algorithms have become the golden standard for segmentation of medical imaging data. In most works, the variability and heterogeneity of real clinical data is acknowledged to still be a problem. One way to automatically overcome this is to capture and exploit this variation explicitly. Here, we propose an approach that improves...

conference paper 2022

document

Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process: A comprehensive study to improve AFP inspection

Meister, S. (author), Wermes, Mahdieu A.M. (author), Stüve, Jan (author), Groves, R.M. (author)

The aerospace industry has established the Automated Fiber Placement process as a common technique for manufacturing fibre reinforced components. In this process multiple composite tows are placed simultaneously onto a tool. Currently in such processes manual testing requires often up to 50% of the manufacturing duration. Moreover, the...

review 2021

document

Esophageal Tumor Segmentation in CT Images using a Dilated Dense Attention Unet (DDAUnet)

Yousefi, Sahar (author), Sokooti, Hessam (author), Elmahdy, Mohamed S. (author), Lips, Irene M. (author), Shalmani, Mohammad T.Manzuri (author), Zinkstok, Roel T. (author), Dankers, Frank J.W.M. (author), Staring, M. (author)

Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional...

journal article 2021

document

Conditioning of deep-learning surrogate models to image data with application to reservoir characterization

Xiao, C. (author), Leeuwenburgh, O. (author), Lin, H.X. (author), Heemink, A.W. (author)

Imaging-type monitoring techniques are used in monitoring dynamic processes in many domains, including medicine, engineering, and geophysics. This paper aims to propose an efficient workflow for application of such data for the conditioning of simulation models. Such applications are very common in e.g. the geosciences, where large-scale...

journal article 2021

document

Accurate Scene Text Detection via Scale-Aware Data Augmentation and Shape Similarity Constraint

Dai, Pengwen (author), Li, Y. (author), Zhang, Hua (author), Li, Jingzhi (author), Cao, Xiaochun (author)

Scene text detection has attracted increasing concerns with the rapid development of deep neural networks in recent years. However, existing scene text detectors may overfit on the public datasets due to the limited training data, or generate inaccurate localization for arbitrary-shape scene texts. This paper presents an arbitrary-shape scene...

journal article 2021

document

Automating malaria diagnosis: a machine learning approach: Erythrocyte segmentation and parasite identification in thin blood smear microscopy images using convolutional neural networks

van Driel, Noor (author)

Reliable malaria diagnosis techniques that are suitable for point-of-care testing in high burden areas, are vital for effective treatment and monitoring of the disease. Identification of malaria parasites in Giemsa stained blood slides is currently the most widely accepted technique, but its availability is limited by the need for highly trained...

master thesis 2020

document

An evaluation of image segmentation techniques for MRI scans

Wagenaar, Claire (author)

In this thesis, several image segmentation techniques will be tested that eventually will be applied to MRI brain scans in order to detect hydrocephalus. The meth- ods include Sobel edge detection, Canny edge detection, active contour model (also known as snakes), k-means clustering and region growing. Furthermore two exten- sions are discussed....

bachelor thesis 2020

document

Algorithm assessment for layup defect segmentation from laser line scan sensor based image data

Meister, S. (author), Wermes, Mahdieu Amin Mahdieu (author), Stüve, Jan (author), Groves, R.M. (author)

The Automated Fiber Placement process is established in the aerospace industry for the production of composite components. This technique places several narrow material strips in parallel. Within current industrial Automated Fiber Placement processes the visual inspection takes typically up to 50% of overall production time. Furthermore,...

conference paper 2020

document

Landslide Detection using Random Forest Classifier

Herrera Herrera, Meylin (author)

Landslides are destructive and recurrent natural disasters that cost annually significant social and economic losses all over the world. These events can be induced by natural factors as earthquakes and extreme rainfall, as well as by human intervention, including construction and mining. A primary resource to conduct landslides studies for...

master thesis 2019

document

Image Segmentation of the γ'-Phase in Nickelbase Superalloys utilising Deep Learning

Riegger, Franzi (author)

Quantitative analysis of material microstructure is a well-known method to derive chemical and physical properties of a sample. This includes the segmentation of e.g. Light Optical Microscopy or Scanning Electron Microscopy images where each pixel is assigned to a material. Since some phases such as the γ-γ’ structure in nickelbased superalloys...

master thesis 2019

document

Feature extraction and clustering analysis of highway congestion

Nguyen, T.T. (author), Krishnakumari, P.K. (author), Calvert, S.C. (author), Vu, Hai L. (author), van Lint, J.W.C. (author)

Classification of congestion patterns is important in many areas in traffic planning and management, ranging from policy appraisal, database design, to prediction and real-time control. One of the key constraints in applying machine learning techniques for classification is the availability of sufficient data (traffic patterns) with clear and...

journal article 2019

document

Segmentation of Carpal Bones: Based on Statistical Shape Models using Spherical Harmonics

Ragunathan, CS (author)

A study has been conducted on the application of Spherical Harmonics based Statistical Shape Modelling for Image Segmentation. This study is focused on the segmentation of Wrist bones using the above mentioned technique.

master thesis 2019

document

Moving object detection and image inpainting in street-view imagery

Uittenbogaard, Ries (author)

In this thesis, a pipeline is created consisting of two parts. In the first part, the moving objects (cars, cyclists, pedestrians) are detected in street-view imagery using image segmentation neural networks and a LIDAR-based moving object detection approach. In the second part, those moving objects are deleted from the image data and an image...

master thesis 2018

document

The effect of mechanical stratigraphy on fracture pattern evolution and permeability in faulted carbonates - insights from analogue sandbox models

Muhamad Muizzudin Bin Mohd Jamaludin, Muiz (author)

Dilatant fractures and faults in carbonate rocks play an important role as fluid path for many applications. However, multidimensional characterization of these fractures remains a challenge due to structural complexity in multiscale. The progressive deformation of dilatant fracture in normal fault system from physical models are imaged in 3D...

master thesis 2018