435 research outputs found
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Gaussian Process Modeling for Upsampling Algorithms With Applications in Computer Vision and Computational Fluid Dynamics
Across a variety of fields, interpolation algorithms have been used to upsample lowresolution or coarse data fields. In this work, novel Gaussian Process based methodsare employed to solve a variety of upsampling problems. Specifically threeapplications are explored: coarse data prolongation in Adaptive Mesh Refinement(AMR) in the field of Computational Fluid Dynamics, accurate document imageupsampling to enhance Optical Character Recognition (OCR) accuracy, and fastand accurate Single Image Super Resolution (SISR). For AMR, a new, efficient,and “3rd order accurate” algorithm called GP-AMR is presented. Next, a novel,non-zero mean, windowed GP model is generated to upsample low resolution documentimages to generate a higher OCR accuracy, when compared to the industrystandard. Finally, a hybrid GP convolutional neural network algorithm is used togenerate a computationally efficient and high quality SISR model
On the Use of Low-Cost RGB-D Sensors for Autonomous Pothole Detection with Spatial Fuzzy <em>c</em>-Means Segmentation
The automated detection of pavement distress from remote sensing imagery is a promising but challenging task due to the complex structure of pavement surfaces, in addition to the intensity of non-uniformity, and the presence of artifacts and noise. Even though imaging and sensing systems such as high-resolution RGB cameras, stereovision imaging, LiDAR and terrestrial laser scanning can now be combined to collect pavement condition data, the data obtained by these sensors are expensive and require specially equipped vehicles and processing. This hinders the utilization of the potential efficiency and effectiveness of such sensor systems. This chapter presents the potentials of the use of the Kinect v2.0 RGB-D sensor, as a low-cost approach for the efficient and accurate pothole detection on asphalt pavements. By using spatial fuzzy c-means (SFCM) clustering, so as to incorporate the pothole neighborhood spatial information into the membership function for clustering, the RGB data are segmented into pothole and non-pothole objects. The results demonstrate the advantage of complementary processing of low-cost multisensor data, through channeling data streams and linking data processing according to the merits of the individual sensors, for autonomous cost-effective assessment of road-surface conditions using remote sensing technology
Metalik yansımalı yüzeylerde otomatik çizik tespiti için görüntü işleme sistemi.
In industry, problems due to human error, mechanical flaws and transportation may occur; besides, they need to be detected in fast and efficient ways. In order to eliminate failure of human inspection, automated systems come in action, usually image processing involved. This thesis work, targets one common mass production problem on specular surfaces, i.e. scratch detection. To achieve this, we have implemented two different prototypes. The low-cost system is based on basic line detection, and the mid-end system depends on learning based detection. Both systems are implemented on embedded platforms and performance comparisons are done. Detailed analysis is carried out on computational cost and detection performance. This real-world episode is done on a mechanical prototype in laboratory environmentM.S. - Master of Scienc
Space-Varying Coefficient Models for Diffusion Tensor Imaging using 3d Wavelets
In this paper, the space-varying coefficients model on the basis of B-splines (Heim et al., (2006)) is adapted to wavelet basis functions and re-examined using artificial and real data. For an introduction to diffusion tensor imaging refer to Heim et al. (2005, Chap. 2). First, wavelet theory is introduced and explained by means of 1d and 2d examples (Sections 1.1 { 1.3). Section 1.4 is dedicated to the most common thresholding techniques that serve as regularization concepts for wavelet based models. Prior to application of the 3d wavelet decomposition to the space-varying coe cient elds, the SVCM needs to be rewritten. The necessary steps are outlined in Section 2 together with the incorporation of the positive de niteness constraint using log-Cholesky parametrization. Section 3 provides a simulation study as well as a comparison with the results obtained through B-splines and standard kernel application. Finally, a real data example is presented and discussed. The theoretical parts are based on books of Gen cay et al. (2002, Chap. 1, 4-6), Härdle et al. (1998), Ogden (1997) and Jansen (2001) if not stated otherwise
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances
With the advent of Deep Learning (DL), Super-Resolution (SR) has also become
a thriving research area. However, despite promising results, the field still
faces challenges that require further research e.g., allowing flexible
upsampling, more effective loss functions, and better evaluation metrics. We
review the domain of SR in light of recent advances, and examine
state-of-the-art models such as diffusion (DDPM) and transformer-based SR
models. We present a critical discussion on contemporary strategies used in SR,
and identify promising yet unexplored research directions. We complement
previous surveys by incorporating the latest developments in the field such as
uncertainty-driven losses, wavelet networks, neural architecture search, novel
normalization methods, and the latests evaluation techniques. We also include
several visualizations for the models and methods throughout each chapter in
order to facilitate a global understanding of the trends in the field. This
review is ultimately aimed at helping researchers to push the boundaries of DL
applied to SR.Comment: accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 202
COLOR MAPPING FOR CAMERA-BASED COLOR CALIBRATION AND COLOR TRANSFER
Ph.DDOCTOR OF PHILOSOPH
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