Image mosaicing based condition monitoring approach for multi robots at production lines in industrial autonomy systems

In today industry, manufacturing become big and serial as it never been before thanks to the autonomy robots. Hitches on such autonomy systems used in industrial production may cause production delaying. In this study, it is aimed to obtain alive bird's eye view map of full system in order to monitor manufacturing robots at production facilities that are big and impossible to be monitored with only one camera. Finding the similar scenes of input images, estimation of homography, warping and blending operations are applied respectively in order to mosaic the images by twos. Thus the robots in the facility can be observed in one screen. With observation of the obtained images, faults on cyber-physical systems that may cause damage in machines which are not cheap can be handled beforetime

Spherical mosaic construction using physical analogy for consistent image alignment

The research contained in this thesis is an investigation into mosaic construction. Mosaic techniques are used to obtain images with a large field of view by assembling a sequence of smaller individual overlapping images. In existing methods of mosaic construction only successive images are aligned. Accumulation of small alignment errors occur, and in the case of the image path returning to a previous position in the mosaic, a significant mismatch between nonconsecutive images will result (looping path problem). A new method for consistently aligning all the images in a mosaic is proposed in this thesis. This is achieved by distribution of the small alignment errors. Each image is allowed to modify its position relative to its neighbour images in the mosaic by a small amount with respect to the computed registration. Two images recorded by a rotating ideal camera are related by the same transformation that relates the camera's sensor plane at the time the images were captured. When two images overlap, the intensity values in both images coincide through the intersection line of the sensor planes. This intersection line has the property that the images can be seamlessly joined through that line. An analogy between the images and the physical world is proposed to solve the looping path problem. The images correspond to rigid objects, and these are linked with forces which pull them towards the right positions with respect to their neighbours. That is, every pair of overlapping images are "hinged" through their corresponding intersection line. Aided by another constraint named the spherical constraint, this network of selforganising images has the ability of distributing itself on the surface of a sphere. As a direct result of the new concepts developed in this research work, spherical mosaics (i.e. mosaics with unlimited horizontal and vertical field of view) can be created

Graph matching with a dual-step EM algorithm

This paper describes a new approach to matching geometric structure in 2D point-sets. The novel feature is to unify the tasks of estimating transformation geometry and identifying point-correspondence matches. Unification is realized by constructing a mixture model over the bipartite graph representing the correspondence match and by affecting optimization using the EM algorithm. According to our EM framework, the probabilities of structural correspondence gate contributions to the expected likelihood function used to estimate maximum likelihood transformation parameters. These gating probabilities measure the consistency of the matched neighborhoods in the graphs. The recovery of transformational geometry and hard correspondence matches are interleaved and are realized by applying coupled update operations to the expected log-likelihood function. In this way, the two processes bootstrap one another. This provides a means of rejecting structural outliers. We evaluate the technique on two real-world problems. The first involves the matching of different perspective views of 3.5-inch floppy discs. The second example is furnished by the matching of a digital map against aerial images that are subject to severe barrel distortion due to a line-scan sampling process. We complement these experiments with a sensitivity study based on synthetic data

A wide angle tail radio galaxy in the COSMOS field: evidence for cluster formation

We have identified a complex galaxy cluster system in the COSMOS field via a wide angle tail (WAT) radio galaxy consistent with the idea that WAT galaxies can be used as tracers of clusters. The WAT galaxy, CWAT-01, is coincident with an elliptical galaxy resolved in the HST-ACS image. Using the COSMOS multiwavelength data set, we derive the radio properties of CWAT-01 and use the optical and X-ray data to investigate its host environment. The cluster hosting CWAT-01 is part of a larger assembly consisting of a minimum of four X-ray luminous clusters within ~2 Mpc distance. We apply hydrodynamical models that combine ram pressure and buoyancy forces on CWAT-01. These models explain the shape of the radio jets only if the galaxy's velocity relative to the intra-cluster medium (ICM) is in the range of about 300-550 km/s which is higher than expected for brightest cluster galaxies (BCGs) in relaxed systems. This indicates that the CWAT-01 host cluster is not relaxed, but is possibly dynamically young. We argue that such a velocity could have been induced through subcluster merger within the CWAT-01 parent cluster and/or cluster-cluster interactions. Our results strongly indicate that we are witnessing the formation of a large cluster from an assembly of multiple clusters, consistent with the hierarchical scenario of structure formation. We estimate the total mass of the final cluster to be approximately 20% of the mass of the Coma cluster.Comment: 18 pages, 13 figures; accepted for publication in ApJS, COSMOS special issue; added color figure (Fig. 13) which was previously unavailabl

Online Super-Resolution For Fibre-Bundle-Based Confocal Laser Endomicroscopy

Probe-based Confocal Laser Endomicroscopy (pCLE) produces microscopic images enabling real-time in vivo optical biopsy. However, the miniaturisation of the optical hardware, specifically the reliance on an optical fibre bundle as an imaging guide, fundamentally limits image quality by producing artefacts, noise, and relatively low contrast and resolution. The reconstruction approaches in clinical pCLE products do not fully alleviate these problems. Consequently, image quality remains a barrier that curbs the full potential of pCLE. Enhancing the image quality of pCLE in real-time remains a challenge. The research in this thesis is a response to this need. I have developed dedicated online super-resolution methods that account for the physics of the image acquisition process. These methods have the potential to replace existing reconstruction algorithms without interfering with the fibre design or the hardware of the device. In this thesis, novel processing pipelines are proposed for enhancing the image quality of pCLE. First, I explored a learning-based super-resolution method that relies on mapping from the low to the high-resolution space. Due to the lack of high-resolution pCLE, I proposed to simulate high-resolution data and use it as a ground truth model that is based on the pCLE acquisition physics. However, pCLE images are reconstructed from irregularly distributed fibre signals, and grid-based Convolutional Neural Networks are not designed to take irregular data as input. To alleviate this problem, I designed a new trainable layer that embeds Nadaraya- Watson regression. Finally, I proposed a novel blind super-resolution approach by deploying unsupervised zero-shot learning accompanied by a down-sampling kernel crafted for pCLE. I evaluated these new methods in two ways: a robust image quality assessment and a perceptual quality test assessed by clinical experts. The results demonstrate that the proposed super-resolution pipelines are superior to the current reconstruction algorithm in terms of image quality and clinician preference

Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy

Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of research on SR in endomicroscopy remains the lack of ground truth high-resolution (HR) images, commonly used for both supervised training and reference-based image quality assessment (IQA). Therefore, alternative methods, such as unsupervised SR are being explored. To address the need for non-reference image quality improvement, we designed a novel zero-shot super-resolution (ZSSR) approach that relies only on the endomicroscopy data to be processed in a self-supervised manner without the need for ground-truth HR images. We tailored the proposed pipeline to the idiosyncrasies of endomicroscopy by introducing both: a physically-motivated Voronoi downscaling kernel accounting for the endomicroscope’s irregular fibre-based sampling pattern, and realistic noise patterns. We also took advantage of video sequences to exploit a sequence of images for self-supervised zero-shot image quality improvement. We run ablation studies to assess our contribution in regards to the downscaling kernel and noise simulation. We validate our methodology on both synthetic and original data. Synthetic experiments were assessed with reference-based IQA, while our results for original images were evaluated in a user study conducted with both expert and non-expert observers. The results demonstrated superior performance in image quality of ZSSR reconstructions in comparison to the baseline method. The ZSSR is also competitive when compared to supervised single-image SR, especially being the preferred reconstruction technique by experts