Bispectral image fusion using multi-resolution transform for enhanced target detection in low ambient light conditions

Performing target detection/identification task using only visible spectrum information becomes extremely difficult during low ambient light conditions. Visible spectrum information consists of information available in the range of 400-700 nm wavelength. However, infrared spectrum carries information beyond 800 nm. To overcome the difficulty of target detection by human operator during the task of surveillance, fusion of visible and infrared spectral image information has been proposed. The image fusion has been performed using multi resolution transform based curvelet technique. The use of curvelet transform has been done because of its high directional sensitivity and reconstruction quality. Curvelet transform has been used to decompose source images to obtain coefficients at coarse, intermediate and fine scale. These coefficients have been fused as per respective decomposition level, followed by reconstruction of fused image using inverse curvelet transform. Bispectral fused image inherits scene information as well as target information both from visible and infrared spectrum images respectively. The proposed image fusion output images are visually and statistically compared with other fusion method outputs. The fused image obtained using proposed fusion method in comparison to other fusion results show clear background details, high target distinctiveness, better reconstruction and lesser clutter

Investigating the Quality of UAV-Based Images for the Thermographic Analysis of Buildings

Thermography for building audits is commonly carried out by means of terrestrial recording processes with static cameras. The implementation of drones to automatically acquire images from various perspectives can speed up and facilitate the procedure but requires higher recording distances, utilizes changing recording angles and has to contend with the effects of movement during image capture. This study investigates the influence of different drone settings on the quality of thermographic images for building audits in comparison to ground-based acquisition. To this end, several buildings are photographically captured via unmanned aerial vehicle and classical terrestrial means to generate a dataset of 968 images in total. These are analyzed and compared according to five quality criteria that are explicitly chosen for this study to establish best-practice rules for thermal image acquisition. We discover that flight speeds of up to 5 m/s have no visible effects on the image quality. The combination of smaller distances (22 m above a building) and a 45° camera angle are found to allow for both the qualitative and quantitative analysis of rooftops as well as a qualitative screening of building façades. Greater distances of 42 m between camera and building may expedite the acquisition procedure for larger-scaled district coverage but cannot be relied upon for thermal analyses beyond qualitative studies

Using Cone Beam Computed Tomography for radiological assessment beyond dento-maxillofacial imaging: a review of the clinical applications in other anatomical districts

Background: Cone Beam Computed Tomography (CBCT) represents the optimal imaging solution for the evaluation of the maxillofacial and dental area when quantitative geometric and volumetric accuracy is necessary (e.g., in implantology and orthodontics). Moreover, in recent years, this technique has given excellent results for the imaging of lower and upper extremities. Therefore, significant interest has been increased in using CBCT to investigate larger and non-traditional anatomical districts. Objective: The purpose of this work is to review the scientific literature in Pubmed and Scopus on CBCT application beyond head districts by paying attention to image quality and radiological doses. Method: The search for keywords was conducted in Pubmed and Scopus databases with no back-date restriction. Papers on applications of CBCT to head were excluded from the present work. From each considered paper, parameters related to image quality and radiological dose were extracted. An overall qualitative evaluation of the results extracted from each issue was done by comparing the conclusive remarks of each author regarding doses and image quality. PRISMA statements were followed during this process. Results: The review retrieved 97 issues from 83 extracted papers; 46 issues presented a comparison between CBCT and Multi-Detector Computed Tomography (MDCT), and 51 reviewed only CBCT. The radiological doses given to the patient with CBCT were considered acceptable in 91% of cases, and the final image quality was found in 99%. Conclusion: CBCT represents a promising technology not only for imaging of the head and upper and lower extremities but for all the orthopedic districts. Moreover, the application of CBCT derived from C-arms (without the possibility of a 360 ° rotation range) during invasive investigations demonstrates the feasibility of this technique for non-standard anatomical areas, from soft tissues to vascular beds, despite the limits due to the incomplete rotation of the tube

Medical Image Registration

Image registration of X-ray images is used to correctly align the patient before the cancer tumour is treated with sets of high dose X-ray radiation. Before the first treatment, a simulated X-ray image is taken showing the best possible localisation of the tumour. From then on, after each of the radiation treatments of the tumour, a low dose of the X-ray theurapeutic radiation is used to take an image of the tumour and its surroundings. This so called portal X-ray image is compared to the simulated image to decide whether the patient should be moved before the next treatment in order to improve the accuracy of the theurapeutic beam and, hence, prevent the high dose radiation from hitting other surrounding tissues close to the tumour. Due to differences in quality of the X-ray plates used for the recording of the simulated and the portal image, the images differ a lot in contrasts, noise level, and possibly even scale and size. The diverse quality of the images is the main problem of the image registration task. At present, the comparison of the X-ray images is carried out by hand. A software for automating the process would decrease the influence of human intuition on the treatment, decrease the treatment time, and enable less qualified personnel to carry out the treatment. Much research has already been done within the field of image registration, many with various results. The preceding pilot study suggests that a deeper study, that would go beyond the scope of this project, is needed for the task. This report strives for presenting fields of possible approaches to the problem and tests performed within these fields. Hopefully, the report will give some guidance on directions that a future research could take

Application of Analogical Reasoning for Use in Visual Knowledge Extraction

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global Vectors (GloVe), in the proposed, Image Recognition Through Analogical Reasoning Algorithm (IRTARA). Given images outside of the CNN’s training data, IRTARA produces contextual information by leveraging semantic information from GloVe. IRTARA’s quality of results is measured by definition, AR, and human factors evaluation methods, which saw consistency at the extreme ends. The research shows the potential for AR to facilitate more a human-like AI through its ability to understand concepts beyond its foundational knowledge in both a textual and visual problem space