Quantum-inspired computational imaging

Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

Automated Image Interpretation for Science Autonomy in Robotic Planetary Exploration

Advances in the capabilities of robotic planetary exploration missions have increased the wealth of scientific data they produce, presenting challenges for mission science and operations imposed by the limits of interplanetary radio communications. These data budget pressures can be relieved by increased robotic autonomy, both for onboard operations tasks and for decision- making in response to science data. This thesis presents new techniques in automated image interpretation for natural scenes of relevance to planetary science and exploration, and elaborates autonomy scenarios under which they could be used to extend the reach and performance of exploration missions on planetary surfaces. Two computer vision techniques are presented. The first is an algorithm for autonomous classification and segmentation of geological scenes, allowing a photograph of a rock outcrop to be automatically divided into regions by rock type. This important task, currently performed by specialists on Earth, is a prerequisite to decisions about instrument pointing, data triage, and event-driven operations. The approach uses a novel technique to seek distinct visual regions in outcrop photographs. It first generates a feature space by extracting multiple types of visual information from the image. Then, in a training step using labeled exemplar scenes, it applies Mahalanobis distance metric learning (in particular, Multiclass Linear Discriminant Analysis) to discover the linear transformation of the feature space which best separates the geological classes. With the learned representation applied, a vector clustering technique is then used to segment new scenes. The second technique interrogates sequences of images of the sky to extract, from the motion of clouds, the wind vector at the condensation level — a measurement not normally available for Mars. To account for the deformation of clouds and the ephemerality of their fine-scale features, a template-matching technique (normalized cross-correlation) is used to mutually register images and compute the clouds’ motion. Both techniques are tested successfully on imagery from a variety of relevant analogue environments on Earth, and on data returned from missions to the planet Mars. For both, scenarios are elaborated for their use in autonomous science data interpretation, and to thereby automate certain steps in the process of robotic exploration

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Classification and Segmentation of Galactic Structuresin Large Multi-spectral Images

Extensive and exhaustive cataloguing of astronomical objects is imperative for studies seeking to understand mechanisms which drive the universe. Such cataloguing tasks can be tedious, time consuming and demand a high level of domain specific knowledge. Past astronomical imaging surveys have been catalogued through mostly manual effort. Immi-nent imaging surveys, however, will produce a magnitude of data that cannot be feasibly processed through manual cataloguing. Furthermore, these surveys will capture objects fainter than the night sky, termed low surface brightness objects, and at unprecedented spatial resolution owing to advancements in astronomical imaging. In this thesis, we in-vestigate the use of deep learning to automate cataloguing processes, such as detection, classification and segmentation of objects. A common theme throughout this work is the adaptation of machine learning methods to challenges specific to the domain of low surface brightness imaging.We begin with creating an annotated dataset of structures in low surface brightness images. To facilitate supervised learning in neural networks, a dataset comprised of input and corresponding ground truth target labels is required. An online tool is presented, allowing astronomers to classify and draw over objects in large multi-spectral images. A dataset produced using the tool is then detailed, containing 227 low surface brightness images from the MATLAS survey and labels made by four annotators. We then present a method for synthesising images of galactic cirrus which appear similar to MATLAS images, allowing pretraining of neural networks.A method for integrating sensitivity to orientation in convolutional neural networks is then presented. Objects in astronomical images can present in any given orientation, and thus the ability for neural networks to handle rotations is desirable. We modify con-volutional filters with sets of Gabor filters with different orientations. These orientations are learned alongside network parameters during backpropagation, allowing exact optimal orientations to be captured. The method is validated extensively on multiple datasets and use cases.We propose an attention based neural network architecture to process global contami-nants in large images. Performing analysis of low surface brightness images requires plenty of contextual information and local textual patterns. As a result, a network for processing low surface brightness images should ideally be able to accommodate large high resolu-tion images without compromising on either local or global features. We utilise attention to capture long range dependencies, and propose an efficient attention operator which significantly reduces computational cost, allowing the input of large images. We also use Gabor filters to build an attention mechanism to better capture long range orientational patterns. These techniques are validated on the task of cirrus segmentation in MAT-LAS images, and cloud segmentation on the SWIMSEG database, where state of the art performance is achieved.Following, cirrus segmentation in MATLAS images is further investigated, and a com-prehensive study is performed on the task. We discuss challenges associated with cirrus segmentation and low surface brightness images in general, and present several tech-niques to accommodate them. A novel loss function is proposed to facilitate training of the segmentation model on probabilistic targets. Results are presented on the annotated MATLAS images, with extensive ablation studies and a final benchmark to test the limits of the detailed segmentation pipeline.Finally, we develop a pipeline for multi-class segmentation of galactic structures and surrounding contaminants. Techniques of previous chapters are combined with a popu-lar instance segmentation architecture to create a neural network capable of segmenting localised objects and extended amorphous regions. The process of data preparation for training instance segmentation models is thoroughly detailed. The method is tested on segmentation of five object classes in MATLAS images. We find that unifying the tasks of galactic structure segmentation and contaminant segmentation improves model perfor-mance in comparison to isolating each task

Aprendizado profundo em triagem de melanoma

Orientadores: Eduardo Alves do Valle Junior, Lin Tzy LiDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: De todos os cânceres de pele, melanoma representa apenas 1% dos casos, mas 75% das mortes. O prognóstico do melanoma é bom quando detectado cedo, mas deteriora rápido ao longo que a doença progride. Ferramentas automatizadas podem prover triagem mais rápida, ajudando médicos a focar em pacientes ou lesões de risco. As características da doença --- raridade, letalidade, rápida progressão, e diagnóstico sutil --- fazem a triagem de melanoma automática particularmente desafiadora. O objetivo deste trabalho é melhor compreender como Deep Learning pode ser utilizado --- mais precisamente, Redes Neurais Convolucionais --- para classificar corretamente imagens de lesões de pele. Para isso, este trabalho está dividido em duas linhas de pesquisa. Primeiro, o estudo está focado na transferibilidade de características das redes CNN pré-treinadas. O objetivo principal desse tópico é estudar como as características transferidas se comportam em diferentes esquemas, com o objetivo de gerar melhores características para a camada de decisão. Em um segundo tópico, esse estudo incidirá na melhoria das métricas de classificação, que é o objetivo geral. Sobre a transferibilidade das características, foram realizados experimentos para analisar a forma como os diferentes esquemas de transferência afetariam a Área sob a Curva ROC (AUC): treinar uma CNN a partir do zero; transferir o conhecimento de uma CNN pré-treinada com imagens gerais ou específicas; realizar uma transferência dupla, que é uma sequência de treinamento onde em um primeiro momento a rede é treinada com imagens gerais, em um segundo momento com as imagens específicas, e, finalmente, em um terceiro momento com as imagens de melanoma. A partir desses experimentos, aprendemos que a transferência de aprendizagem é uma boa prática, assim como é o ajuste fino. Os resultados também sugerem que modelos mais profundos conduzem a melhores resultados. Hipotetizamos que a transferência de aprendizagem de uma tarefa relacionada sob ponto de vista médico (no caso, a partir de um dataset de imagens de retinopatia) levaria a melhores resultados, especialmente no esquema de transferência dupla, mas os resultados mostraram o oposto, sugerindo que a adaptação de tarefas muito específicas representa desafios específicos. Sobre a melhoria das métricas, discute-se o pipeline vencedor utilizado no International Skin Imaging Collaboration (ISIC) Challenge 2017, alcançando o estado da arte na classificação de melanoma com 87.4% AUC. A solução é baseada em stacking/meta learning dos modelos Inception v4 e Resnet101, realizando fine tuning enquanto executa a aumentação de dados nos conjuntos de treino e teste. Também comparamos diferentes técnicas de segmentação --- multiplicação elemento a elemento da imagem da lesão de pele e sua máscara de segmentação, e utilizar a máscara de segmentação como quarto canal --- com uma rede treinada sem segmentação. A rede sem segmentação é a que obteve melhor desemepnho (96.0% AUC) contra a máscara de segmentação como quarto canal (94.5% AUC). Nós também disponibilizamos uma implementação de referência reprodutível com todo o código desenvolvido para as contribuições desta dissertaçãoAbstract: From all skin cancers, melanoma represents just 1% of cases, but 75% of deaths. Melanoma¿s prognosis is good when detected early, but deteriorates fast as the disease progresses. Automated tools may play an essential role in providing timely screening, helping doctors focus on patients or lesions at risk. However, due to the disease¿s characteristics --- rarity, lethality, fast progression, and diagnosis subtlety --- automated screening for melanoma is particularly challenging. The objective of this work is to understand better how can we use Deep Learning --- more precisely, Convolutional Neural Networks --- to correctly classify images of skin lesions. This work is divided into two lines of investigation to achieve the objective. First, the study is focused on the transferability of features from pretrained CNN networks. The primary objective of that thread is to study how the transferred features behave in different schemas, aiming at generating better features to the classifier layer. Second, this study will also improve the classification metrics, which is the overall objective of this line of research. On the transferability of features, we performed experiments to analyze how different transfer schemas would impact the overall Area Under the ROC Curve (AUC) training a CNN from scratch; transferring from pretrained CNN on general and specific image databases; performing a double transfer, in a sequence from general to specific and finally melanoma databases. From those experiments, we learned that transfer learning is a good practice, as is fine tuning. The results also suggest that deeper models lead to better results. We expected that transfer learning from a related task (in the case, from a retinopathy image database) would lead to better outcomes, but results showed the opposite, suggesting that adaptation from particular tasks poses specific challenges. On the improvement of metrics, we discussed the winner pipeline used in the International Skin Imaging Collaboration (ISIC) Challenge 2017, reaching state-of-the-art results on melanoma classification with 87.4% AUC. The solution is based on the stacking/meta-learning from Inception v4 and Resnet101 models, fine tuning them while performing data augmentation on the train and test sets. Also, we compare different segmentation techniques - elementwise multiplication of the skin lesion image and its mask, and input the segmentation mask as a fourth channel - with a network trained without segmentation. The network with no segmentation is the one who performs better (96.0% AUC) against segmentation mask as a fourth channel (94.5% AUC). We made available a reproducible reference implementation with all developed source code for the contributions of this thesisMestradoEngenharia de ComputaçãoMestre em Engenharia Elétrica133530/2016-7CNP

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science