An End-to-End License Plate Localization and Recognition System

An end-to-end license plate recognition (LPR) system is proposed. It is composed of pre-processing, detection, segmentation and character recognition to find and recognize plates from camera based still images. The system utilizes connected component (CC) properties to quickly extract the license plate region. A novel two-stage CC filtering is utilized to address both shape and spatial relationship information to produce high precision and recall values for detection. Floating peak and valleys (FPV) of projection profiles are used to cut the license plates into individual characters. A turning function based method is proposed to recognize each character quickly and accurately. It is further accelerated using curvature histogram based support vector machine (SVM). The INFTY dataset is used to train the recognition system. And MediaLab license plate dataset is used for testing. The proposed system achieved 89.45% F-measure for detection and 87.33% accuracy for overall recognition rate which is comparable to current state-of-the-art systems

Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images

Clinical analysis and medical diagnosis of diverse diseases adopt medical imaging techniques to empower specialists to perform their tasks by visualizing internal body organs and tissues for classifying and treating diseases at an early stage. Content-Based Image Retrieval (CBIR) systems are a set of computer vision techniques to retrieve similar images from a large database based on proper image representations. Particularly in radiology and histopathology, CBIR is a promising approach to effectively screen, understand, and retrieve images with similar level of semantic descriptions from a database of previously diagnosed cases to provide physicians with reliable assistance for diagnosis, treatment planning and research. Over the past decade, the development of CBIR systems in medical imaging has expedited due to the increase in digitized modalities, an increase in computational efficiency (e.g., availability of GPUs), and progress in algorithm development in computer vision and artificial intelligence. Hence, medical specialists may use CBIR prototypes to query similar cases from a large image database based solely on the image content (and no text). Understanding the semantics of an image requires an expressive descriptor that has the ability to capture and to represent unique and invariant features of an image. Radon transform, one of the oldest techniques widely used in medical imaging, can capture the shape of organs in form of a one-dimensional histogram by projecting parallel rays through a two-dimensional object of concern at a specific angle. In this work, the Radon transform is re-designed to (i) extract features and (ii) generate a descriptor for content-based retrieval of medical images. Radon transform is applied to feed a deep neural network instead of raw images in order to improve the generalization of the network. Specifically, the framework is composed of providing Radon projections of an image to a deep autoencoder, from which the deepest layer is isolated and fed into a multi-layer perceptron for classification. This approach enables the network to (a) train much faster as the Radon projections are computationally inexpensive compared to raw input images, and (b) perform more accurately as Radon projections can make more pronounced and salient features to the network compared to raw images. This framework is validated on a publicly available radiography data set called "Image Retrieval in Medical Applications" (IRMA), consisting of 12,677 train and 1,733 test images, for which an classification accuracy of approximately 82% is achieved, outperforming all autoencoder strategies reported on the Image Retrieval in Medical Applications (IRMA) dataset. The classification accuracy is calculated by dividing the total IRMA error, a calculation outlined by the authors of the data set, with the total number of test images. Finally, a compact handcrafted image descriptor based on Radon transform was designed in this work that is called "Forming Local Intersections of Projections" (FLIP). The FLIP descriptor has been designed, through numerous experiments, for representing histopathology images. The FLIP descriptor is based on Radon transform wherein parallel projections are applied in a local 3x3 neighborhoods with 2 pixel overlap of gray-level images (staining of histopathology images is ignored). Using four equidistant projection directions in each window, the characteristics of the neighborhood is quantified by taking an element-wise minimum between each adjacent projection in each window. Thereafter, the FLIP histogram (descriptor) for each image is constructed. A multi-resolution FLIP (mFLIP) scheme is also proposed which is observed to outperform many state-of-the-art methods, among others deep features, when applied on the histopathology data set KIMIA Path24. Experiments show a total classification accuracy of approximately 72% using SVM classification, which surpasses the current benchmark of approximately 66% on the KIMIA Path24 data set

Super-resolução em vídeos de baixa qualidade para aplicações forenses, de vigilância e móveis

Orientadores: Siome Klein Goldenstein, Anderson de Rezende RochaTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Algoritmos de super-resolução (SR) são métodos para obter um aumento da resolução de imagens compostas por pixels. Na super-resolução por múltiplas imagens, um conjunto de imagens de baixa resolução de uma cena é combinado para construir uma imagem de resolução superior. Super-resolução é uma solução barata para superar as limitações dos sistemas de aquisição de imagens, e pode ser útil em diversos casos em que o dispositivo não pode ser melhorado ou substituído - mas em que é possível obter diversas capturas da mesma cena. Neste trabalho, é explorada a super-resolução por múltiplas imagens para imagens naturais, em cenários nos quais é possível obter diversas imagens de uma cena. São propostas cinco variações de um método que explora propriedades geométricas de múltiplas imagens de baixa resolução para combiná-las em uma imagem de resolução superior; duas variações de um método que combina técnicas de inpainting e super-resolução; e mais três variações de um método que utiliza filtros adaptativos e regularização para resolver um problema de mínimos quadrados. Super-resolução por múltiplas imagens é possível quando existe movimento e informações não redundantes entre as imagens de baixa resolução. Entretanto, combiná-las em uma imagem de resolução superior pode não ser computacionalmente viável por técnicas complexas de super-resolução. A primeira aplicação dos métodos propostos é para um conjunto de imagens capturadas pelos dispositivos móveis mais recentes. Este tipo de ambiente requer algoritmos eficazes que sejam executados rapidamente e utilizando baixo consumo de memória. A segunda aplicação é na Ciência Forense. Câmeras de vigilância espalhadas pelas cidades poderiam fornecer dicas importantes para identificar um suspeito, por exemplo, em uma cena de crime. Entretanto, o reconhecimento dos caracteres de placas veiculares é especialmente difícil quando a resolução das imagens é baixa. Por isso, este trabalho também propõe um arcabouço que realiza a super-resolução de placas veiculares em vídeos reais de vigilância, capturados por câmeras de baixa qualidade e não projetadas especificamente para esta tarefa, ajudando o especialista forense a compreender um evento de interesse. O arcabouço realiza todas as etapas necessárias para rastrear, alinhar, reconstruir e reconhecer automaticamente os caracteres de uma placa suspeita. O usuário recebe, como saída, a imagem de alta resolução reconstruída, mais rica em detalhes, e também a sequência de caracteres reconhecida automaticamente nesta imagem. São apresentadas validações quantitativas e qualitativas dos algoritmos propostos e de suas aplicações. Os experimentos mostram, por exemplo, que é possível aumentar o número de caracteres reconhecidos corretamente, colocando o arcabouço proposto como uma ferramenta importante para fornecer aos peritos uma solução para o reconhecimento de placas veiculares sob condições adversas de aquisição. Por fim, também é sugerido o número mínimo de imagens a ser utilizada como entrada em cada aplicaçãoAbstract: Super-resolution (SR) algorithms are methods for achieving high-resolution (HR) enlargements of pixel-based images. In multi-frame super resolution, a set of low-resolution (LR) images of a scene are combined to construct an image with higher resolution. Super resolution is an inexpensive solution to overcome the limitations of image acquisition hardware systems, and can be useful in several cases in which the device cannot be upgraded or replaced, but multiple frames of the same scene can be obtained. In this work, we explore SR possibilities for natural images, in scenarios wherein we have multiple frames of a same scene. We design and develop five variations of an algorithm which rely on exploring geometric properties in order to combine pixels from LR observations into an HR grid; two variations of a method that combines inpainting techniques to multi-frame super resolution; and three variations of an algorithm that uses adaptive filtering and Tikhonov regularization to solve a least-square problem. Multi-frame super resolution is possible when there is motion and non-redundant information from LR observations. However, combining a large number of frames into a higher resolution image may not be computationally feasible by complex super-resolution techniques. The first application of the proposed methods is in consumer-grade photography with a setup in which several low-resolution images gathered by recent mobile devices can be combined to create a much higher resolution image. Such always-on low-power environment requires effective high-performance algorithms, that run fastly and with a low-memory footprint. The second application is in Digital Forensic, with a setup in which low-quality surveillance cameras throughout the cities could provide important cues to identify a suspect vehicle, for example, in a crime scene. However, license-plate recognition is especially difficult under poor image resolutions. Hence, we design and develop a novel, free and open-source framework underpinned by SR and Automatic License-Plate Recognition (ALPR) techniques to identify license-plate characters in low-quality real-world traffic videos, captured by cameras not designed for the ALPR task, aiding forensic analysts in understanding an event of interest. The framework handles the necessary conditions to identify a target license plate, using a novel methodology to locate, track, align, super resolve, and recognize its alphanumerics. The user receives as outputs the rectified and super-resolved license-plate, richer in details, and also the sequence of license-plates characters that have been automatically recognized in the super-resolved image. We present quantitative and qualitative validations of the proposed algorithms and its applications. Our experiments show, for example, that SR can increase the number of correctly recognized characters posing the framework as an important step toward providing forensic experts and practitioners with a solution for the license-plate recognition problem under difficult acquisition conditions. Finally, we also suggest a minimum number of images to use as input in each applicationDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação1197478,146886153996/3-2015CAPESCNP

Current Air Quality Issues

Air pollution is thus far one of the key environmental issues in urban areas. Comprehensive air quality plans are required to manage air pollution for a particular area. Consequently, air should be continuously sampled, monitored, and modeled to examine different action plans. Reviews and research papers describe air pollution in five main contexts: Monitoring, Modeling, Risk Assessment, Health, and Indoor Air Pollution. The book is recommended to experts interested in health and air pollution issues

Radiobiology Textbook:Space Radiobiology

The study of the biologic effects of space radiation is considered a “hot topic,” with increased interest in the past years. In this chapter, the unique characteristics of the space radiation environment will be covered, from their history, characterization, and biological effects to the research that has been and is being conducted in the field. After a short introduction, you will learn the origin and characterization of the different types of space radiation and the use of mathematical models for the prediction of the radiation doses during different mission scenarios and estimate the biological risks due to this exposure. Following this, the acute, chronic, and late effects of radiation exposure in the human body are discussed before going into the detailed biomolecular changes affecting cells and tissues, and in which ways they differ from other types of radiation exposure. The next sections of this chapter are dedicated to the vast research that has been developed through the years concerning space radiation biology, from small animals to plant models and 3D cell cultures, the use of extremophiles in the study of radiation resistance mechanisms to the importance of ground-based irradiation facilities to simulate and study the space environment

African Handbook of Climate Change Adaptation

This open access book discusses current thinking and presents the main issues and challenges associated with climate change in Africa. It introduces evidences from studies and projects which show how climate change adaptation is being - and may continue to be successfully implemented in African countries. Thanks to its scope and wide range of themes surrounding climate change, the ambition is that this book will be a lead publication on the topic, which may be regularly updated and hence capture further works. Climate change is a major global challenge. However, some geographical regions are more severly affected than others. One of these regions is the African continent. Due to a combination of unfavourable socio-economic and meteorological conditions, African countries are particularly vulnerable to climate change and its impacts. The recently released IPCC special report "Global Warming of 1.5º C" outlines the fact that keeping global warming by the level of 1.5º C is possible, but also suggested that an increase by 2º C could lead to crises with crops (agriculture fed by rain could drop by 50% in some African countries by 2020) and livestock production, could damage water supplies and pose an additonal threat to coastal areas. The 5th Assessment Report produced by IPCC predicts that wheat may disappear from Africa by 2080, and that maize— a staple—will fall significantly in southern Africa. Also, arid and semi-arid lands are likely to increase by up to 8%, with severe ramifications for livelihoods, poverty eradication and meeting the SDGs. Pursuing appropriate adaptation strategies is thus vital, in order to address the current and future challenges posed by a changing climate. It is against this background that the "African Handbook of Climate Change Adaptation" is being published. It contains papers prepared by scholars, representatives from social movements, practitioners and members of governmental agencies, undertaking research and/or executing climate change projects in Africa, and working with communities across the African continent. Encompassing over 100 contribtions from across Africa, it is the most comprehensive publication on climate change adaptation in Africa ever produced

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Triple Helix as a Strategic Tool to Fast-Track Climate Change Adaptation in Rural Kenya: Case Study of Marsabit County

AbstractThe lack of affordable, clean, and reliable energy in Africa's rural areas forces people to resort to poor quality energy source, which is detrimental to the people's health and prevents the economic development of communities. Moreover, access to safe water and food security are concerns closely linked to health issues and children malnourishment. Recent climate change due to global warming has worsened the already critical situation.Electricity is well known to be an enabler of development as it allows the use of modern devices thus enabling the development of not only income-generating activities but also water pumping and food processing and conservation that can promote socioeconomic growth. However, all of this is difficult to achieve due to the lack of investors, local skills, awareness by the community, and often also government regulations.All the above mentioned barriers to the uptake of electricity in rural Kenya could be solved by the coordinated effort of government, private sector, and academia, also referred to as Triple Helix, in which each entity may partially take the other's role. This chapter discretizes the above and shows how a specific county (Marsabit) has benefited from this triple intervention. Existing government policies and actions and programs led by nongovernmental organizations (NGOs) and international agencies are reviewed, highlighting the current interconnection and gaps in promoting integrated actions toward climate change adaptation and energy access