Optimum non linear binary image restoration through linear grey-scale operations

Non-linear image processing operators give excellent results in a number of image processing tasks such as restoration and object recognition. However they are frequently excluded from use in solutions because the system designer does not wish to introduce additional hardware or algorithms and because their design can appear to be ad hoc. In practice the median filter is often used though it is rarely optimal. This paper explains how various non-linear image processing operators may be implemented on a basic linear image processing system using only convolution and thresholding operations. The paper is aimed at image processing system developers wishing to include some non-linear processing operators without introducing additional system capabilities such as extra hardware components or software toolboxes. It may also be of benefit to the interested reader wishing to learn more about non-linear operators and alternative methods of design and implementation. The non-linear tools include various components of mathematical morphology, median and weighted median operators and various order statistic filters. As well as describing novel algorithms for implementation within a linear system the paper also explains how the optimum filter parameters may be estimated for a given image processing task. This novel approach is based on the weight monotonic property and is a direct rather than iterated method

Driving Assistance System with Lane Change Detection

In this study, a simple technology for a self-driving system called “driver assistance system” is developed based on embedded image identification. The system consists of a camera, a Raspberry Pi board, and OpenCV. The camera is used to capture lane images, and the image noise is overcome through color space conversion, grayscale, Otsu thresholding, binarization, erosion, and dilation. Subsequently, two horizontal lines parallel to the X-axis with a fixed range and interval are used to detect left and right lane lines. The intersection points between the left and right lane lines and the two horizontal lines can be obtained, and can be used to calculate the slopes of the left and right lanes. Finally, the slope change of the left and right lanes and the offset of the lane intersection are determined to detect the deviation. When the angle of lanes changes drastically, the driver receives a deviation warning. The results of this study suggest that the proposed algorithm is 1.96 times faster than the conventional algorithm

Intelligent computational system for colony-forming-unit enumeration and differentiation

Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in a wide range of fields. The enumeration processes of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular pathologies require an urgent and accurate response for the therapy to be effective. To reduce time limitations and perhaps discrepancies, this work introduces an intelligent image processing software capable of automatically quantifying the number of Colony Forming Units (CFUs) in Petri-plates. This rapid enumeration enables the technician to provide an expeditious assessment of the microbial load. Moreover, an auxiliary system is able to differentiate among colony images of Echerichia coli, Pseudomonas aeruginosa and Staphylococcus aureus via Machine Learning, based on a Convolutional Neural Network in a process of cross-validation. For testing and validation of the system, the three bacterial groups were cultured, and a significant labeled database was created, exercising suited microbiological laboratory methodologies and subsequent image acquisition. The system demonstrated acceptable accuracy measures; the mean values of precision, recall and F-measure were 95%, 95% and 0.95, for E. coli, 91%, 91% and 0.90 for P. aeruginosa, and 84%, 86% and 0.85 for S. aureus. The adopted deep learning approach accomplished satisfactory results, manifesting 90.31% of accuracy. Ultimately, evidence related to the time-saving potential of the system was achieved; the time spent on the quantification of plates with a high number of colonies might be reduced to a half and occasionally to a third.A análise quantitativa de microrganismos é uma ferramenta essencial na aferição da segurança e qualidade numa ampla variedade de áreas. O processo de enumeração de microrganismos viáveis através das técnicas de cultura tradicionais é económica e metodologicamente adequado, conferindo lhe alta aplicabilidade a nível mundial. Contudo, a contagem manual pode ser morosa, laboriosa e imprecisa. Em adição, certas patologias requerem uma urgente e precisa resposta de modo a que a terapia seja eficaz. De forma a reduzir limitações e discrepâncias, este trabalho apresenta um software inteligente de processamento de imagem capaz de quantificar automaticamente o número de Unidades Formadoras de Colónias (UFCs) em placas de Petri. Esta rápida enumeração, possibilita ao técnico uma expedita avaliação da carga microbiana. Adicionalmente, um sistema auxiliar tem a capacidade de diferenciar imagens de colónias de Echerichia coli, Pseudomonas aeruginosa e Staphylococcus aureus recorrendo a Machine Learning, através de uma Rede Neuronal Convolucional num processo de validação cruzada. Para testar e validar o sistema, os três grupos bacterianos foram cultivados e uma significativa base de dados foi criada, recorrendo às adequadas metodologias microbiológicas laboratoriais e subsequente aquisição de imagens. O sistema demonstrou medidas de precisão aceitáveis; os valores médios de precisão, recall e F-measure, foram 95%, 95% e 0.95, para E. coli, 91%, 91% e 0.90 para P. aeruginosa, e 84%, 86% e 0.85 para S. aureus. A abordagem deep learning obteve resultados satisfatórios, manifestando 90.31% de precisão. O sistema revelou potencial em economizar tempo; a duração de tarefas afetas à quantificação de placas com elevado número de colónias poderá ser reduzida para metade e ocasionalmente para um terço

Statistical Techniques and Artificial Neural Networks for Image Analysis

The main topic of this PhD thesis is image analysis. The subject was investigated from different perspectives, starting from different image types and with different goals. At the beginning x-ray hazelnuts images were analyzed. The target of this analysis was to determine whether a hazelnut was good or not. In order to do this an Artificial Neural Network was used, whose features were statistical variables. At a later stage a SVM was implemented to try to get better results. The second kind of images were still x-ray ones; they were, however, images coming from a PCB productive process. What we were asked to do was to highlight the air bubbles trapped into a solder joint (particularly those inside the thermal pads). In this case filters and morphological operations were used. The third case were ulcers photographs: the goal of the collaboration with SIF (Società Italiana di Flebologia, Italian society of phlebology) is to give doctors a way to evaluate ulcers remotely, in order to customize the treatments according to how the healing behaves. A small digression was the development of a small and cheap Arduino-based robot for an educational laboratory (Xkè?, in collaboration with prof. Angelo Raffaele Meo from DAUIN, Politecnico di Torino). This should have been the first step towards the development of an evolved robot for agricultural purposes, but the project didn’t start

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

Digital Image Processing Applications

Digital image processing can refer to a wide variety of techniques, concepts, and applications of different types of processing for different purposes. This book provides examples of digital image processing applications and presents recent research on processing concepts and techniques. Chapters cover such topics as image processing in medical physics, binarization, video processing, and more

Automated Quality Assessment of Printed Objects Using Subjective and Objective Methods Based on Imaging and Machine Learning Techniques.

Estimating the perceived quality of printed patterns is a complex task as quality is subjective. A study was conducted to evaluate how accurately a machine learning method can predict human judgment about printed pattern quality. The project was executed in two phases: a subjective test to evaluate the printed pattern quality and development of the machine learning classifier-based automated objective model. In the subjective experiment, human observers ranked overall visual quality. Object quality was compared based on a normalized scoring scale. There was a high correlation between subjective evaluation ratings of objects with similar defects. Observers found the contrast of the outer edge of the printed pattern to be the best distinguishing feature for determining the quality of object. In the second phase, the contrast of the outer print pattern was extracted by flat-fielding, cropping, segmentation, unwrapping and an affine transformation. Standard deviation and root mean square (RMS) metrics of the processed outer ring were selected as feature vectors to a Support Vector Machine classifier, which was then run with optimized parameters. The final objective model had an accuracy of 83%. The RMS metric was found to be more effective for object quality identification than the standard deviation. There was no appreciable difference in using RGB data of the pattern as a whole versus using red, green and blue separately in terms of classification accuracy. Although contrast of the printed patterns was found to be an important feature, other features may improve the prediction accuracy of the model. In addition, advanced deep learning techniques and larger subjective datasets may improve the accuracy of the current objective model