Medical image enhancement using threshold decomposition driven adaptive morphological filter

One of the most common degradations in medical images is their poor contrast quality. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. In this paper, a new edge detected morphological filter is proposed to sharpen digital medical images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradientbased operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the detected edge deblurring filter improved the visibility and perceptibility of various embedded structures in digital medical images. Moreover, the performance of the proposed filter is superior to that of other sharpener-type filters

Image Transformations and Printing of Plaster Layers in Spiral Growth Manufacturing

Spiral growth manufacturing (SGM) is a high speed rapid manufacturing technique in which objects are built up, layer by layer, by simultaneously depositing, levelling and selectively consolidating thin powder layers onto a rotating build platform. The size and position of the jetted droplets are mapped by the position and greyscale level of pixels within an 8 bit greyscale bitmap image. This paper reports on the development of software in which mathematical algorithms apply geometric transformations to images in preparation for printing onto a rotating substrate. In support of this work, dimensional accuracy measurements of printed images and methods to correct radial print density variations are reported. The accuracy of printed images were found to be within ±0.2mm of their predicted size. The experimental work is briefly extended to the direct printing of plaster layers, formed by mixing two reactive ink solutions.Mechanical Engineerin

The South Dakota cooperative land use effort: A state level remote sensing demonstration project

Remote sensing technology can satisfy or make significant contributions toward satisfying many of the information needs of governmental natural resource planners and policy makers. Recognizing this potential, the South Dakota State Planning Bureau and the EROS Data Center together formulated the framework for an ongoing Land Use and Natural Resource Inventory and Information System Program. Statewide land use/land cover information is generated from LANDSAT digital data and high altitude photography. Many applications of the system are anticipated as it evolves and data are added from more conventional sources. The conceptualization, design, and implementation of the program are discussed

Improving Transfer Learning for Use in Multi-Spectral Data

Recently Nasa as well as the European Space Agency have made observational satellites images public. The main reason behind opening it to public is to foster research among university students and corporations alike. Sentinel is a program by the European Space Agency which has plans to release a series of seven satellites in lower earth orbit for observing land and sea patterns. Recently huge datasets have been made public by the Sentinel program. Many advancements have been made in the field of computer vision in the last decade. Krizhevsky, Sutskever & Hinton, 2012, revolutionized the field of image analysis by training deep neural nets and introduced the idea of using convolutions to obtain a high accuracy value on coloured image dataset of more than one million images known as Imagenet ILSVRC. Convolutional Neural Network, or CNN architecture has undergone much improvement since then. One CNN model known as Resnet or Residual Network architecture (He, Zhang, Ren & Sun, 2015) has seen mass acceptance in particular owing to it processing speed and high accuracy. Resnet is widely used for applying features it learned in Imagenet ILSVRC tasks into other image classification or object detection tasks. This concept, in the domain of deep learning, is known as Transfer learning, where a classifier is trained on a bigger more complex task and then learning is transferred to a smaller, more specific task. Transfer learning can often lead to good performance on new smaller tasks and this approach has given state of the art results in several problem domains of image classification and even in object detection (Dai, Li, He, & Sun, 2016). The real problem is that not all the problems in computer vision field belongs to regular RGB images or images consisting of only Red, Green, and Blue band set. For example, a field like medical image analysis has most of the images belonging to greyscale color space, while most of the Remote sensing images collected by satellites belong to multispectral bands of light. Transferring features learned from Imagenet ILSVRC tasks to these fields might give you higher accuracy than training from scratch, but it is a problem of fundamentally incorrect approach. Thus, there is a need to create network models that can learn from single channel or multispectral images iv and can transfer features seamlessly to similar domains with smaller datasets.This thesis presents a study in multispectral image analysis using multiple ways of feature transfer. In this study, Transfer Learning of features is done using a Resnet50 model which is trained on RGB images, and another Resnet50 model which is trained on Greyscale images alone. The dataset used to pretrain these models is a combination of images from ImageNet (Deng, Dong, Socher, Li, Li, & Fei-Fei, 2009) and Eurosat (Helber, Bischke, Dengel, & Borth. 2017). The idea behind choosing Resnet50 is that it has been doing really well in image processing and transfer learning and has outperformed all the other traditional techniques, while still not being computationally prohibitive to train in the context of this work. An attempt is made to classify different land-cover classes in multispectral images taken up by Sentinel 2A satellite. The dataset used here has a key challenge of a smaller number of samples, which means a CNN classifier trained from scratch on these small number of samples will be highly inaccurate and overfitted. This thesis focuses on improving the accuracies of this classifier using transfer learning, and the performance is measured after fine-tuning the baseline above Resnet50 model. The experiment results show that fine-tuning the Greyscale or single channel based Resnet50 model helps in improving the accuracy a bit more than using a RGB trained Resnet50 model for fine tuning, though it haven\u27t achieved great result due to the limitation of lesser computational power and smaller dataset to train a large computer vision network like Resnet50. This work is a contribution towards improving classification in domain of multispectral images usually taken up by satellites. There is no baseline model available right now, which can be used to transfer features to single or multispectral domains like the rest of RGB image field has. The contribution of this work is to build such a classifier for multispectral domain and to extend the state of the art in such computer vision domains

Quality grading of painted slates using texture analysis

This paper details the development of an automated vision-based solution for identification of paint and substrate defects on painted slates. The developed vision system consists of two major components. The first component of the system addresses issues including the mechanical implementation and interfacing the inspection system with the sensing and optical equipment. The second component involves the development of an image processing algorithm that is able to identify the visual defects present on the slate surface. The process of imaging the slate proved to be very challenging as the slate surface is darkly coloured and presents depth non-uniformities. Hence, a key issue for this inspection system was to devise an adequate illumination system that was able to accommodate challenges including the slates’ surface depth non-uniformities and vibrations generated by the conveying system. The visual defects are detected using a novel texture analysis solution where the greyscale (tonal characteristics) and texture information are embedded in a composite model. The developed inspection system was tested for robustness and experimental results are presented

Microalgal biomass quantification from the non-invasive technique of image processing through red-green-blue (RGB) analysis

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGContinuous monitoring of biomass concentration in microalgae cultures is essential and one of the most important parameters to measure in this field. This study aims at digital image processing in RGB and greyscale models, being a simple and low-cost method for cell estimation. Images obtained from different photobioreactors with wastewater and at different conditions for the cultivation of Chlorella vulgaris were analyzed. The results suggested that this technique is very effective under controlled lighting conditions, in contrast to photobioreactors placed outdoors and of different design, presenting a lower linearity. The accuracy of the method could be improved with a high-quality charge-coupled device (CCD) camera. The development of efficient methods to assess biomass concentration is an important and necessary step towards large-scale microalgae cultivation. The colour analysis technique has a great potential to meet the needs of monitoring cultures in a cost-effective and automated way using simple and cheap instruments