Implementation of filters for image pre-processing for leaf analyses in plantations

In this work an image pre-processing module has been developed to extract quantitative information from plantation images with various degrees of infestation. Four filters comprise this module: the first one acts on smoothness of the image, the second one removes image background enhancing plants leaves, the third filter removes isolated dots not removed by the previous filter, and the fourth one is used to highlight leaves' edges. At first the filters were tested with MATLAB, for a quick visual feedback of the filters' behavior. Then the filters were implemented in the C programming language. At last, the module as been coded in VHDL for the implementation on a Stratix II family FPGA. Tests were run and the results are shown in this paper. © 2008 Springer-Verlag Berlin Heidelberg.

Applying enhancement filters in the pre-processing of images of lymphoma

Lymphoma is a type of cancer that affects the immune system, and is classified as Hodgkin or non-Hodgkin. It is one of the ten types of cancer that are the most common on earth. Among all malignant neoplasms diagnosed in the world, lymphoma ranges from three to four percent of them. Our work presents a study of some filters devoted to enhancing images of lymphoma at the pre-processing step. Here the enhancement is useful for removing noise from the digital images. We have analysed the noise caused by different sources like room vibration, scraps and defocusing, and in the following classes of lymphoma: follicular, mantle cell and B-cell chronic lymphocytic leukemia. The filters Gaussian, Median and Mean-Shift were applied to different colour models (RGB, Lab and HSV). Afterwards, we performed a quantitative analysis of the images by means of the Structural Similarity Index. This was done in order to evaluate the similarity between the images. In all cases we have obtained a certainty of at least 75%, which rises to 99% if one considers only HSV. Namely, we have concluded that HSV is an important choice of colour model at pre-processing histological images of lymphoma, because in this case the resulting image will get the best enhancement.

The minimization of open stacks problem: A review of some properties and their use in pre-processing operations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Image segmentation using morphological watershed applied to cartography

Image segmentation is a process frequently used in several different areas including Cartography. Feature extraction is a very troublesome task, and successful results require more complex techniques and good quality data. The aims of this paper is to study Digital Image Processing techniques, with emphasis in Mathematical Morphology, to use Remote Sensing imagery, making image segmentation, using morphological operators, mainly the multi-scale morphological gradient operator. In the segmentation process, pre-processing operators of Mathematical Morphology were used, and the multi-scales gradient was implemented to create one of the images used as marker image. Orbital image of the Landsat satellite, sensor TM was used. The MATLAB software was used in the implementation of the routines. With the accomplishment of tests, the performance of the implemented operators was verified and carried through the analysis of the results. The extration of linear feature, using mathematical morphology techniques, can contribute in cartographic applications, as cartographic products updating. The comparison to the best result obtained was performed by means of the morphology with conventional techniques of features extraction. © Springer-Verlag 2004.

Video target tracking by using competitive neural networks

A target tracking algorithm able to identify the position and to pursuit moving targets in video digital sequences is proposed in this paper. The proposed approach aims to track moving targets inside the vision field of a digital camera. The position and trajectory of the target are identified by using a neural network presenting competitive learning technique. The winning neuron is trained to approximate to the target and, then, pursuit it. A digital camera provides a sequence of images and the algorithm process those frames in real time tracking the moving target. The algorithm is performed both with black and white and multi-colored images to simulate real world situations. Results show the effectiveness of the proposed algorithm, since the neurons tracked the moving targets even if there is no pre-processing image analysis. Single and multiple moving targets are followed in real time.

Unsupervised segmentation method for cuboidal cell nuclei in histological prostate images based on minimum cross entropy

This paper presents a novel segmentation method for cuboidal cell nuclei in images of prostate tissue stained with hematoxylin and eosin. The proposed method allows segmenting normal, hyperplastic and cancerous prostate images in three steps: pre-processing, segmentation of cuboidal cell nuclei and post-processing. The pre-processing step consists of applying contrast stretching to the red (R) channel to highlight the contrast of cuboidal cell nuclei. The aim of the second step is to apply global thresholding based on minimum cross entropy to generate a binary image with candidate regions for cuboidal cell nuclei. In the post-processing step, false positives are removed using the connected component method. The proposed segmentation method was applied to an image bank with 105 samples and measures of sensitivity, specificity and accuracy were compared with those provided by other segmentation approaches available in the specialized literature. The results are promising and demonstrate that the proposed method allows the segmentation of cuboidal cell nuclei with a mean accuracy of 97%. © 2013 Elsevier Ltd. All rights reserved.

Determinação da velocidade de um objeto, a partir de uma seqüência de imagens, aplicando as equações de colinearidade modificadas

Pós-graduação em Ciências Cartográficas - FCT

Sistema de armazenamento de imagens comprimidas através da transformada wavelet

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science

Caracterização e mapeamento da cultura da cana-de-açúcar no município de Rio Claro (SP) por meio de técnicas de sensoriamento remoto e sistemas de informações geográficas

This paper discusses about the use of remote sensing image and processing digital images tools for mapping and assessing the effect on the biomass of the culture of sugar cane in the city of Rio Claro. We used satellite images from CBERS in the passages of 19/04/2009 and 23/09/08, which correspond respectively to the stages where the sugar cane appeared in growing and adult pre-harvest stage. In these images, we applied procedures of digital processing, as the application of the procedures for extending linear contrast, radiometric normalization, Normalized Vegetation Index (NDVI) and pixel by pixel classification by ISOSEG through of the software SPRING. As a result we obtained mapping of the distribution and development stages of the culture of sugar cane in the city of Rio Claro and the mapping of the existing biomass of this culture, showing that the method used to assess the relative effect on biomass in culture of sugar cane was efficient, and that images of low-medium resolution are not the most suitable for the mapping of this culture

Estimation of croplands using indicator kriging and fuzzy classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparative analysis between a CAD model design and physical models obtained by manufacturing additive technologies using optical scan

This article presents a detailed study of the application of different additive manufacturing technologies (sintering process, three-dimensional printing, extrusion and stereolithographic process), in the design process of a complex geometry model and its moving parts. The fabrication sequence was evaluated in terms of pre-processing conditions (model generation and model STL SLI), generation strategy and physical model post-processing operations. Dimensional verification of the obtained models was undertook by projecting structured light (optical scan), a relatively new technology of main importance for metrology and reverse engineering. Studies were done in certain manufacturing time and production costs, which allowed the definition of an more comprehensive evaluation matrix of additive technologies.

Uso da cor da casca como indicador de qualidade do maracujá amarelo (Passiflora edulis Sims. f. flavicarpa Deg.) destinado à industrialização

As características físico-químicas do maracujá amarelo em três estádios de cor de casca (1/3 amarelo, 2/3 amarelo e inteiro amarelo), em quatro épocas da safra/99 foram avaliadas, visando estabelecer o ponto de colheita de melhor qualidade da fruta para a industrialização. Foram determinados o tamanho dos frutos, a cor da casca e da polpa, o rendimento de extração da polpa, o teor de sólidos solúveis totais, o pH, a acidez total, o ratio e o teor de vitamina C. Foi empregado o delineamento experimental inteiramente casualizado, com 8 repetições, 5 frutos por parcela e 3 tratamentos. Os resultados mostraram que o rendimento de polpa não apresentou diferença significativa entre os tratamentos e entre as colheitas. Com relação à cor da polpa, houve diferença significativa entre os três estádios apenas na 1ª colheita. Os frutos de casca 1/3 amarela apresentaram teor de sólidos solúveis significativamente inferior aos demais apenas na 3ª e 4ª colheitas, mas o maior valor médio ocorreu na 4ª colheita. O teor de acidez total dos frutos de casca inteira amarela foi significativamente inferior àqueles das colheitas 2, 3 e 4. Os valores mais elevados de vitamina C foram obtidos na 1ª colheita e, os frutos de casca 1/3 amarela apresentaram teores significativamente inferiores nas colheitas 1 e 2. de maneira geral, os resultados indicaram que, embora tenham ocorrido algumas diferenças nas características físico-químicas dos frutos nos diferentes estádios de cor de casca, frutos colhidos em todos os estádios de cor de casca estudados apresentaram-se adequados à industrialização.

Aircraft interior failure pattern recognition utilizing text mining and neural networks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Metrics to support the evaluation of association rule clustering

Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.