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The rice seed setting rate (RSSR) is an important component in calculating rice yields and a key phenotype for its genetic analysis. Automatic calculations of RSSR through computer vision technology have great significance for rice yield predictions. The basic premise for calculating RSSR is having an accurate and high throughput identification of...
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We describe an approach to making a model be aware of not only intensity but also properties such as feature direction and scale. Such properties can be important when analysing images containing curvilinear structures such as vessels or fibres. We propose the General Multi-Angle Scale Convolution (G-MASC), whose kernels are arbitrarily rotatable a...
Citations
... The Focused D* algorithm proved to be more efficient than D* in environments in which maps were incomplete or inaccurate [93]. In 2016, Lulio and Lugli et al. implemented a J Segmentation (JSEG) algorithm, statistical Artificial Neural Networks (ANN) image segmentation techniques and sensory fusion in the AgriBOT robot, based on the extraction of objects from real natural scenes, identifying items such as fruits, grasses, stems, branches and leaves [94,95]. ...
The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.
... One way to segment images with textures is to consider the spatial arrangement of pixels using a region-growing technique whereby a homogeneous mode is defined with pixels grouped in the segmented region. This is known J measure based SEGmentation "JSEG" [37]. It is an unsupervised method used for segmentation and classification. ...
In this paper, an adapted unsupervised segmentation approach is proposed to fully automate the segmentation of white blood cells and their nuclei. Segmentation and counting of white blood cells from microscope images are challenging tasks, especially the segmentation of white blood cell nuclei from the cell wall and cytoplasm because of the need to consider intra-class variations arising from non-uniform illumination, stage of maturity, colour distribution, scale, and overlapped cells with other components of the blood. We propose the use of the JSEG algorithm based on colour-texture distribution, and adapted region growing using the Fuzzy C Mean to segment and count WBCs and their nuclei. First, colours in the image are quantized to represent differentiated regions in the image. Image pixel colours are then replaced by their corresponding colour class labels, thus forming a class-map of the image. A criterion for "good" segmentation using this spatial class-map is applied to local image windows resulting in J-images, which can be segmented using adapted region growing based on the Fuzzy C Mean algorithm. The Fuzzy C Mean is also employed for counting each white blood cell in images. Performance of the proposed method is evaluated on a combined dataset of 10 types of white blood cell with 200 digital images collected from 3 datasets. It achieves an average segmentation accuracy using four indices for WBC segmentation: jaccard distance, rand index, boundary detection error and F-value indices, 0.002, 0.93, 10.11, 0.93, respectively, while for WBC nuclei segmentation, it achieves indices values, 0.015, 0.88, 14.11, 0.90, respectively. The segmentation accuracy of the proposed method is also compared and benchmarked with the other existing techniques for segmentation of white blood cells over the same datasets and the results show that the proposed method is superior to other approaches.
... O método de segmentação com este algoritmo passa por dois estágios principais, a quantização do espaço de cores (processo de redução de número de cores distintas em uma determinada imagem) onde cada pixel será rotulado pela sua correspondente classe, criado um mapa de classes da imagem e a segmentação espacial responsável por pegar o mapa das classes e aplicar o crescimento de região (Lulio, 2013 ). ...
O processo de seleção de frutos em larga escala ainda é feito de forma manual, o que acarreta em grandes erros na seleção. Neste sentido, foi proposto uma aplicação utilizando visão computacional para automatizar esta tarefa, tornando mais rápida e eficaz. Conceitos de processamento de imagem e aprendizado de máquina foram usados no desenvolvimento deste estudo que acarretou em uma taxa de 95% de acerto durante a etapa de classificação da qualidade dos frutos.
