5 research outputs found
Segmentasi Kerusakan Daun Padi pada Citra Digital
Kerusakan daun padi menyebabkan produksi padi mengalami penurunan dan kerugian ekonomi terutama di bidang pertanian. Pada bidang visi komputer hal penting dalam mendeteksi kerusakan adalah melakukan segmentasi area daun yang rusak. Berbagai kajian tentang segmentasi area telah dilakukan oleh para peneliti sebelumnya. Namun pada penelitian ini dikaji tentang segmentasi otomatis. 聽Pada聽聽 penelitian ini bertujuan untuk mendapatkan warna citra yang menghasilkan segmentasi kerusakan pada daun padi terbaik. Penulis mengusulkan metode baru untuk segmentasi berdasarkan statistika nilai piksel citra daun padi sebagai alternatif dari metode yang sudah ada. Statistika nilai piksel untuk segmentasi yang digunakan adalah 0.20 dari nilai tertinggi masing-maisng komponen warna Hue, Saturation, Value (0.20 * maks (HSV)). Metode yang diusulkan telah diujikan pada komponen warna Hue, Saturation, Value dan Grayscale. Hasil yang diperoleh dari pengujian menunjukkan bahwa komponen warna Hue sukses melakukan segmentasi, sementara komponen warna Saturation, Value dan pada citra dengan warna grayscale gagal melakukan segmentasi.
Anisotropic Mesh Adaptation for Image Segmentation based on Partial Differential Equations
Title from PDF of title page viewed January 12, 2021Dissertation advisor: Xianping LiVitaIncludes bibliographical references (pages 69-85)Thesis (Ph.D.)--Department of Mathematics and Statistics and School of Computing and Engineering. University of Missouri--Kansas City, 2020As the resolution of digital images increases significantly, the processing of
images becomes more challenging in terms of accuracy and efficiency. In this dissertation,
we consider image segmentation by solving a partial differential equation
(PDE) model based on the Mumford-Shah functional. We first, develop a new
anisotropic mesh adaptation (AMA) framework to improve segmentation efficiency and accuracy. In the AMA framework, we incorporate an anisotropic mesh adaptation
for image representation and a nite element method for solving the PDE model.
Comparing to traditional algorithms solved by the finnite difference method, our AMA
framework provides faster and better results without the need for re-sizing the images
to lower quality. We also extend the algorithm to segment images with multiple
regions.
We also improve the well-known Chan-Vese model by developing a locally
enhanced Chan-Vese (LECV) model. Our LECV model incorporates a newly define
signed pressure force (SPF) function, which is built upon the local image information.
The SPF function helps to attract the contour curve to the object boundaries for images with inhomogeneous intensities. The proposed LECV model, together with the
AMA segmentation framework can successfully segment the image with or without
inhomogeneous intensities. While most other segmentation methods only work on low-resolution
images, our LECV model is successfully applied to high-resolution images,
with improved efficiency and accuracy.Introduction -- PDE-Based Image Segmentation -- Background and Literature review -- AMA Segmentation Method -- LECV Model for Image Segmentation -- Conclusion and discussio
Segmentation of images by color features: a survey
En este articulo se hace la revisi贸n del estado del arte sobre la segmentaci贸n de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown
Uso de t茅cnicas de visi贸n artificial para la identificaci贸n de da帽os foliares causados por plagas y enfermedades en plantas de jitomate
Actualmente, el 谩rea agr铆cola se ha diversificado en la producci贸n de diferentes cultivos, logrando incrementar la producci贸n por planta y calidad del fruto, realizando esta actividad a cielo abierto como en ambientes protegidos (invernaderos). Bajo esta premisa, la distribuci贸n de cultivos se ha extendido hacia el extranjero, obteniendo ingresos financieros considerables; sin embargo, una de las principales preocupaciones de los productores son los descensos econ贸micos por la llegada no deseada de enfermedades y plagas que afectan a los cultivos. Por ende, en esta investigaci贸n, se ha implementado un sistema para el reconocimiento de da帽os foliares causados por enfermedades y plagas en plantas de jitomate, que funja como herramienta de apoyo para la correcta identificaci贸n con base en t茅cnicas de visi贸n artificial. Para resolver esta problem谩tica, se ha desarrollado una metodolog铆a con una estructura modular, representada por las siguientes etapas: preprocesamiento, segmentaci贸n, extracci贸n de caracter铆sticas, equilibrio de clases, y clasificaci贸n. As铆 mismo, se ha usado el conjunto de datos Plantvillage, el cual contiene diez clases distintas, representadas por ocho enfermedades, una plaga, y una clase completamente sana. Derivado de las fases experimentales desarrolladas a lo largo de esta investigaci贸n, el sistema propuesto, alcanza un rendimiento del 94.65% de exactitud