A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class islabeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selectionproblems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates thehistogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to the Journa

Analysis Of Banana Plant Disease Characterization Using Thermal Camera With Tressolding Method

Banana is a fruit plant that is widely produced in Indonesia. Unfortunately, this plant is very susceptible to diseases which can reduce the quality and quantity of the crop. This paper proposes disease detection in banana plants using a thermal camera. The detection is carried out using image processing techniques with multilevel thresholding methods. The image is captured using a thermal camera, then the image is preprocessed to suit what is desired. After that, so that the position is the same as the image taken using a digital camera, the image produced by the thermal camera is carried out by an image registration process. The image processing result is compared with the ground truth image obtained from a digital camera to determine the effectiveness of the proposed method. The effectiveness of the proposed method is measured using the parameters Recall, Precision, F-measure, and Accuracy. The effectiveness of the proposed method is quite effective because it produces parameter values above 80%, namely the recall value of 86,59%, the Precision of 99,1%, the F-measure of 92%, and the accuracy of 89,78%

Комп’ютерний аналіз характерних елементів фрактографічних зображень

Для кількісного аналізу фрактографічних зображень використано спосіб їх сегментації багаторівневим порогуванням. На відміну від відомих підходів для меншого спотворення сегментованого зображення враховували спільні ознаки, властиві не лише великим, але і малим його деталям, яким відповідають невисокі піки на гістограмі яскравості. Це дало змогу розділити зображення фрагмента поверхні зламу на зв’язні області, а після цього визначати та аналізувати їх кількісні характеристики.Для количественного анализа фрактографических изображений использован способ их сегментации путем многоуровневого определения порога. В отличие от известных подходов для меньшего искажения сегментированного изображения учтены общие признаки, свойственные не только крупным, но и мелким его деталям, которым соответствуют невысокие пики на гистограмме яркостей. Это дало возможность разделить изображение фрагмента поверхности излома на связные области, а после этого определять и анализировать их количественные характеристики.For the quantitative analysis of the fractographic images the method of their segmentation by multilevel definition of the threshold was used. Unlike the known approaches to get the less distortion of the segmented image, the general features typical of not only large but also small details, to which low peaks in the histogram of brightness correspond, are taken into account. It enables us to separate the image fragment of the fracture surface into connected regions, and then to identify and analyze their quantitative characteristics

DETEKSI LEBAR DAERAH ALIRAN SUNGAI CITARUM BERDASARKAN PENGOLAHAN CITRA GOOGLE EARTH MENGGUNAKAN METODE MULTILEVEL THRESHOLDING

Bumi terdiri dari dari 2 komponen utama yaitu daratan dan perairan. Perairan memiliki jumlah rasio yang lebih besar dari dataran yaitu 2/3 atau sebesar 70%. Adapun salah satu bentuk perairan yang sering ditemukan adalah sungai. Sungai adalah aliran air yang besar dan memanjang yang mengalir secara terus-menerus dari hulu (sumber) menuju hilir (muara) yang memisahkan dataran. Indonesia sebagai negara tropis tentunya memiliki beberapa pulau besar dan kecil serta memiliki banyak sungai, sehingga memerlukan jembatan untuk menghubungkan wilayah yang terpisahkan oleh sungai tersebut. Untuk membangun jembatan dibutuhkan beberapa data salah satunya adalah lebar sungai. Menghitung besarnya sungai, tentunya akan sulit dilakukan jika harus menghitungnya dengan media konvensial. Salah satu aplikasi yang dapat digunakan untuk mempermudah mendeteksi atau mengetahui suatu tempat dan bangunan adalah google earth. Aplikasi yang dihasilkan oleh perusahaan Google ini menggunakan satelit untuk mendeteksi suatu lokasi salah satunya adalah sungai. Oleh karena itu, penelitian ini memanfaatkan aplikasi google earth untuk mempermudah mendeteksi sungai Citarum di Bandung. Thresholding merupakan salah satu metode segmentasi citra di mana prosesnya didasarkan pada perbedaan derajat keabuan citra. Multilevel thresholding merupakan metode segmentasi citra thresholding yang menggunakan dua atau lebih nilai threshold. Pada tugas akhir ini, melakukan deteksi lebar aliran sungai dengan menggunakan pengolahan citra google earth dengan metode multilevel thresholding untuk mempermudah dalam pembangunan di sekitar sungai oleh pihak yang tertentu. Adapun hasil yang didapatkan dari penelitian ini adalah deteksi lebar sungai dengan diatas 90

New Thresholding Methods for Unimodal Images of Food and Agricultural Products

Global thresholding methods fail to segment poor contrast unimodal food and agricultural images. Many local adaptive thresholding and multi-level thresholding methods are reported in image processing journals, but there are limited studies extending them to food and agricultural images. This article presents development of Reverse Water Flow, a new local adaptive thresholding method, and Twice Otsu, a new multi-level thresholding method, to segment food and agricultural images. Reverse Water Flow method was well suited for identification of smaller objects such as 2 mm diameter holes. It reduced computational time by 61.1% compared to the previous best method. Twice Otsu method was well suited to identify larger objects. Both thresholding methods successfully segmented food and agricultural images from different imaging sources and should be extendable to other unimodal and poor contrast images. The developed methods may also facilitate further development of segmentation methods for food and agricultural applications