Finding Connected Components in a Gray Scale Image

Abstract—Finding connected components are well defined for binary images. The concept of connected components can be extended for gray level image. But the problem is the criteria based on which a connected component would be defined. A gray level image is an image having 256 different pixel intensity levels. If we consider connected regions having only a particular pixel values, the number of connected components would not be meaningful and the purpose of finding connected components would be lost. So, we define a connected component in a gray scale image based on range of pixel mapping and new method to find connected components in a gray scale image is proposed. Three different types of pixel range mapping are introduced, using connected components in a gray level image can be successfully found. Connected components in a gray level image are the segments of image having the same range of pixel values. Different regions or segments of image can be found easily.Keywords—Connected component, gray scale labelling, pixel range mapping, linear mapping, logarithmic mapping, square root mapping.(Article history: Received 1 November 2016 and accepted 30 December 2016

CHARACTER IMAGE SEGMENTATION OF JAVANESE SCRIPT USING CONNECTED COMPONENT METHOD

Automation of Javanese script translation is needed to make it easier for people to understand the meaning of ancient Javanese script. By using Javanese script image as input, the translation system generally consists of character segmentation, character recognition, and combining the recognized characters as a meaningful word. The segmentation which obtains region of interest of each character, is an important process in the translation system. In the previous research, segmentation using projection profile method can separate each character well. The method can overcome characters overlapping, but it still produces truncated characters. In this study, we proposed a new segmentation to reduce the truncated character. The first step of the proposed method is pre-processing that consists of converting input into binary image and cleaning noises. The next step is to determine the connected component labels, which further perform as candidate of characters. Some of the candidates are still represented by more than one labels, so that we need a process to merge the connected component labels that have centroid distance less than threshold. We evaluate the proposed method using Intersection over Union (IoU). The evaluation shows the best accuracy 93,26%

Un algoritmo en tiempo real para etiquetado de componentes conectados en imágenes

Esta comunicación presenta un algoritmo de dos pasadas para el etiquetado en tiempo real de los componentes conexos en una imagen. El algoritmo propuesto es una buena opción frente a otras alternativas de dos y múltiples pasadas ya que ha sido diseñado considerando que su implementación en FPGAs ofrezca un buen compromiso entre recursos ocupados y velocidad de operación. Se describen dos implementaciones hardware de este algoritmo, cuyo desarrollo se ha llevado a cabo siguiendo un flujo de diseño basado en la herramienta System Generator de Xilinx.Comunidad Económica Europea MOBY-DIC FP7-IST- 248858Ministerio de Ciencia e Innovación (España) TEC2008-04920Junta de Andalucía P08- TIC-03674Fondos Feder P08- TIC-0367

DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans

Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads to dissection or rupture through progressive enlargement of the aorta. It is usually asymptomatic and screening recommendation are limited. The gold-standard evaluation is performed by computed tomography angiography (CTA) and radiologists time-consuming assessment. Scans for other indications could help on this screening, however if acquired without contrast enhancement or with low dose protocol, it can make the clinical evaluation difficult, besides increasing the scans quantity for the radiologists. In this study, it was selected 587 unique CT scans including control and TAA patients, acquired with low and standard dose protocols, with or without contrast enhancement. A novel segmentation model, DeepVox, exhibited dice score coefficients of 0.932 and 0.897 for development and test sets, respectively, with faster training speed in comparison to models reported in the literature. The novel TAA classification model, SAVE-CT, presented accuracies of 0.930 and 0.922 for development and test sets, respectively, using only the binary segmentation mask from DeepVox as input, without hand-engineered features. These two models together are a potential approach for TAA screening, as they can handle variable number of slices as input, handling thoracic and thoracoabdominal sequences, in a fully automated contrast- and dose-independent evaluation. This may assist to decrease TAA mortality and prioritize the evaluation queue of patients for radiologists.Comment: 23 pages, 4 figures, 7 table

EKSTRAKSI FITUR PADA PENGENALAN KARAKTER AKSARA JAWA BERBASIS HISTOGRAM OF ORIENTED GRADIENT

Buku-buku kuno Bahasa Jawa memiliki konten kekayaan intelektual Indonesia seperti agama, linguistik, filosofi, mitos, pelajaran moral, hukum dan norma adat, kerajaan, cerita rakyat, sejarah, dan lain sebagainya. Tidak banyak yang mempelajari karya tersebut karena ditulis dengan Aksara Jawa dan tidak banyak yang memahami. Untuk membantu penerjemahan dokumen berbahasa Jawa dilakukan otomatisasi sistem penerjemahan. tahap penerjemahan terdiri dari segmentasi untuk mendapatkan karakter dari citra tulisan dalam naskah Aksara Jawa. Kemudian tiap karakater dikenali sebagai abjad. Dan yang terakhir adalah mengkombinasikan tulisan latin yang telah dikenali menjadi kata yang berarti. Penelitian yang membahas tentang pengenalan Aksara Jawa telah dilakukan, seperti fokus pada segmentasi karakter dan pengenalan Aksara Jawa. Pada penelitian sebelumnya dilakukan perbaikan pada metode segmentasi namun tetap mendapatkan hasil yang sama dalam hal akurasi kebenaran. Pada penelitian kali ini diusulkan metode baru pada tahap ekstraksi fitur, yaitu menggunakan metode Histogram of Oriented Gradient (HOG). Metode HOG banyak digunakan pada pengenalan wajah, hewan, dan deteksi citra kendaraan, dan lain-lain. Penelitian ini juga pernah diusulkan untuk mengenali tulisan tangan berbahasa Inggris dan Huruf Bengali dan mendapatkan hasil yang optimal. Pada penelitian ini didapatkan hasil akurasi pengenalan karakter Aksara Jawa sebesar 89,7%.Ekstraksi Fitur, Histogram of Oriented Gradient, Aksara Jaw

Метод об’єктної фільтрації карт класифікації земного покриву на основі морфологічних ознак

Запропоновано метод зменшення шуму накартах класифікації земного покриву, отриманих на основі супутникових знімків високого розрізнення. Метод ґрунтується на дослідженні властивостей кожної групи пікселів з однаковим значенням класу.Точність та ефективність даного методу підтверджується тестуванням на незалежній вибірці, а також шляхом візуального порівняння з результатами загальновідомих методів фільтрації.Предложен метод уменьшения шума на картах классификации земного покрова, полученных на основе спутниковых снимков высокого разрешения. Метод основан на исследовании свойств каждой группы точек с одинаковым значением класса. Точность и эффективность данного метода подтверждается тестированием на независимой выборке, а также путем визуального сравнения с результатами общеизвестных методов фильтрации.This paper presents a method for noise reduction on the classification maps based on high resolution satellite images. Method is based on the properties investigation of each group of pixels with the same class value. The accuracy and efficiency of this method is confirmed by testing on an independent set, as well as by visual comparison with well-known filtration methods