Academic literature on the topic 'Otsu thresholding'

Abstrak
 Image Thresholding merupakan proses segmentasi untuk pemisahkan foreground dan background pada citra dengan cara membagi histogram citra menjadi dua kelas. Beberapa metode thresholding seperti Otsu dan Range-constrained Otsu menggunakan nilai variansi dari histogram untuk mendapatkan titik threshold, namun ketika menangani citra yang memiliki nilai variansi kelas foreground dan background tidak seimbang titik threshold yang dihasilkan kurang tepat. Paper ini mengusulkan metode Adaptif Range-constrained Otsu untuk mengatasi permasalahan variansi kelas yang tidak seimbang dengan cara
 mencari kelas yang memiliki nilai variansi lebih besar, untuk mendapatkan titik threshold yang lebih tepat. Pengujian menggunakan 22 NDT image dengan evaluasi misclassification error rate dan metode perankingan menunjukkan metode ini menghasilkan rerata ME 0.1153. Sedangkan Otsu sebesar 0.1746. Nilai rerata ranking 3.55, selisih 0.05 dibanding Kittler III. Hasil ini menunjukkan metode yang diusulkan kompetitif, terutama untuk segmentasi citra yang memiliki variansi kelas tidak sama.
 Kata kunci: segmentasi, thresholding, histogram, Otsu, Range-constrained.
 
 Abstract
 Image thresholding is segmentation process for separating foreground and background of an image by dividing image histogram into two classes. Several thresholding methods like Otsu and Rangeconstrained Otsu using the variance value of the histogram to get the threshold point, but when handling images that have unbalance class variance of the foreground and background produce less accurate threshold point. This paper proposes a method Adaptive Range-constrained Otsu to solve unbalance class variance problem by finding a class that has greater variance value to obtain more accurate threshold point. NDT testing using 22 images with misclassification error rate evaluation and ranking methods shows that this method results ME average of 0.1153, while Otsu method results 0.1746. The
 rankings mean value is 3.55, which has the difference of 0.05 when compared with Kittler III. These results show that the proposed method is competitive, especially for image segmentation with different class variance.
 Key word: segmentasi, thresholding, histogram, Otsu, Range-constrained.

Image segmentation is the key step in the process from image processing to image analysis. Otsu method is one of the most successful methods for image thresholding because of its simple calculation. Otsu method can select threshold automatically and divide the object from the background in the image. In this paper, various Otsu algorithm are studied.

Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria, so the segmentation efficiency is extremely low, and the real-time application cannot be realized. In order to solve this problem, a recursive algorithm of maximum entropy thresholding on circular histogram is proposed. Moreover, the recursive algorithm is extended to the case of multiclass thresholding. A large number of experimental results show that the proposed recursive algorithms are more efficient than brute force and the existing circular histogram thresholding based on the Otsu criteria.

<p><span>Ancient document usually contains multiple noises such as uneven-background, show-through, water-spilling, spots, and blur text. The noise will affect the binarization process. Binarization is an extremely important process in image processing, especially for character recognition. This paper presents an improvement to Nina binarization technique. Improvements were achieved by reducing processing steps and replacing median filtering by Wiener filtering. First, the document background was approximated by using Wiener filter, and then image subtraction was applied. Furthermore, the manuscript contrast was adjusted by mapping intensity of image value using intensity transformation method. Next, the local Otsu thresholding was applied. For removing spotting noise, we applied labeled connected component. The proposed method had been testing on H-DIBCO 2014 and degraded Jawi handwritten ancient documents. It performed better regarding recall and precision values, as compared to Otsu, Niblack, Sauvola, Lu, Su, and Nina, especially in the documents with show-through, water-spilling and combination noises.</span></p>

Iridologi atau yang biasa disebut sebagai diagnosis iris adalah suatu metode kedokteran yang menyatakan bahwa tiap bagian pada tubuh manusia dapat direpresentasikan dengan wilayah yang terdapat pada iris mata (bagian yang berwarna pada pupil) dan dapat mengetahui seseorang yang telah mengalami gejala penyakit. Untuk menentukan gejala suatu penyakit yang diderita pasien pada umumnya akan dilakukan tes laboratorium, dimana tes ini cukup mahal dan terkadang menimbulkan luka serta hasilnya pun terkadang lama untuk diketahui, metode sebelumnya mampu melakukan segmentasi citra iris mata tapi sulit untuk pengambilan fokus iris mata yang akurat dan nilai tingkat pada akurasi karena antara sklera, iris mata dan pupil masih jadi satu. Oleh karena itu, pada penelitian ini mengimplementasikan segmentasi tiga kelas menggunakan metode otsu thresholding, metode otsu merupakan metode pencarian ambang batas atau treshold otomatis yang baik kemudian menggunakan metode tiga kelas dengan menentukan foreground, TBD (To Be Determinand) dan background. Metode ini mampu melakukan segmentasi citra iris mata dengan baik yaitu dengan akurasi 99.07 % dan nilai Area Under Curve (AUC) pada kurva relaive operating characteristic (ROC) sebesar 99.07 %.