Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.

To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%.  Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology.

International audienceThis paper presents a system dedicated to automatic language identification of text regions in heterogeneous and complex documents. This system is able to process documents with mixed printed and handwritten text and various layouts. To handle such a problem, we propose a system that performs the following sub-tasks: writing type identification (printed/handwritten), script identification and language identification. The methods for the writing type recognition and the script discrimination are based on the analysis of the connected components while the language identification approach relies on a statistical text analysis , which requires a recognition engine. We evaluate the system on a new public dataset and present detailed results on the three tasks. Our system outperforms the Google plug-in evaluated on the ground-truth transcriptions of the same dataset

Kata kunci merupakan hal terpenting dalam mencari sebuah informasi. Penggunaan kata kunci yang tepat menghasilkan informasi yang relevan. Saat penggunaannya sebagai query, pengguna menggunakan bahasa yang alami, sehingga terdapat kata di luar dokumen jawaban yang telah disiapkan oleh sistem. Sistem tidak dapat memproses bahasa alami secara langsung yang dimasukkan oleh pengguna, sehingga diperlukan proses untuk mengolah kata-kata tersebut dengan mengekspansi setiap kata yang dimasukkan pengguna yang dikenal dengan Query Expansion (QE). Metode QE pada penelitian ini menggunakan Word Embedding karena hasil dari Word Embedding dapat memberikan kata-kata yang sering muncul bersama dengan kata-kata dalam query. Hasil dari word embedding dipakai sebagai masukan pada pseudo relevance feedback untuk diperkaya berdasarkan dokumen jawaban yang telah ada. Metode QE diterapkan dan diuji coba pada aplikasi chatbot. Hasil dari uji coba metode QE yang diterapkan pada chatbot didapatkan nilai recall, precision, dan F-measure masing-masing 100%; 70% dan 82,35 %. Hasil tersebut meningkat 1,49% daripada chatbot tanpa menggunakan QE yang pernah dilakukan sebelumnya yang hanya meraih akurasi sebesar 68,51%. Berdasarkan hasil pengukuran tersebut, QE menggunakan word embedding dan pseudo relevance feedback pada chatbot dapat mengatasi query masukan dari pengguna yang ambigu dan alami, sehingga dapat memberikan jawaban yang relevan kepada pengguna.  Keywords are the most important words and phrases used to obtain relevant information on content. Although users make use of natural languages, keywords are processed as queries by the system due to its inability to process. The language directly entered by the user is known as query expansion (QE). The proposed QE in this research uses word embedding owing to its ability to provide words that often appear along with those in the query. The results are used as inputs to the pseudo relevance feedback to be enriched based on the existing documents. This method is also applied to the chatbot application and precision, and F-measure values of the results obtained were 100%, 70%, 82.35% respectively. The results are 1.49% better than chatbot without using QE with 68.51% accuracy. Based on the results of these measurements, QE using word embedding and pseudo which gave relevance feedback in chatbots can resolve ambiguous and natural user’s input queries thereby enabling the system retrieve relevant answers.

<p class="Abstrak">Penelitian tentang segmentasi gigi individu telah banyak dilakukan dan memperoleh hasil yang baik. Namun, ketika dihadapkan kepada gigi overlap maka hal ini menjadi sebuah tantangan. Untuk memisahkan dua gigi overlap, maka perlu mengekstrak objek overlap terlebih dahulu. Metode level set banyak digunakan untuk melakukan segmentasi objek overlap, namun memiliki kelemahan yaitu perlu didefinisikan inisial awal metode level set secara manual oleh pengguna. Dalam penelitian ini diusulkan strategi inisialisasi otomatis pada metode level set untuk melakukan segmentasi gigi overlap menggunakan Hierarchical Cluster Analysis (HCA) pada citra panorama gigi. Tahapan strategi yang diusulkan terdiri dari preprocessing dimana di dalamnya ada proses perbaikan, rotasi dan cropping citra, dilanjutkan proses inisialisasi otomatis menggunakan algoritma HCA , dan yang terakhir segmentasi menggunakan metode level set. Hasil evaluasi menunjukkan bahwa strategi yang diusulkan berhasil melakukan inisialisasi secara otomatis dengan akurasi 73%. Hasil evaluasi segmentasi objek overlap cukup memuaskan dengan rasio misclassification error  0,93% dan relative foreground area error 24%. Dari hasil evaluasi menunjukkan bahwa strategi yang diusulkan dapat melakukan inisialisasi otomatis dengan baik. Inisialisasi yang tepat menghasilkan segmentasi yang baik pada metode level set.</p><p><em><strong><br /></strong></em></p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Individual teeth segmentation has done a lot of the recent research and obtained good results.</em><em> W</em><em>hen faced with overlapping teeth, this is quite challenging. To separate overlapping teeth, it is necessary to extract the overlapping object first. </em><em>The l</em><em>evel set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial</em><em> </em><em>level set method manually by the user. This research proposes an automatic initialization strategy for the level set method to segment overlapping teeth using Hierarchical Cluster Analysis on dental panoramic radiograph images. The proposed strategy stage consists of preprocessing </em><em>where</em><em> there </em><em>are</em><em> several process</em><em>es</em><em> of enhancement, rotation</em><em>,</em><em> and cropping of the image, Then the automatic initialization process uses the HCA algorithm and the last is segmentation using the level set method. The evaluation results show that the proposed strategy is successful in carrying out automatic initialization with an accuracy of 73%. The results of the overlap object segmentation evaluation are satisfactory with a misclassification error ratio of 0.93% and a relative foreground area error of 24%. The evaluation results show that the proposed strategy can carry out automated initialization well. Proper initialization results can perform good segmentation of the level set method.</em></p><p><em><strong><br /></strong></em></p>