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"An efficient Region-Based Image Retrieval (RBIR) system must consider query region determination techniques and target regions in the retrieval process. A query region is a region that must contain a Region of Interest (ROI) or saliency region. A query region determination can be specified manually or automatically. However, manual determination is considered less efficient and tedious for users. The selected query region must determine specific target regions in the image collection to reduce the retrieval time. This study proposes a strategy of query region determination based on the Region Importance Index (RII) value and relative position of the Saliency Region Overlapping Block (SROB) to produce a more efficient RBIR. The entire region is formed by using the mean shift segmentation method. The RII value is calculated based on a percentage of the region area and region distance to the center of the image. Whereas the target regions are determined by considering the relative position of SROB, the performance of the proposed method is tested on a CorelDB dataset. Experimental results show that the proposed method can reduce the Average of Retrieval Time to 0.054 seconds with a 5x5 block size configuration."

"Dental record is one of the ways to identify human identity. Identification requires a system, which is able to recognize each human tooth automatically. Teeth and gums becomes an important issue be-cause they have a high similarity in a dental radiograph image. This similarity tends to influence the segmentation error. This paper proposes a new contrast enhancement by using parameter sigmoid transform to improve the segmentation accuracy. The five main steps are: 1) preprocessing to improve the image contrast using our proposed method, 2) teeth segmentation using horizontal and vertical in-tegral projection, 3) feature extraction, 4) teeth classification using Support Vector Machine (SVM) and 5) teeth numbering. Experimental results using our proposed method have an accuracy rate of 88% for classification and 73% for teeth numbering.

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Data rekaman gigi adalah salah satu cara untuk mengidentifikasi manusia. Pengidentifikasian membutuhkan sebuah sistem yang mampu mengenali tiap gigi secara otomatis. Intensitas gigi dan gusi yang hampir sama menjadi masalah utama pada citra dental radiographs karena dapat menga-kibatkan kesalahan dalam proses segmentasi. Pada paper ini diusulkan sebuah metode perbaikan kontras yang baru dengan menggunakan parameter sigmoid transform untuk meningkatkan keaku-ratan hasil segmentasi. Lima tahapan utama yaitu: 1) praproses untuk memperbaiki kontras gambar menggunakan metode yang diusulkan, 2) segmentasi gigi menggunakan horizontal dan vertical inte-gral projection, 3) ekstraksi fitur, 4) klasifikasi meggunakan Support Vector Machine (SVM) dan 5) penomoran gigi. Hasil eksperimen menggunakan metode yang diusulkan menunjukkan tingkat keaku-ratan hasil klasifikasi sebesar 88% dan penomoran gigi sebesar 73%."