Alternatif Pengukuran Luas Lubang Jalan Berbasis Data Video Menerapkan Threshold-based Marking dan GLCM

Road as one transport infrastructure has become the lifeblood of society which has an important role in development of state and nation. Indonesia today has around 3.800 kilometers of damaged roads, or about 10 percent of the total length of nation roads. One obstacle that causes the slow progress of road repairment is the measurement of the pothole area. In this process, calculation of area in each pothole is done. Measurement process nowadays is manually done by using a conventional tool (such as roll meter) with the help of human entirely. This research is trying to develop the system for detecting and measuring pothole area, by implementing the threshold-base marking and GLCM method in asphalt surface video.Based on Christian Koch and Loannis Brilakis, threshold-based method is able to do the segmentation and marking the possibility of pothole in asphalt surface video frame. Based on Mark Nixon and Aguado Alberto, gray level co-occurrence matrix has a good performance to extracting the texture in order to distinguish the texture of pothole and normal surface. Combination of both methods could be an alternative for measurement of pothole area in the process of road repairment in Indonesia.One  obstacle  that  causes  the  slow  progress  of  road repairment  is  the  measurement  of  the  pothole  area.  In  this  process, calculation of area in each pothole is done. Measurement process nowadays is manually performed by using a conventional tool (such as roll meter) with the help of human entirely. This research objective is to develop the system for detecting and measuring pothole area, by implementing the threshold-base marking and GLCM method in asphalt surface video. The system consists of two stages starting with candidate pothole detection using threshold-based marking then continued by classification process based on feature vector obtained through the GLCM. The results show that the accuracy rate of 91.67% system with a time of 0.08 seconds to process each frame.keywords: Pothole, Image detection, GLCMSalah satu kendala yang menyebabkan lambatnya perbaikan jalan yaitu pada proses pengukuran kerusakan jalan. Pada proses ini, dilakukan penghitungan luas tiap-tiap kerusakan. Proses pengukuran saat ini dilakukan secara manual menggunakan alat ukur sederhana (roll meter) dengan bantuan tenaga manusia sepenuhnya. Pada penelitian ini dikembangkan suatu sistem deteksi dan pengukuran kerusakan jalan khususnya lubang, berbasis data video, dengan menerapkan threshold-based marking dan GLCM. Sistem terdiri atas 2 tahapan, dimulai dengan mendeteksi kemungkinan area lubang  menggunakan threshold-based marking dilanjutkan dengan klasifikasi berdasarkan vektor ciri yang diperoleh melalui GLCM. Hasil pengujian menunjukkan bahwa tingkat akurasi sistem sebesar 91.67% dengan waktu proses 0,08 detik untuk setiap frame.kata kunci: Lubang jalan, Deteksi citra, GLC

Compact-Fusion Feature Framework for Ethnicity Classification

In computer vision, ethnicity classification tasks utilize images containing human faces to extract ethnicity labels. Ethnicity is one of the soft biometric feature categories useful in data analysis for commercial, public, and health sectors. Ethnicity classification begins with face detection as a preprocessing process to determine a human’s presence; then, the feature representation is extracted from the isolated facial image to predict the ethnicity class. This study utilized four handcrafted features (multi-local binary pattern (MLBP), histogram of gradient (HOG), color histogram, and speeded-up-robust-features-based (SURF-based)) as the basis for the generation of a compact-fusion feature. The compact-fusion framework involves optimal feature selection, compact feature extraction, and compact-fusion feature representation. The final feature representation was trained and tested with the SVM One Versus All classifier for ethnicity classification. When it was evaluated in two large datasets, UTKFace and Fair Face, the proposed framework achieved accuracy levels of 89.14%, 82.19%, and 73.87%, respectively, for the UTKFace dataset with four or five classes and the Fair Face dataset with four classes. Furthermore, the compact-fusion feature with a small number of features at 4790, constructed based on conventional handcrafted features, achieved competitive results compared with state-of-the-art methods using a deep-learning-based approach