Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach.

Driven by health care reform and the advent of the private sector in the late 1980s, and by commitments made to the Association of Southeast Asian Nations (ASEAN), Vietnam is faced with a need to increase the regulation and training of its health care professionals. Previously, a diploma from an accredited health professional school was sufficient to practice for a lifetime. Legislation has recently been passed that will institute a licensing system, will require continuing medical education (CME) to maintain the license, and will probably place a large burden on the health professional schools and training institutes to provide CME. Supported by international nongovernmental organizations and foreign universities, the medical universities in Vietnam are responding and are preparing for their new and expanded role.

Biometric traits gradually proved their importance in real-life applications, especially in identification field. Among the available biometric traits, the unique shape of the human ear has also received loads of attention from scientists through the years. Hence, numerous ear-based approaches have been proposed with promising performance. With these methods, plenty problems can be solve by the distinctiveness of ear features, such as recognizing human with mask or diagnose ear-related diseases. As a complete identification system requires an effective detector for real-time application, and the current richness and variety of ear detection algorithms are poor due to the small and complex shape of human ears. In this paper, we introduce a new human ear detection pipeline based on the YOLOv3 detector. A well-known face detector named RetinaFace is also added in the detection system to narrow the regions of interest and enhance the accuracy. The proposed method is evaluated on an unconstrained dataset, which shows its effectiveness.

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