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To realize a practical bread recognition system, we introduce RGB-D sensor to recognize partially overlapped breads. In this paper, we focus on upper objects, which leans on other objects. Appearance of the object changes significantly, so it degrades recognition accuracy. In the proposed method, we adjust the superior object's appearance by using depth sensor, to improve recognition accuracy.
To realize automated checker system of cafeteria, we have developed a meal menu recognition system. This system employs a RGB-D sensor to get both color image and depth image. From these images, first we extract and distinguish each dishes. By knowing what type of dish is used for the meal, we can reduce candidates for meal menus, it leads up to improvement of menu recognition accuracy. Some experimental...
To improve object recognition accuracy, we introduce RGB-D sensor to get depth features and we construct two-stage recognition which combine recognition method with different characteristics. Experimental result show that the combination of these two refinement is effective to distinguish similar objects.
To realize object recognition in practical applications, it is necessary to handle layered objects. In this paper we introduce RGB-D sensor for object recognition system to treat overlapped objects. In the proposed method, we divide objects into segments and merge them by consulting partially recognition scheme. By several experiments, out method can recognize 30% occluded object with enough accuracy.
To recognize objects within narrow categories, it is important to extract effective features from small number of training samples. In this paper, first we discuss several depth features to improve object recognition accuracy. After that, we also discuss feature dimension reduction when we have insufficient training samples.
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