Counterfeit drugs have been a serious problem causing damage to people's health over the world. Numerous anti-counterfeiting methods based on tagging have been proposed; however, they suffer from three major issues: (1) tagging is applicable only to packages, not tablets directly; (2) end-users, i.e., patients, cannot inspect the tags; (3) tagging incurs extra costs for manufacturers. This paper describes a new method that we propose for authenticating individual medicine tablets as-is by matching images of printed characters. The printed characters on individual tablets of the same medicine seem the same to human eyes, but each is characterized by tiny unique differences. The contributions of this paper are: (a) to reveal the uniqueness of the characters printed by an actual pharmaceutical-use machine and (b) to propose a practical system to identify individual tablets using image matching. Our system is useful for any patients who want to authenticate a medicine tablet at hand: it only requires a picture with a smartphone camera. Our system is also useful for medicine manufacturers, because the database can be constructed using the existing manufacturing process without incurring additional cost. Our image matching algorithm recognizes very detailed features of the images and is accurate and fast even for a large-scale database. In conducted experiments, 1,000 sample tablets were captured using the same optical setup as an actual medicine manufacturing machine. Obtained results showed that 100% accuracy in individual tablet authentication was achieved.

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This paper presents a novel framework and its prototype tool for indexing and retrieving specific fragments of voice recordings obtained during discussions about physical objects such as text documents, pictures, or 3D models. When a specific part of an object is mentioned, it is tagged with an ink dot that is immediately registered in a database by capturing a microscopic image of the dot. Simultaneously, an index of the recording fragment is created and linked with the dot. After the recording, a dot can be scanned and identified by matching its microscopic image with the database to retrieve the linked recording fragment for playback. A handy tool was developed to facilitate these operations while the user concentrates on the ongoing discussion. Performance tests of the dot identification have shown genuine matches without error. In demonstrations of a realistic usage scenario, the tool successfully facilitated the creation of indexes with dots during a voice recording and correctly played back all the specific recording fragments linked to the dots.@en

This paper presents a new identification system for tiny parts that have no space for applying conventional ID marking or tagging. The system marks the parts with a single dot using ink containing shiny particles. The particles in a single dot naturally form a unique pattern. The parts are then identified by matching microscopic images of this pattern with a database containing images of these dots. In this paper, we develop an automated system to conduct dotting and image capturing for massproduced parts. Experimental results show that our "Tip-on-achip" system can uniquely identify more than ten thousand chip capacitors.

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This paper presents a fully automated system for detecting, classifying, microscopic imaging, and individually identifying multiple parts without ID-marking or tagging. The system is beneficial for product assemblers, who handle multiple types of parts simultaneously. They can ensure traceability quite easily by only placing the parts freely on the system platform. The system captures microscopic images of parts as their "fingerprints," which are matched with pre-registered images in a database to identify an individual part's information such as its serial number. We demonstrate a working prototype and interaction scenario.@en