Counterfeit medicines present a severe public health threat, especially in low-resource countries where consumers lack reliable means to verify the medicines they purchase. Visual inspection of medicine packaging images through keypoint matching techniques offers a promising approach for detecting design inconsistencies that could indicate counterfeit products. However, conventional methods often struggle with high computational costs and reduced accuracy when processing images of varying quality and perspectives. To address these limitations, we propose the Angle and Scale Voting (ASVote) method, which enhances keypoint-based image matching by introducing a 2D voting mechanism that leverages relative angles and scales of the keypoints to eliminate false matches(outliers) while identifying consistent matches (inliers). This approach significantly improves both processing time and accuracy. Experiments on a real-world dataset of medicine packages show that ASVote improves processing time and accuracy, outperforming conventional methods.

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.

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 mass-produced parts. Experimental results show that our "Tip-on-a-chip" system can uniquely identify more than ten thousand chip capacitors.

We propose a new smart lock system that uses user's own item as a smart key to the lock system. The system marks the item with an ink dot, named mIDoT-key (pronounced "my-dot-key"), and the assigned lock is opened by identifying the microscopic image of the dot. Unlike conventional smart keys, new users do not have to get an IC card or to install new software for using our system. Thus, the registration process is much easier for new users. This instant registration is beneficial to access control of many rooms. New visitors of these rooms can be supplied with a mIDoT registration key for easy access. We demonstrate a miniature door lock that can be opened with various items marked with the mIDoT-keys.

We present Intelli-Wrench, a smart tool for providing just-in-time information for mechanical assembly and maintenance workers, which releases the workers from miss-operation and annoying manual documents. The Intelli-Wrench captures the image of a bolt head as its "fingerprint" by using FIBAR (Fingerprint Imaging by Binary Angular Reflection) imaging method. The fingerprint image is then uploaded to a cloud server and matched with parts databases in order to provide the specific information associated with the bolt. Receiving the information, the Intelli-Wrench informs the workers about the designated location and torque requirements of the bolt. This direct integration of information retrieval into a tangible tool provides immediate access to relevant information otherwise found in manual documents. Furthermore, the Intelli-Wrench automatically logs the interaction and eliminates the annoying pointing-and-calling procedure which is the traditional way of secure servicing. We demonstrate a working prototype and interaction scenario.

This paper presents a novel method of enhancing image quality of face pictures using 3D and spectral information. Most conventional techniques directly work on the image data, shifting the skin color to a predefined skin tone, and thus do not take into account the effects of shape and lighting. The proposed method first recovers the 3D shape of a face in an input image using a 3D morphable model. Then, using color constancy and inverse rendering techniques, specularities and the true skin color, i.e., its spectral reflectance, are recovered. The quality of the input image is improved by matching the skin reflectance to a predefined reference and reducing the amount of specularities. The method realizes the enhancement in a more physically accurate manner compared to previous ones. Subjective experiments on image quality demonstrate the validity of the proposed method.

This paper proposes a new method for removing specularity from face images so that albedo (diffuse reflectivity) can be accurately estimated. Our method utilizes common structural properties of face images to estimate the Lambertian component (including shadows) without using albedo, then specularity is separated as the positive component of the difference between the estimated Lambertian component and the source image. Experimental results show that face-recognition performance is significantly improved by applying our new algorithm. Numerous previous face-recognition methods use 3D shape and albedo of an enrolled image for face recognition under variable pose and illumination conditions. However albedo used by the previous methods contained a residual of specularity from the enrolled image, which produced matching errors. Our algorithm is effective for solving this problem.

Three-dimensional measurement methods using structured light projection enable accurate depth measurements by decoding the disparity between the projector-camera pair from an observed pattern. However, actual projection devices have various systematic error sources that distort the structured light pattern, directly affecting the accuracy of 3-D measurements. We propose a new method of measuring depth that is not affected by the systematic errors in the projected pattern. In our method, the image of a pattern projected onto a plane is referenced to cancel errors. Based on the invariance of the cross-ratio under perspective projections, depth is obtained from the disparity determined on the referenced image. In experiments, our method removed the systematic errors and improved the accuracy of depth measurement without any extra calibration or measurement.

We propose a face recognition method that is robust against image variations due to arbitrary lighting and a large extent of pose variations, ranging from frontal to profile views. Existing appearance models defined on image planes are not applicable for such pose variations that cause occlusions and changes of silhouette. In contrast, our method constructs an appearance model of a three-dimensional (3-D) object on its surface. Our proposed model consists of a 3-D shape and geodesic illumination bases (GIBs). GIBs can describe the irradiances of an object's surface under any illumination and generate illumination subspace that can describe illumination variations of an image in an arbitrary pose. Our appearance model is automatically aligned to the target image by pose optimization based on a rough pose, and the residual error of this model fitting is used as the recognition score. We tested the recognition performance of our method with an extensive database that includes 14 000 images of 200 individuals with drastic illumination changes and pose variations up to 60/spl deg/ sideward and 45/spl deg/ upward. The method achieved a first-choice success ratio of 94.2% without knowing precise poses a priori.