A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.

Phase-shifting fringe projection methods have been developed for three-dimensional scanning (Zuo et al., 2018). However, the 3-Dimensional (3D) scanning of objects with a high dynamic reflectivity range based on structured light is a challenging task to achieve (Feng et al., 2018). The incorrect intensities captured will cause phase and measurement errors. Thus, this paper proposes a method that improves the current High Dynamic Range (HDR) (Jiang et al., 2016)) method to increase the dynamic range. The camera and projector have 3 channels, red, green, and blue, which can absorb and project these lights independently. This paper proposes a method that makes use of this by controlling the intensity of each projected for the camera. Each image can be split into 3 channels and provide 3 images which contain different intensities, then it will be used to compute the 3D information. In general, this is done by controlling the projection of red, green and blue (RGB) channel and apply the Jiang’s algorithm (Jiang et al., 2016). The results are compared and analysed with current HDR (Jiang’s method) and the regular three-step phase-shifting methods. From the experimental results, it has shown that our proposed method outperforms the current HDR and the regular three-step phase-shifting methods. Specifically, the proposed method manages to increase the dynamic range of the reflective property of objects. Additionally, our proposed method has also significantly reduced the times of 3D object measurements.

Various method was developed for the acquisition of the three-dimensional surface of an object. One of the more popular methods used is the structured light profilometry method. It can capture a high-resolution three-dimensional object in real-time. Not only that, but this method is also non-invasive, which is very suitable for the measurement of fragile samples. This paper discusses the accuracy and stability of a structured light profilometry method that is used to obtain a 3D measurement. The experiment is done by using a calibrated camera and projector. A light pattern is then projected onto the sample and captured by the camera. The accuracy of the system is investigated by capturing a flat plate with an increment of 50 µm from 0 µm to 1000 µm. The result has shown a maximum percentage error of this system is 15.76% which is 9.3511 µm, and the minimum percentage error is 0.15% which is 0.7339 µm. For the stability test, the plate was captured thirty times at the same location, and the data obtained shows the consistency of the system has a minimum and maximum standard deviation of 2.4991 µm and 6.8886 µm, which is within 7 µm. The test on the feeler gauge shows a maximum percentage error of 2.12% which is 2.1671 µm.