Generating haptic texture using solid noise

This article presents a robust and efficient system for euro and Mexican banknote recognition. A high banknote recognition and classification rate was achieved using neural networks and deep learning with real scene images taken with both sunlight and artificial light. Without extracting characteristics by hand, the convolutional neural networks was fed with raw images. Analysis and experiments were carried out on banknotes based on key features, such as; watermarks, portraits on the bills, bill value written in words and numbers, and the complete banknotes. It was concluded that both the color information and some regions of the banknotes, as well as the banknote denomination written in words and numbers and the complete banknote, is the appropriate information to achieve a high rate of banknote classification and recognition. The experimental results show that the proposed approach is promising with quite remarkable results; it performs an efficient and robust classification using real scene images taken with both sunlight and artificial light and is invariant to banknote rotation and translation. A high recognition rate was achieved for Mexican banknotes and for euros. At present, the results contained herein are an improvement over thosereported in the state of the art.

Femtosecond laser dot-matrix marking on nickel-based alloy using a simple diaphragm-based spatial shaped modulation: Size and position control of marking units with high recognition rate

This paper is concerned with haptic simulation in multi-user virtual environments in which the users can haptically interact in a shared virtual world from separate workstations over an Ethernet local-area Network (LAN). High-fidelity haptic rendering requires a minimum control update rate of 1000Hz which is beyond the capability of popular network protocols such as the UDP and TCP/IP. Consequently, a multi-rate control strategy is adopted in which local force-feedback loops are executed at higher rates than data packet transmission between the user workstations. Two control architectures, i.e. centralized and distributed, are presented and their stability margins are compared. Two methods for mathematical modelling and analysis of the proposed multi-rate haptic control systems are examined. Analytical and experimental results demonstrate that the distributed control architecture is superior to the centralized controller from performance and stability perspectives. This is confirmed through experiments with a dual-user dual-finger haptic rendering platform.

A new computer haptics algorithm to be used in general interactive manipulations of deformable virtual objects is presented. In multimodal interactive simulations, haptic feedback computation often comes from contact forces. Subsequently, the fidelity of haptic rendering depends significantly on contact space modeling. Contact and friction laws between deformable models are often simplified in up to date methods. They do not allow a "realistic" rendering of the subtleties of contact space physical phenomena (such as slip and stick effects due to friction or mechanical coupling between contacts). In this paper, we use Signorini's contact law and Coulomb's friction law as a computer haptics basis. Real-time performance is made possible thanks to a linearization of the behavior in the contact space, formulated as the so-called Delassus operator, and iteratively solved by a Gauss-Seidel type algorithm. Dynamic deformation uses corotational global formulation to obtain the Delassus operator in which the mass and stiffness ratio are dissociated from the simulation time step. This last point is crucial to keep stable haptic feedback. This global approach has been packaged, implemented, and tested. Stable and realistic 6D haptic feedback is demonstrated through a clipping task experiment.

This paper describes the development of a real-time system for the recognition of a real human subject using the palm dorsa subcutaneous vein pattern (PDSVP) as a physiological biometric feature. The system has been developed using a low-cost, single board computer, called the Raspberry Pi Model B, in conjunction with an infrared sensitive camera, called the Raspberry Pi No Infrared camera, and other components. The camera is sensitive to near infrared (NIR) radiations and this acquisition property has been used to acquire the pattern of vascular structure present in the subcutaneous layer of the dorsum of the human palm. Moreover, an automatic two-axis pan-tilt mechanism has been developed on which the camera is mounted. This is a completely novel mechanism that has been developed so that the data acquisition is independent of the position where the palm dorsum is positioned, as an automatic palm dorsum self-locating strategy is developed using the two-axis pan-tilt mechanism. Now, the NIR images of the PDSVP acquired, in the aforementioned methodology, do not represent the vein pattern with appreciable clarity and discernibility. Therefore, each image acquired undergoes few steps of image preprocessing, to extract the vein pattern, before they are subjected to testing conditions or they are incorporated into the training database. The recognition strategy has been developed using the collaborative representation-based classification. In this paper, we have emphasized upon the most severe case of small sample size, which is single sample per person-based training data set creation. The proposed method is tested on a well-structured database, of NIR images of the PDSVP, JU-NIR-V1: NIR Vein Database, developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India. Subsequently, through extensive experimentation it has been proven that the proposed strategy attains substantially high and stable recognition rate. Moreover, the performance of the recognition strategy is highly robust even in the presence of artifacts, such as angular displacement and scaling, that corrupt the NIR images acquired during data acquisition.

An inspection and classification method for chip solder joints using color grads and Boolean rules

A pose-wise linear illumination manifold model for face recognition using video

Multi-view action recognition based on action volumes, fuzzy distances and cluster discriminant analysis

In this paper, a support vector machine (SVM) pairwise coupling algorithm is developed for classification of satellite communications signals used in second generation of digital video broadcasting via satellite (DVB-S2) standard. DVB-S2 standard adaptively uses one of QPSK, 8PSK, 16APSK, and 32APSK modulations. The proposed method extracts fourth and sixth order cumulants as features from the received signal. The features are given to a SVM pairwise coupling algorithm in which there is one binary SVM for each pair of modulation types. Finally the algorithm selects the modulation type chosen by the maximal number of pairwise SVMs as final decision. SVMs are trained by samples from different modulation types corrupted by Gaussian noise. The simulation results show that the proposed method allows higher recognition rates in comparison with previous methods, especially at low SNRs.

This paper introduces an innovative method, Dynamic Local Feature Analysis (DLFA), for human face recognition. In our proposed method, the face shape and the facial texture information are combined together by using the Local Feature Analysis (LFA) technique. The shape information is obtained by using our proposed adaptive edge detecting method that can reduce the effect on different lighting conditions, while the texture information provides the details of the normalized facial feature on the image. Finally, both the shape and texture information is combined together by means of LFA for dimension reduction. As a result, a high recognition rate is achieved no matter the face is enrolled under different or bad lighting conditions.

This article presents an efficient visual landmark extraction and recognition method that can autonomously and rapidly detect visual features such as objects or groups of small objects, and that can be applied to visual object recognition based SLAM and navigation in indoor/large environments using a monocular/omnidirection vision system. Our method consists of two-stage: (1) we autonomously extract object regions with modified fuzzy object segmentation. We generate a saliency map of the scene based on Modified Phase spectrum of Fourier Transform (mPFT) and extract the final salient object landmark with weighted combination of candidate of objects and saliency map. (2) Using these result, we register current objects as visual landmark and then recognize the current image the scale invariant feature transform (SIFT) - based recognition with probabilistic voting. In experiments results in real indoor and large hall environments, the proposed method was simpler and 10~15% better performance in computation efficiency and successfully extracted salient object landmark in complex environments with high recognition rates. The proposed algorithm can be easily implemented in real-time by reducing the number of objects considered.

<p>Human recognition systems have gained great importance recently in a wide range of applications like access, control, criminal investigation and border security. Ear is an emerging biometric which has rich and stable structure and can potentially be implemented reliably and cost efficiently. Thus human ear recognition has been researched widely and made greatly progress. High recognition rates which are reported in most existing methods can be reached only under closely controlled conditions. Actually a slight amount of rotation and translation which is inescapable would be injurious for system performance. In this paper, a method that uses a transformed type of DCT is implemented to extract meaningful features from ear images. This algorithm is quite robust to ear rotation, translation and illumination. The proposed method is experimented on two popular databases, i.e. USTB II and IIT Delhi II, which achieves significant improvement in the performance in comparison to other methods with good efficiency based on LBP, DSIFT and Gabor. Also because of considering only important coefficients, this method is faster compared to other methods.<em></em></p>

Counters for the various kinds of banknotes require high-speed distinctive point extraction and recognition. In this paper, we propose a new point extraction and recognition algorithm for banknotes. For distinctive point extraction we use a coordinate data extraction method from specific parts of a banknote representing the same color. To recognize banknotes, we trained 5 neural networks. One is for inserting direction and the others are for the face value. The algorithm is designed to minimize recognition time. The simulated results show the high recognition rate and low training period. The proposed method can be applied to high-speed banknote counting machines.

Finger-vein recognition is a biometric technology, which makes use of the pattern of veins in the skin of fingers. Finger-vein recognition is widely employed owing to its high recognition rate and ease of use. A spoof detection method is essential for identifying spoof attacks in biometric-recognition systems. Studies have primarily investigated methods for detecting spoof attacks that utilize image printing or replay using fake finger-vein images. However, no studies have investigated the detection of spoof attacks based on fake finger-vein images generated through a generative adversarial network (GAN), which is commonly implemented in deep-learning technologies. To solve this problem, we newly propose a spoof attack detection method robust against GAN-based fake finger-vein image generation. We verified the use of spoof finger-vein images generated through cycle-consistent adversarial networks (CycleGAN) to attack conventional finger-vein recognition systems and used this finding as the motivation for our research. We also propose a finger-vein spoof-detection method that employs score fusion of ensemble networks based on support vector machine using adaptive moment estimation (Adam) with a sharpness-aware minimization (SAM) optimizer. We conducted experiments using two open databases, ISPR and Idiap, to evaluate the proposed framework. The average classification error rates (ACER) of spoof detection using the proposed method with ISPR and Idiap were 0.37% and 0.23%, respectively. This result confirms the better performance by our method than conventional state-of-the-art methods.

In order to overcome the problem of low recognition rate of building image when constructing three-dimensional model with traditional methods, a new idea of top shape and structure model of assembly building based on BIM is proposed in this paper. The method calculates and identifies the structural parameters of the top contour of assembly building according to BIM technology, retrieves the internal model of the top contour structure of assembly building from the database which is consistent with the result of feature recognition, and obtains the three-dimensional construction of image surface and the three-dimensional modelling of scattered point cloud surface through digital camera and point cloud data, so as to build the final three-dimensional model. Finally, the three-dimensional visualisation of assembly is realised. The experimental results show that the proposed method has high recognition rate.