HOMER: Cryptography based Currency Detection System for Visually Impaired People

IoT Enabled Automated Object Recognition for the Visually Impaired

Adaptive ORB feature detection with a variable extraction radius in RoI for complex illumination scenes

PurposeThis paper aims to propose a real-time augmented reality (AR)-based assembly assistance system using a coarse-to-fine marker-less tracking strategy. The system automatically adapts to tracking requirement when the topological structure of the assembly changes after each assembly step.Design/methodology/approachThe prototype system’s process can be divided into two stages: the offline preparation stage and online execution stage. In the offline preparation stage, planning results (assembly sequence, parts position, rotation, etc.) and image features [gradient and oriented FAST and rotated BRIEF (ORB)features] are extracted automatically from the assembly planning process. In the online execution stage, too, image features are extracted and matched with those generated offline to compute the camera pose, and planning results stored in XML files are parsed to generate the assembly instructions for manipulators. In the prototype system, the working range of template matching algorithm, LINE-MOD, is first extended by using depth information; then, a fast and robust marker-less tracker that combines the modified LINE-MOD algorithm and ORB tracker is designed to update the camera pose continuously. Furthermore, to track the camera pose stably, a tracking strategy according to the characteristic of assembly is presented herein.FindingsThe tracking accuracy and time of the proposed marker-less tracking approach were evaluated, and the results showed that the tracking method could run at 30 fps and the position and pose tracking accuracy was slightly superior to ARToolKit.Originality/valueThe main contributions of this work are as follows: First, the authors present a coarse-to-fine marker-less tracking method that uses modified state-of-the-art template matching algorithm, LINE-MOD, to find the coarse camera pose. Then, a feature point tracker ORB is activated to calculate the accurate camera pose. The whole tracking pipeline needs, on average, 24.35 ms for each frame, which can satisfy the real-time requirement for AR assembly. On basis of this algorithm, the authors present a generic tracking strategy according to the characteristics of the assembly and develop a generic AR-based assembly assistance platform. Second, the authors present a feature point mismatch-eliminating rule based on the orientation vector. By obtaining stable matching feature points, the proposed system can achieve accurate tracking results. The evaluation of the camera position and pose tracking accuracy result show that the study’s method is slightly superior to ARToolKit markers.

Increased use of 3D digitization methods leads to large numbers of 3D models with script like cuneiform tablets, temple wall inscriptions from the Mayan empire and Minoan and Mycenaean seal imprints. Analyzing and in particular comparing those objects in our projects is a core challenge for robust symbol-spotting and transcription of ancient scripts. The discrete manifolds using irregular grids describe the objects' surface and contain complex shapes that cannot be wholly captured by a single rendering to a two dimensional plane in an image format. As the plethora of standard image descriptor extraction methods is not available on those 3D meshes we propose a novel approach adapting relevant image descriptor methods to process irregular grids. We show preliminary evaluations of our approach by performing a symbol spotting task on cuneiform tablets. Our approach proceeds in three stages: (i) We compute a local surface feature with Multi-scale Integral Invariants (MSII), (ii) for each vertex we extract radial geodesic patches which are embedded locally into a polar coordinate system, (iii) a rectangular sampling scheme converts these patches into raster image patches that are accessible to 2D image descriptors. For the symbol spotting task we compute Histogram of Oriented Gradients (HOG) and Oriented FAST and Rotated BRIEF (ORB) descriptors on the query mesh and document mesh for each vertex. Similar symbols are retrieved by sliding a geodesic patch along the document mesh. Like in many other Cultural Heritage applications we have to consider the lack of any ground-truth. Therefore we provide a qualitative analysis of the spotting results showing that a query mesh is reliably found on the document mesh.

The determination of the currency denomination is an issue in paper currency recognition system. This paper proposes a robust method to recognize the paper currency using the pattern matching. In the proposed algorithm a similarity measure is used to classify the currency based on the similarity of the extracted features. To evaluate the performance of the proposed method experiments were conducted over 200 of currencies of different denominations. On performing the experiment the proposed method gives 97% accuracy.

Purpose: The purpose of the study was to find out the preferred, accurate, and effective way to identify the new Indian currency note by a blind individual. Methods: A cross-sectional study was conducted in two phases among 30 subjects in a school for the blind. In phase 1, comparison was done in terms of accuracy and the time taken to identify the Indian currency between the old and new currency notes by using the tactile method by the blind individuals. In phase 2, comparison was done same as phase 1, with the addition of two more note identification methods, one was by using Mani note identifier application via smart phone and the other was Notex. The accuracy and time were analyzed and compared for all three used methods for both phases. Results: Thirty blind individuals were included in the study. The mean age of the subjects was 29.43 years (±10.88 years) and the age range was 18–54 years. The total time taken to identify all the currency notes in each category was recorded in seconds. The average time taken with the help of tactile sense was 75.34 s, but the accuracy here was very poor, even many currencies went unidentified. The average time taken with the help of Notex was 136.54 s. The average time taken with the help of Mani mobile note identifier software was 774.07 s. Conclusion: Notex was the most effective method of identifying Indian currency as compared to the other two methods that were used in our study.

This work presents a method for detection of fuel and charging stations. Automobiles may run out of fuel and leave people stranded. Detection of fuel and charging stations plays an important role in driver assistance system. This paper uses combined approach of computer vision technique and machine learning algorithms. Proposed system alerts users by sending an audio message when fuel stations are detected. Combination of Binary Robust Invariant Scalable Key points (BRISK) and Oriented FAST and Rotated BRIEF (ORB) methods is deployed for extraction of features. A blend of K-Means and principal component analysis (PCA) is used for reducing size of the feature vector. Accuracy of soft voting method with five classifiers is 77.90%. Random forest algorithm provided higher accuracy of 82.26% and 83% F1 score.

The visually challenged people lack the ability to perform activities for daily living in general and instrumental daily living activities in particular. The major problems faced by visually challenged people and elderly people with low vision in pursuing money transactions are to distinguish between an original and fake currency note and identify the correct denomination of the given currency note. This paper proposes a hand held portable device that implements a currency recognition algorithm developed using customized Convolution neural networks. The proposed device has the ability to distinguish between original and fake currency notes and recognize the denomination of the currency notes. Further, it sends the information regarding the currency denomination by an audio feedback to the visually challenged user thus enabling him to do money transactions on his own. The performance of the proposed system is verified under real time by giving it to the actual user and the accuracy of the proposed system is found to be 97.4% executed within 10 s of time.

Currency has become absolutely necessary part in human life. At present recognition of counterfeit and real currency notes is very important. If the application learns to classify the denominations of the currency and detect the counterfeit currency then it will improve the accuracy of the recognition and confidence of the people. Implementation of deep learning techniques on various applications is increasing rapidly, and now many researchers are using deep learning techniques for the applications related to currency notes. In this paper various techniques which have been used for extracting the features from currency note, recognizing the currency note and detecting the counterfeit note have been presented.

Most commercial transactions are still done using physical currencies. Detection of fake currency can improve the reliability of ATMs and counting machines in order to ensure proper maintenance operations and confirm the value and authenticity of currency. Many paper currencies or banknotes are exposed to the presence of some problems, such as folding, rolling, and wrinkling. In this research, we study and analyse the impact of applying one of the currency recognition algorithms, which is speeded up robust scale invariant feature transform (SR-SIFT) algorithm in detecting the currency paper in the normal presentation and also in any troubles like rolled, wrinkled, or folded. The results show that the SR-SIFT algorithm can usefully recognise the currencies in a different situation with high accuracy.

Microbial contamination plays a massive role in spreading diseases globally. Bacterial contamination of gram- positive, spore-forming bacteria, gram-negative rods, and fungus was tested on Indian rupee notes of various denominations. Bacteria and fungus were identified on almost all of the cash notes that were analyzed. Overall, our findings indicated that banknotes are contaminated with pathogenic germs and intestinal microorganisms and could be a source of severe infection. The present study recommends that paper currency be converted into plastic currency notes to avoid infections. Keywords: Contamination, fomites, Indian currency notes, infectious diseases, bacteria, fungi.

The currency has a significant meaning in everyday life. Researchers have suggested diverse approaches to improve currency recognition for visually impaired people by implementing portable devices and various image-processing techniques. However, only some of the nearly developed currency recognition systems cater to meet the Sri Lankan currency identification requirement. This paper presents a model that works on a mobile application based on a Convolutional Neural Network using a pre-trained model to identify currency notes in real time from a smartphone camera. Crucial factors in constructing this model are speed, accuracy, and size. A self-collected dataset of 14,000 Sri Lankan currency note images in numerous conditions was used for primary model training. Data augmentation, transfer learning through VGG16, VGG19, ResNet50, DenseNet, MobileNet, NASNet, and EfficientNet pre-trained models using bottleneck features and fine-tuning on top of bottleneck features are the main methods used in this model. This model reached above 98% in terms of testing accuracy using DenseNet121 with fine-tuning by testing 315 partially occulted, rotated Sri Lankan currency notes in different daylight conditions and cluttered backgrounds. The proposed model can recognize the currency note using TensorFlow Lite when it is pointed at the camera. The present research is limited to daylight image capturing to ensure the better performance of the model. The findings of this research would help visually impaired people to recognize Sri Lankan currency notes precisely and with less effort through the simple installation of a smartphone.

Breast cancer is one of the most common cancer types especially met in women. The number of breast cancer patients increases every year. Thus, to detect breast cancer at its early stages gains importance. Breast region detection is the pioneering step of breast cancer diagnosis researches performed via image processing techniques. The performance of computer-aided breast cancer diagnosis systems can be improved by exactly determining the breast region of interest. In this study, the goal is to determine a region of interest for breast MR images, in which one or more lesion can appear. The achieved region includes two breasts and lymph nodes. The proposed region of interest detection system is fully automatic and it utilizes several image processing techniques. At first, the local adaptive thresholding technique is applied to the noise-filtered grey level breast magnetic resonance images taken with ethical permissions from Sakarya Education and Research Hospital. After adaptive thresholding, connected component analysis is performed to exclude extra structures around the breast region as thorax area. This analysis selects the largest area in the binary image which corresponds to a gyrate region including breast area and lymph nodes over the backbone. Then, the integral of horizontal projection is calculated to determine an optimum horizontal line that allows setting the region of interest apart. In the following step, sternum midpoint is detected to separate the right breast from the left one. Finally, a masking operation is applied to get corresponding right and left breast regions in the original MR image. To evaluate the performance of the proposed study, the results of automatic region of interest detection system are compared with the manual region of interest selection performed by an expert radiologist. Dice similarity coefficient and Jaccard coefficient are used as performance criteria. According to the results, the proposed system can detect region of interest for computer-aided breast cancer diagnosis researches, exactly.

This paper proposes a teleophthalmology support system in which we use algorithms of object detection and semantic segmentation, such as faster region-based CNN (FR-CNN) and SegNet, based on several CNN architectures such as: Vgg16, MobileNet, AlexNet, etc. These are used to segment and analyze the principal anatomical elements, such as optic disc (OD), region of interest (ROI) composed by the macular region, real retinal region, and vessels. Unlike the conventional retinal image quality assessment system, the proposed system provides some possible reasons about the low-quality image to support the operator of an ophthalmoscope and patient to acquire and transmit a better-quality image to central eye hospital for its diagnosis. The proposed system consists of four steps: OD detection, OD quality analysis, obstruction detection of the region of interest (ROI), and vessel segmentation. For the OD detection, artefacts and vessel segmentation, the FR-CNN and SegNet are used, while for the OD quality analysis, we use transfer learning. The proposed system provides accuracies of 0.93 for the OD detection, 0.86 for OD image quality, 1.0 for artefact detection, and 0.98 for vessel segmentation. As the global performance metric, the kappa-based agreement score between ophthalmologist and the proposed system is calculated, which is higher than the score between ophthalmologist and general practitioner.

In this paper we will present a computer-aided detection (CAD) system for mass detection and classification. This CAD system performs mass detection on regions of interest (ROIs) and performs the normal-abnormal classification on detected masses. To enhance the ROIs, histogram equalization and average filtering were employed. ROIs or suspicious lesions were detected by an edge-base segmentation technique. Each ROI was represented by 12 textural features calculated from spatial gray-level co-occurrence matrix (GLCM). Finally, a back propagation neural network was used to classify a ROI to normal or abnormal one. Performance of the proposed system was analyzed in mini-MIAS data set by means of receiver operating characteristics curve (ROC curve) and free-response ROC curve (FROC curve). We archived area under the ROC curve AZ = 0,815. This value lied in the range from 0.7 to 0.9 so that our CAD system could be considered accurate enough.