Reinforced Palmprint Reconstruction Attacks in Biometric Systems

Biometric signals can be acquired with different sensors and recognized in secure identity management systems. However, it is vulnerable to various attacks that compromise the security management in many applications, such as industrial IoT. In a real-world scenario, the target template stored in the database of a biometric system can possibly be leaked, and then used to reconstruct a fake image to fool the biometric system. As such, many reconstruction attacks have been proposed, yet unsatisfactory naturalness, poor visual quality or incompleteness remains as major limitations. Thus, two reinforced palmprint reconstruction attacks are proposed. Any palmprint image, which can be easily obtained, is used as the initial image, and the region of interest is iteratively modified with deep reinforcement strategies to reduce the matching distance. In the first attack, Modification Constraint within Neighborhood (MCwN) limits the modification extent and suppresses the reckless modification. In the second attack, Batch Member Selection (BMS) selects the significant pixels (SPs) to compose the batch, which are simultaneously modified to a slighter extent to reduce the matching number and the visual-quality degradation. The two reinforced attacks can satisfy all the requirements, which cannot be simultaneously satisfied by the existing attacks. The thorough experiments demonstrate that the two attacks have a highly successful attack rate for palmprint systems based on the most state-of-the-art coding-based methods.

Development of Automated Angiogram Labeling via AI Active Learning Pipeline

Background and Objective: As technology is integrated further into medicine, more specialties are discovering new uses for it in their clinical practice. However, the tasks that we want technology to complete are often removed from developer’s intended tasks. A field of research is growing that integrates medicine with current AI technology to bridge the gap and utilize already existing technology for medical uses. We desire to use an active learning pipeline (a form of machine learning) to automate the labeling of blood vessels on angiograms and potentially develop the ability to detect occlusions. By using machine learning, it would essentially allow the machine to teach itself with human guidance. Methods: A machine learning pipeline is in development for automation of the process. To create a baseline for the machine to start learning, the first set of angiograms are being labeled by hand using the program 3D Slicer. For the first pass, we have been quickly labeling the blood vessels by changing the color sensitivity threshold to highlight the darker blood vessels juxtaposed next to lighter tissue. For the second pass, we have erased any erroneous highlighting that was picked up in the first pass such as tools, tissue, contrast outside the injection site, and sutures. For the third pass, we have labeled and segmented the arteries into specific vessels such as femoral, common iliac, internal iliac, etc. This will then be entered into the machine for automated learning. Results: We are in the process of labeling the initial image set. Potential Impact: By creating a lab for angiogram automation, it will allow physicians to efficiently search images for specific arteries and save valuable time usually spent searching images. This would also allow for automated labeling of occlusions that a physician could then look at to verify.

Digit recognition using decimal coding and artificial neural network

Current artificial neural network image recognition techniques use all the pixels of an image as input. In this paper, we present an efficient method for handwritten digit recognition that involves extracting the characteristics of a digit image by coding each row of the image as a decimal value, i.e., by transforming the binary representation into a decimal value. This method is called the decimal coding of rows. The set of decimal values calculated from the initial image is arranged as a vector and normalized; these values represent the inputs to the artificial neural network. The approach proposed in this work uses a multilayer perceptron neural network for the classification, recognition, and prediction of handwritten digits from 0 to 9. In this study, a dataset of 1797 samples were obtained from a digit database imported from the Scikit-learn library. Backpropagation was used as a learning algorithm to train the multilayer perceptron neural network. The results show that the proposed approach achieves better performance than two other schemes in terms of recognition accuracy and execution time.

Learning to solve sequential physical reasoning problems from a scene image

In this article, we propose deep visual reasoning, which is a convolutional recurrent neural network that predicts discrete action sequences from an initial scene image for sequential manipulation problems that arise, for example, in task and motion planning (TAMP). Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g., first-order logic) with continuous motion planning such as nonlinear trajectory optimization. The action sequences represent the discrete decisions on a symbolic level, which, in turn, parameterize a nonlinear trajectory optimization problem. Owing to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we introduce deep visual reasoning: based on a segmented initial image of the scene, a neural network directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. Our method generalizes to scenes with many and varying numbers of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene and the goal in (segmented) images as input to the neural network, instead of a fixed feature vector. We show that the framework can not only handle kinematic problems such as pick-and-place (as typical in TAMP), but also tool-use scenarios for planar pushing under quasi-static dynamic models. Here, the image-based representation enables generalization to other shapes than during training. Results show runtime improvements of several orders of magnitudes by, in many cases, removing the need to search over the discrete action sequences.

INNV-10. TUMOR TREATING FIELDS (TTF) AS MAINTENANCE THERAPY FOR AN OLIGODENDROGLIOMA CASE AND LONG-TERM FOLLOW-UP

INTRODUCTION Recurrence of low- and high-grade gliomas is observed often after radiation and chemotherapy. Despite continued research on glioma management, there is limited knowledge regarding maintenance treatment after standard care. Tumor Treating Fields as maintenance therapy after radiation and chemotherapy may potentially help prevent recurrence. CASE REPORT In 2011, a 35-year-old male reported headaches to a local ED. A non-enhancing mass was detected on his brain MRI. Observation was advised by his local surgeon. The patient presented with seizures in early 2014, and his brain MRI found the mass developed new enhancement. In late 2014, the patient had multiple seizures, and significant progression of the tumor was discovered. The patient underwent craniotomy and biopsy in the middle cerebral artery distribution area in early 2015. Pathology reported grade II oligodendroglioma; however, the patient only underwent a biopsy, which might have missed the high-grade glioma region. His brain MRI showed a significant enhancing lesion with rapid growth, which was clinically more consistent with grade III oligodendroglioma. Radiation therapy was conducted followed by 6 cycles of PCV chemotherapy throughout 11 months. Post-radiation-chemotherapy brain MRI was stable with a moderate size residual tumor at the right insula region. Following patient wishes, NovoTTF-100A, or Optune, was administered 6 weeks after completion of chemotherapy as maintenance therapy for 2 years to prevent tumor progression. Now, after 3 years being off Optune, the patient is stable without new neurological symptoms or tumor progression. DISCUSSION: Our case highlights the potential effect of Tumor Treating Fields as maintenance therapy after initial radiation and chemotherapy for low- and high-grade oligodendrogliomas. The patient had rapid tumor progression prior to diagnosis and only underwent a biopsy without resection, yet uncommonly, has survived since initial image diagnosis 10 years ago and tissue diagnosis 6 years ago and is fully functional currently.

Intelligent Fuzzy Focusing System For Dark Channel Image Based On Ratio Constraint

The starting point of the establishment of the optimized dark channel algorithm is to stabilize the contrast of the two parts of the image (light and dark). The first step of this method is to divide the initial image into two suitable light and dark parts, and then to obtain the contrast data between the two parts, and the second step is to solve the dark area according to the above optimized operation principle. The third step is to exert its accurate management bright features to the extreme to ensure its contrast stability through the operation of double histogram equalization. Modify the unreasonable arrangement of brightness. Take the CCD or CMOS image sensor as an example, the reference will be shown in the above example according to the lens system, and then the intelligent chip of the non-manually operated focusing device will complete the next processing, transmitting the discrimination result to the front system through the motor, and finally focusing. The intelligent fuzzy focusing is composed of the upper and lower computers, the former is the module of collating, collecting materials and processing all the data, the latter is the management and control module of the evaluation results, and the communication part provides communication for the two. Finally, it can be seen that the optimized dark channel algorithm is obviously better than the de-fog algorithm in terms of the effect of information entropy, brightness and average gradient, which makes the detailed characteristics of being obscured by fog more obvious.

Streamlining the image-guided radiotherapy process for proton beam therapy

Objectives: This work evaluated the on-treatment imaging workflow in the UK’s first proton beam therapy (PBT) centre, with a view to reducing times and unnecessary imaging doses to patients. Methods: Imaging dose and timing data from the first 20 patients (70% paediatrics, 30% TYA/adult) treated with PBT using the initial image-guided PBT (IGPBT) workflow of a 2-dimensional kilo-voltage (2DkV), followed by cone-beam computed-tomography (CBCT) and repeat 2DkV was included. Pearson correlations and Bland-Altman analysis were used to describe correlations between 2DkV and CBCT images to determine if any images were superfluous. Results: 229 treatment sessions were evaluated. Patient repositioning following the initial 2DkV (i2DkV) was required on 19 (8.3%) fractions. This three-step process resulted in an additional mean imaging dose of 3.4 mGy per patient, and 5.1 minutes on the treatment bed for the patient, over a whole course of PBT, compared to a two-step workflow (removing the i2DkV image). Correspondence between the mean displacements from i2DkV and CBCT was high, with R = 0.94, 0.94 and 0.80 in the anteroposterior, superiorinferior and right-left directions, respectively. Bland-Altman analysis showed very little bias and narrow limits of agreement. Conclusions: Removing the i2DkV, streamlining to a two-step workflow, would reduce treatment times and imaging dose, and has been implemented as standard verification protocol. For challenging cases (e.g. paediatric patients under GA), further investigations are required before the three-step workflow can be modified. Advances in knowledge: This is the first report assessing a preliminary imaging protocol in PBT in the UK and determining a way to reduce dose and time, which ultimately benefits the patient.

AUTOMATIC INTEGRATION OF LASER SCANNING AND PHOTOGRAMMETRIC POINT CLOUDS: FROM ACQUISITION TO CO-REGISTRATION

Laser scanning systems have been developed to capture very high-resolution 3D point clouds and consequently acquire the object geometry. This object measuring technique has a high capacity for being utilized in a wide variety of applications such as indoor and outdoor modelling. The Terrestrial Laser Scanning (TLS) is used as an important data capturing measurement system to provide high quality point cloud from industrial or built-up environments. However, the static nature of the TLS and complexity of the industrial sites necessitate employing a complementary data capturing system e.g. cameras to fill the gaps in the TLS point cloud caused by occlusions which is very common in complex industrial areas. Moreover, employing images provide better radiometric and edge information. This motivated a joint project to develop a system for automatic and robust co-registration of TLS data and images directly, especially for complex objects. In this paper, the proposed methods for various components of this project including gap detection from point cloud, calculation of initial image capturing configuration, user interface and support system for the image capturing procedures, and co-registration between TLS point cloud and photogrammetric point cloud are presented. The primarily results on a complex industrial environment are promising.

Automatic road sign detection and recognition based on neural network

Road sign detection and recognition is an integral part of intelligent transportation sys-tems (ITS). It increases protection by reminding the driver of the current condition of the route, such as notices, bans, limitations and other valuable driving information. This paper describes a novel system for automatic detection and recognition of road signs, which is achieved in two main steps. First, the initial image is pre-processed using DBSCAN clustering algorithm. The clustering is performed based on color information, and the generated clusters are segmented using Artificial neural networks (ANN) classifier. The resulting ROIs are then carried out based on their aspect ratio and size to retain only significant ones. Then, a shape-based classification is performed using ANN as classifier and HDSO as feature to detect the circular, rectangular and triangular shapes. Second, a hybrid feature is defined to recognize the ROIs detected from the first step. It involves a combination of the so-called GLBP-Color which is an extension of the classical gradient local binary patterns (GLPB) feature to the RGB color space and the local self-similarity (LSS) feature. ANN, Adaboost and support vector machine (SVM) have been tested with the introduced hybrid feature and the first one is selected as it outperforms the other two. The proposed method has been tested in outdoor scenes, using a collection of common databasets, well known in the traffic sign community (GTSRB, GTSDB and STS). The results demonstrate the effectiveness of our method when compared to recent state-of-the-art methods.

An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters as well as analyzing the sharpness of they themselves measured through image analysis. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW) and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.