The present work is devoted to the analysis of local objects on radar images. In comparison, the following algorithms are used: decision tree;Bayesian classifier for normal distribution;Nearest neighbor method;Support...
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The present work is devoted to the analysis of local objects on radar images. In comparison, the following algorithms are used: decision tree;Bayesian classifier for normal distribution;Nearest neighbor method;Support vector Method (SvM). As preliminary processing of images provided by a synthetic aperture radar. The research is carried out on the objects from the base of radar images MSTAR. The paper presents the results of the conducted studies.
Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a crime scene. Such fingerprints are usually incomplete or partial, making it challenging to match them to full fingerprints register...
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Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a crime scene. Such fingerprints are usually incomplete or partial, making it challenging to match them to full fingerprints registered in fingerprint databases. Latent fingerprints may contain few minutiae and no singular structures. Matching algorithms that entirely rely on minutiae or alignment of singular structures fail when those structures are missing. This paper presents an approach for matching latent to rolled fingerprints using the (a) similarity of learned representations of patches and (b) the minutiae on the correlated patches. A deep learning network is used to learn optimized representations of image patches. Similarity scores between patches from the latent and reference fingerprints are determined using a distance metric learned with a convolutional neural network. The matching score is obtained by fusing the patch and minutiae similarity scores. The proposed system was tested by matching fingerprints segmented from the 258 latent fingerprints in the NIST SD27 database against a database of 2,257 rolled fingerprints from NIST SD27 and SD4 databases. Experimental results show a rank-1 identification rate of 81.35% and highlights the promise of our proposed approach.
Biomarkers in urine samples are widely used in clinical diagnosis. Involving imageprocessing and data analysis, urinalysis is very popular in hospitals because of its convenience and speediness;and the most important...
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Biomarkers in urine samples are widely used in clinical diagnosis. Involving imageprocessing and data analysis, urinalysis is very popular in hospitals because of its convenience and speediness;and the most important reason is its high accuracy rating. This paper presents colorimetric recognition for urine test device with different algorithms aiming to find a good-performance classifier. Those algorithms can train a set of data and get a model to discriminate the test data. Almost the accuracy of each classifier is beyond 92%, even 99%. Although the classifier that has highest average accurate rate of recognition is K-Nearest Neighbor, we cannot overlook the performance of Support vector Machine, which perform best in protein test. In order to compare these eight algorithms, we use Python simulation to validate the results and show the accuracy of each classifier.
In this paper we developed the algorithm for the automatic tracking of unmanned aircraft for image sequence based on correlation filtering to improve the efficiency of conducting objective control and simulation in Ma...
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ISBN:
(纸本)9781538605219
In this paper we developed the algorithm for the automatic tracking of unmanned aircraft for image sequence based on correlation filtering to improve the efficiency of conducting objective control and simulation in Matlab and C#. The measurement results showed good convergence of the theoretical and experimental data. The advantage of this approach is the possibility to use it in real-time through the application of fast direct and inverse Fourier transform;availability of the implementation in various environments for object-oriented programming such as visual C#/C using the library of structures and algorithms EmguCv/OpenCv and the libraries of algorithms for fast discrete Fourier transform FFTWSharp/FFTW;the possibility of the approach implementing in the systems with reconfigurable integrated circuits based on programmable logic;"Field programmable gate array" (FPGA);the use of the adaptive approach in the program code design. For further improvement of the algorithm it is appropriate to work in the area of window tracking changes due to the deformation or removal of the object to reduce the computational cost.
Depth sensing is useful for a variety of applications that range from augmented reality to robotics. Time-of-flight (TOF) cameras are appealing because they obtain dense depth measurements with low latency. However, f...
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Depth sensing is useful for a variety of applications that range from augmented reality to robotics. Time-of-flight (TOF) cameras are appealing because they obtain dense depth measurements with low latency. However, for reasons ranging from power constraints to multi-camera interference, the frequency at which accurate depth measurements can be obtained is reduced. To address this, we propose an algorithm that uses concurrently collected images to estimate the depth of non-rigid objects without using the TOF camera. Our technique models non-rigid objects as locally rigid and uses previous depth measurements along with the optical flow of the images to estimate depth. In particular, we show how we exploit the previous depth measurements to directly estimate pose and how we integrate this with our model to estimate the depth of non-rigid objects by finding the solution to a sparse linear system. We evaluate our technique on a RGB-D dataset of deformable objects, where we estimate depth with a mean relative error of 0.37% and outperform other adapted techniques.
This paper proposes a semi-supervised learning method based on weakly-labeled data to automatically classify ultrasound (US) thyroid nodules. Key to our new approach is the unification of multi-instance learning (MIL)...
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This paper proposes a semi-supervised learning method based on weakly-labeled data to automatically classify ultrasound (US) thyroid nodules. Key to our new approach is the unification of multi-instance learning (MIL) with deep learning. Benefiting from that, our method can directly use off-the-shelf clinical data, which involves no labels to indicate nodule classes. To this end, we take the US images of a patient as a bag, and take the corresponding pathology report as the bag label. Specifically, we first propose a bag generating method, wherein the detected thyroid nodules are considered as instances corresponding to certain bag. After that, we design an effective EM algorithm to train a convolutional neural network (CNN) for nodule classification. We conduct extensive experiments and comprehensive evaluations on different datasets, and all the experiments confirm that, our method significantly outperforms state-of-the-art MIL algorithms, which exhibits great potential in clinical applications.
In recent years graphical processing units (GPUs) have become a powerful tool in scientific computing. Their potential to speed up highly parallel applications brings the power of high performance computing to a wider...
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In recent years graphical processing units (GPUs) have become a powerful tool in scientific computing. Their potential to speed up highly parallel applications brings the power of high performance computing to a wider range of users. However, programming these devices and integrating their use in existing applications is still a challenging task. In this paper we examined the potential of GPUs for two different applications. The first application, created at Paul Scherrer Institut (PSI), is used for parameter fitting during data analysis of mu SR (muon spin rotation, relaxation and resonance) experiments. The second application, developed at ETH, is used for PET (Positron Emission Tomography) image reconstruction and analysis. Applications currently in use were examined to identify parts of the algorithms in need of optimization. Efficient GPU kernels were created in order to allow applications to use a GPU, to speed up the previously identified parts. Benchmarking tests were performed in order to measure the achieved speedup. During this work, we focused on single GPU systems to show that real time data analysis of these problems can be achieved without the need for large computing clusters. The results show that the currently used application for parameter fitting, which uses OpenMP to parallelize calculations over multiple CPU cores, can be accelerated around 40 times through the use of a GPU. The speedup may vary depending on the size and complexity of the problem. For PET image analysis, the obtained speedups of the GPU version were more than x40 larger compared to a single core CPU implementation. The achieved results show that it is possible to improve the execution time by orders of magnitude. (C) 2017 Elsevier B.v. All rights reserved.
image restoration has been carried out by texture synthesis mostly for large regions and inpainting algorithms for small cracks in images. In this paper, we propose a new approach that allows for the simultaneous fill...
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image restoration has been carried out by texture synthesis mostly for large regions and inpainting algorithms for small cracks in images. In this paper, we propose a new approach that allows for the simultaneous fill-in of different structures and textures by processing in a wavelet domain. A combination of structure inpainting and patch-based texture synthesis is carried out, which is known as patch-based inpainting, for filling and updating the target region. The wavelet transform is used for its very good multiresolution capabilities. The proposed algorithm uses the wavelet domain subbands to resolve the structure and texture components in smooth approximation and high frequency structural details. The subbands are processed separately by the prioritized patch-based inpainting with isophote energy driven texture synthesis at the core. The algorithm automatically estimates the wavelet coefficients of the target regions of various subbands using optimized patches from the surrounding DWT coefficients. The suggested performance improvement drastically improves execution speed over the existing algorithm. The proposed patch optimization strategy improves the quality of the fill. The fill-in is done with higher priority to structures and isophotes arriving at target boundaries. The effectiveness of the algorithm is demonstrated with natural and textured images with varying textural complexions.
Synthetic-aperture radar is usually a complex software and hardware system. It allows obtaining images in radio range, comparable in resolution with optical systems. The advantage of radio waves is that the images are...
Synthetic-aperture radar is usually a complex software and hardware system. It allows obtaining images in radio range, comparable in resolution with optical systems. The advantage of radio waves is that the images are of high quality, despite cloudiness and dark time. The development of algorithms for such systems is a rather complex process. Mathematical modeling applied in purpose to reduce costs. In this paper, we give an overview of early created systems. We discuss the methods for calculating the scattered electromagnetic field. We choose methods that are most suitable for simulating a synthetic aperture radar. Combination of different approximation methods allows us to process large scenes. We take into account the various effects that arise when propagating radio waves. Also, we describe algorithms for a synthesis of radar images. In particular, we consider range-migration algorithm and time-frequency processing algorithm. We show that the frequency-time processing algorithm is preferable for synthesis a radio image in the X-band due to its speed. In opposite, the range-migration effect in P-band is too strong to ignore it. The time-frequency algorithm gives not focused image with serious artifacts. It is better to use the range-migration algorithm for P-band.
Recent biometric research has examined the possibility of obtaining attributes such as hair color, age, gender, weight, ethnicity, height, etc. from biometric traits, face, hand geometry, fingerprints and iris. This p...
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ISBN:
(纸本)9781538619599
Recent biometric research has examined the possibility of obtaining attributes such as hair color, age, gender, weight, ethnicity, height, etc. from biometric traits, face, hand geometry, fingerprints and iris. This paper examines detailed study about different process, for each step of predicting gender from iris for achieving better accuracy and authentication. Capturing the iris image in high quality specification camera is used to achieving specific features of iris. Different algorithms and software systems are used to locate the boundaries of an iris image. Mutual Information(MI) is better used to compare other feature in geometry, texture etc. UND_v and GFI datasets are used to get more accuracy in SvM classifier for better gender prediction.
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