Nowadays there are many computer vision algorithms dedicated to solve the problem of object detection, from many different perspectives. Many of these algorithms take a considerable processing time even for low resolu...
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Reading text from natural images is much more difficult than from scanned text documents since the text may appear in all colors, different sizes and types, often with distorted geometry or textures applied. The paper...
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ISBN:
(纸本)9783319238142;9783319238135
Reading text from natural images is much more difficult than from scanned text documents since the text may appear in all colors, different sizes and types, often with distorted geometry or textures applied. The paper presents the idea of high-speed image preprocessingalgorithms utilizing the quasi-local histogram based methods such as binarization, ROI filtering, line and corners detection, etc. which can be helpful for this task. Their low computational cost is provided by a reduction of the amount of processed information carried out by means of a simple random sampling. The approach presented in the paper allows to minimize some problems with the implementation of the OCR algorithms operating on natural images on devices with low computing power (e.g. mobile or embedded). Due to relatively small computational effort it is possible to test multiple hypotheses e.g. related to the possible location of the text in the image. Their verification can be based on the analysis of images in various color spaces. An additional advantage of the discussed algorithms is their construction allowing an efficient parallel implementation further reducing the computation time.
This paper proposes a novel framework called concatenated image completion via tensor augmentation and completion (ICTAC), which recovers missing entries of color images with high accuracy. Typical images are second-o...
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ISBN:
(纸本)9781509009411
This paper proposes a novel framework called concatenated image completion via tensor augmentation and completion (ICTAC), which recovers missing entries of color images with high accuracy. Typical images are second-or third-order tensors (2D/3D) depending if they are grayscale or color, hence tensor completion algorithms are ideal for their recovery. The proposed framework performs image completion by concatenating copies of a single image that has missing entries into a third-order tensor, applying a dimensionality augmentation technique to the tensor, utilizing a tensor completion algorithm for recovering its missing entries, and finally extracting the recovered image from the tensor. The solution relies on two key components that have been recently proposed to take advantage of the tensor train (TT) rank: A tensor augmentation tool called ket augmentation (KA) that represents a low-order tensor by a higher-order tensor, and the algorithm tensor completion by parallel matrix factorization via tensor train (TMac-TT), which has been demonstrated to outperform state-of-the-art tensor completion algorithms. Simulation results for color image recovery show the clear advantage of our framework against current state-of-the-art tensor completion algorithms.
The MPS approach (Minimal Path Selection) has shown in [1] to provide robust and accurate segmentation of cracks within pavement images compared to other algorithms. As a counterpart, MPS suffers from a large computin...
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The MPS approach (Minimal Path Selection) has shown in [1] to provide robust and accurate segmentation of cracks within pavement images compared to other algorithms. As a counterpart, MPS suffers from a large computing time. In this paper, we present three different ongoing improvements to reduce the computing time and to improve the overall segmentation performance. Most of the work focuses on the first three steps of the algorithm which achieve the segmentation of the crack skeleton. This is at first the improvement of the MPS methodology under Matlab coding, then, the C language MPS version and finally, the first attempt to parallelize MPS under the GPU platform. The results on pavement images illustrate the achieved improvements in terms of better segmentation and faster computational time.
The main goal of works described in the paper is to test and select algorithms to be implemented in the 'SM4Public' security system for public spaces. The paper describes the use of cascading approaches in the...
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ISBN:
(纸本)9783319238142;9783319238135
The main goal of works described in the paper is to test and select algorithms to be implemented in the 'SM4Public' security system for public spaces. The paper describes the use of cascading approaches in the scenario concerning the detection of vehicles in static images. Three feature extractors were used along with benchmark datasets in order to prepare eight various cascades of classifiers. The algorithms selected for feature extraction are Histogram of Oriented Gradients, Local Binary Patterns and Haar-like features. AdaBoost was used as a classifier. The paper briefly introduces the 'SM4Public' system characteristics, characterizes the employed algorithms and presents sample experimental results.
Shadow is formed by the interaction of light with object. Effect of shadow is very crucial in the case of satellite imageprocessing. Roads, buildings, trees etc are detected for various applications. But the interfer...
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ISBN:
(纸本)9781509033492
Shadow is formed by the interaction of light with object. Effect of shadow is very crucial in the case of satellite imageprocessing. Roads, buildings, trees etc are detected for various applications. But the interference of shadow makes mismatching of these objects. Several algorithms are being developed to detect and reconstruct the shadow region. This paper presents a Shadow detection technique based on Niblack segmentation. Niblack segmentation gives better shadow regions compared to Otsu's thresholding method and Sauvola based thresholding. Reconstruction of the shadow region is done by the Bayesian classifier. This classifier generate a training vector and reconstruct non shadow region from shadow region. Posterior probability is determined to reconstruct the non shadow image intensity level. This algorithm is successfully tested with VHSR images.
In this paper the application for generation of HDR image based on two consecutive images (underexposed and overexposed) for Android mobile operating system is presented. The implemented software preserves a lot of im...
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ISBN:
(纸本)9788362065271
In this paper the application for generation of HDR image based on two consecutive images (underexposed and overexposed) for Android mobile operating system is presented. The implemented software preserves a lot of image details and maintains a low execution time. These features are particularly important for pictures taken using mobile devices in emergency situations. Such photos may constitute evidence that a threat occurred, was properly recognized, or someone committed a crime. HDR images can be also used in mobile systems for supporting pedestrians or drivers. Obtained results indicate on a high effectiveness of the presented solution.
In this paper, we proposed new framework for human action representation, which leverages the strengths of convolutional neural networks (CNNs) and the linear dynamical system (LDS) to represent both spatial and tempo...
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ISBN:
(纸本)9781509041183
In this paper, we proposed new framework for human action representation, which leverages the strengths of convolutional neural networks (CNNs) and the linear dynamical system (LDS) to represent both spatial and temporal structures of actions in videos. We make two principal contributions: first, we incorporate image-trained CNNs to detect action clip concepts, which takes advantage of different levels of information by combining the two layers in CNNs trained from images;Second, we further propose adopting a linear dynamical system (LDS) to model the relationships between these clip concepts, which captures temporal structures of actions. We have applied the proposed method on two challenging realistic benchmark datasets, and our method achieves high performance up to 86.16% on the YouTube and 82.76% UCF50 datasets, which largely outperforms most of the state-of-the-art algorithms with more sophisticated techniques.
Symmetric non-negative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection and image segmentation. In this paper, we propose a novel non...
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ISBN:
(纸本)9781509041183
Symmetric non-negative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection and image segmentation. In this paper, we propose a novel nonconvex variable splitting method for solving SymNMF. Different from the existing works, we prove that the algorithm converges to the set of Karush-Kuhn-Tucker (KKT) points of the nonconvex SymNMF problem with a global sublinear convergence rate. We also show that the algorithm can be efficiently implemented in a distributed manner. Further, we provide sufficient conditions that guarantee the global and local optimality of the obtained solutions. Extensive numerical results performed on both synthetic and real data sets suggest that the proposed algorithm yields high quality of the solutions and converges quickly to the set of local minimum solutions compared with other algorithms.
Computational photography systems are becoming increasingly diverse, while computational resources-for example on mobile platforms-are rapidly increasing. As diverse as these camera systems may be, slightly different ...
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ISBN:
(纸本)9781450342797
Computational photography systems are becoming increasingly diverse, while computational resources-for example on mobile platforms-are rapidly increasing. As diverse as these camera systems may be, slightly different variants of the underlying imageprocessing tasks, such as demosaicking, deconvolution, denoising, inpainting, image fusion, and alignment, are shared between all of these systems. Formal optimization methods have recently been demonstrated to achieve state-of-the-art quality for many of these applications. Unfortunately, different combinations of natural image priors and optimization algorithms may be optimal for different problems, and implementing and testing each combination is currently a time-consuming and error-prone process. ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the imageprocessing pipeline, deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate highly efficient solvers that achieve state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.
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