In imageprocessing measuring and valuing a distance between two points is important. The obtained values can be used for determining whether two points are close to each other or to define weights for a filter concen...
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ISBN:
(纸本)9781509026616
In imageprocessing measuring and valuing a distance between two points is important. The obtained values can be used for determining whether two points are close to each other or to define weights for a filter concentrated around a central element. While there are measures of proximity, neither of them was defined with a such use in mind, mostly concentrating on problem of an optimization. The idea is to turn the Euclidean distance between two points into a measure of how close (or far) two points are from each other, basing on two given ranges. The function was mostly obtained by a theoretical analysis supported with a mathematical calculation and examples of use. As it was proven in the work, the obtained function can be implemented not only to measure proximity, but also as a flexible kernel for image filters, allowing for blurring or edge-detection.
Face recognition, although being a popular area of research and study, still has many challenges, and with the appearance of the Microsoft Kinect device, new possibilities of research were uncovered, one of which is f...
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ISBN:
(纸本)9781467395564
Face recognition, although being a popular area of research and study, still has many challenges, and with the appearance of the Microsoft Kinect device, new possibilities of research were uncovered, one of which is face recognition using the Kinect. With the goal of enhancing face recognition, this paper is aiming to prove how depth maps, since not effected by illumination, can improve face recognition with a benchmark algorithm based on the Eigenface. This required some experiments to be carried out, mainly in order to check if algorithms created to recognize faces using normal images can be as effective if not more effective with depth map images. The OpenCV Eigenface algorithm implementation was used for the purpose of training and testing both normal and depth-map images. Finally, results of the experiments are presented to prove the ability of the tested algorithm to function with depth maps, also, proving the capability of depth maps face recognition's task in poor illumination.
The proceedings contain 19 papers. The special focus in this conference is on Advances in Mechanical Engineering. The topics include: Frameworks not restorable from self stresses;structural modifications synthesis of ...
ISBN:
(纸本)9783319295787
The proceedings contain 19 papers. The special focus in this conference is on Advances in Mechanical Engineering. The topics include: Frameworks not restorable from self stresses;structural modifications synthesis of bennett mechanism;kinematic research of bricard linkage modifications;drive selection of multidirectional mechanism with excess inputs;engineering calculations of bolt connections;modern methods of contact forces between wheelset and rails determining;a novel design of an electrical transmission line inspection machine;one stable scheme of centrifugal forces dynamic balance;new effective data structure for multidimensional optimization orthogonal packing problems;one-dimensional models in turbine blades dynamics;stationary oscillation in two-mass machine aggregate with universal-joint drive;the vibrations of reservoirs and cylindrical supports of hydro technical constructions partially submerged into the liquid;mathematical modelling of interaction of the biped dinamic walking robot with the ground;programmable movement synthesis for the mobile robot with the orthogonal walking drivers;processing of data from the camera of structured light for algorithms of image analysis in control systems of mobile robots;structural and phase transformation in material of steam turbines blades after high-speed mechanical effect;stress corrosion cracking and electrochemical potential of titanium alloys;metal flow control at processes of cold axial rotary forging and use of the capabilities of acoustic-emission technique for diagnostics of separate heat exchanger elements.
The aim of this article is to present a method to detect visual objects from color digital images by volumetric segmentation. We will discuss algorithms for visual and multimedia computing. The problem of partitioning...
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ISBN:
(纸本)9781509034307
The aim of this article is to present a method to detect visual objects from color digital images by volumetric segmentation. We will discuss algorithms for visual and multimedia computing. The problem of partitioning images into homogenous regions or semantic entities is a basic problem for identifying relevant objects. The presented method is a general-purpose volumetric segmentation method and it produces results from two different perspectives: (a) from the perspective of perceptual grouping of regions from the images, and also (b) from the perspective of determining regions if the input spatial images contain visual objects. We present a unified framework for volumetric image segmentation and prism cells used is the first run into volumetric segmentation algorithms. The major concept used in graph-based volumetric segmentation method is the concept of homogeneity of volumes and thus the edge weights are based on color distance. The complexity of our original algorithm for volumetric segmentation is linear.
For optical remote sensing images, an effective method to reduce or eliminate the impact of clouds is important. With big data input and real-time processing demands, efficient parallelization strategies are essential...
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For optical remote sensing images, an effective method to reduce or eliminate the impact of clouds is important. With big data input and real-time processing demands, efficient parallelization strategies are essential for high performance computing on multi-core systems. This paper proposes an efficient high performance parallel computing framework for cloud filtering and smoothing. A comparison and benchmarking of two parallel algorithms for cloud filtering that incorporates spatial smoothing solved by two-dimensional dynamic programming is implemented. The experiments were carried out on an NVIDIA GPU accelerator with evaluations of approximation, parallelism and performance. The test results show significant performance improvements with high accuracy compared with sequential CPU implementation, and can be applied to other multi-core systems.
In this paper, distributed optimization problem is investigated under a second-order multi-agent network, in which each agent is described as the double integrator. The multi-agent network is introduced for solving a ...
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ISBN:
(纸本)9781509009107
In this paper, distributed optimization problem is investigated under a second-order multi-agent network, in which each agent is described as the double integrator. The multi-agent network is introduced for solving a large scale optimization problem by the cooperation of coupled agents. Based on the interaction over the network, the optimal solution of the problem can be obtained. Since the existing distributed algorithms for second-order multi-agent network enforce each agent to transmit complete information(both state information and derivation information of the independent variable, i.e., corresponded position and velocity information of the agent), this paper is motivated to design the distributed algorithm with only using the position information of neighbors, which reduces the requirement on communication bandwidth. With the help of Lyapunov analysis and La Sallel's Invariance Principle, the optimal solution is derived and the optimization problem is solved via the second-order multi-agent network. Finally, a numerical example is presented to illustrate the theoretical result.
This paper describes the mapping and the acceleration of an object detection algorithm on a multiprocessor system based on an FPGA. We use HOG (Histogram of Oriented Gradients), one of the most popular algorithms for ...
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ISBN:
(纸本)9781509030859
This paper describes the mapping and the acceleration of an object detection algorithm on a multiprocessor system based on an FPGA. We use HOG (Histogram of Oriented Gradients), one of the most popular algorithms for detection of different classes of objects and currently being used in smart embedded systems. The use of HOG on such systems requires efficient implementations in order to provide high performance possibly with low energy/power consumption budgets. Also, as variations and adaptations of this algorithm are needed to deal with different scenarios and classes of objects, programmability is required to allow greater development flexibility. In this paper we show our approach towards implementing the HOG algorithm into a multi-softcore Nios II based-system, bearing in mind high-performance and programmability issues. By applying source-to-source transformations we obtain speedups of 19× and by using pipelined processing we reduce the algorithms execution time 49×. We also show that improving the hardware with acceleration units can result in speedups of 72.4× compared to the embedded baseline application.
The explosion of computational imaging has seen the frontier of imageprocessing move past linear problems, like denoising and deblurring, and towards non-linear problems such as phase retrieval. There has a been a co...
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ISBN:
(纸本)9781509015535
The explosion of computational imaging has seen the frontier of imageprocessing move past linear problems, like denoising and deblurring, and towards non-linear problems such as phase retrieval. There has a been a corresponding research thrust into non-linear image recovery algorithms, but in many ways this research is stuck where linear problem research was twenty years ago: Models, if used at all, are simple designs like sparsity or smoothness. In this paper we use denoisers to impose elaborate and accurate models in order to perform inference on generalized linear systems. More specifically, we use the state-of-the-art BM3D denoiser within the Generalized Approximate Message Passing (GAMP) framework to solve compressive phase retrieval. Our method demonstrates recovery performance equivalent to existing techniques using fewer than half as many measurements. This dramatic improvement in compressive phase retrieval performance opens the door for a whole new class of imaging systems.
image filtering is a key technology in imageprocessing applications for de-noising corrupted images. Digital images are often polluted by noise during capturing and hence they may not show the features or colors clea...
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ISBN:
(纸本)9781509006274
image filtering is a key technology in imageprocessing applications for de-noising corrupted images. Digital images are often polluted by noise during capturing and hence they may not show the features or colors clearly. image filtering removes the noise in an image and improves the contrast to provide better input for various imageprocessing applications. This paper proposes an efficient image filtering technique using fuzzy logic. The proposed method employs fuzzy membership functions in order to replace the noisy pixels based on the degree of membership of the neighboring pixels within a filter mask. Experimental results confirm that our method is very effective and fast for removing impulsive noise while preserving the small and sharp details in the image.
The paper analyzes well-known automated microscopy systems. The work presents the comparative analysis of low- and middle-designed algorithms. The adaptive module of pre-processing and image segmentation has been work...
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ISBN:
(纸本)9781509014149
The paper analyzes well-known automated microscopy systems. The work presents the comparative analysis of low- and middle-designed algorithms. The adaptive module of pre-processing and image segmentation has been worked out.
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