Procedures of various feature extractions in synchronous modes, segmentation for the subsequent coding and reconstruction of the initial gray-tone image are realized. The model of a two-dimensional layer describing a ...
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Procedures of various feature extractions in synchronous modes, segmentation for the subsequent coding and reconstruction of the initial gray-tone image are realized. The model of a two-dimensional layer describing a one-layer (one-component) distributed neuron-like system is used for the computer simulation. The system consists of active neuron-like elements with a nonlocal coupling function between them. Analysis of possible collective activity patterns in such a system is carried out. The developed algorithm of image segmentation and reconstruction belongs to the class of synchronous data-processingalgorithms.
The architecture of a data dividing based airborne SAR real-time imaging system is discussed, and its merit and const merit are analyzed. Aimed at RD imaging algorithms, the analysis of real-time based on granularity ...
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The architecture of a data dividing based airborne SAR real-time imaging system is discussed, and its merit and const merit are analyzed. Aimed at RD imaging algorithms, the analysis of real-time based on granularity is proposed, and the implementation of a real-time imaging system is given. The imaging results with raw data are in agreement with the theoretical analysis.
The SCHEMA Network of Excellence aims to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene anal...
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The SCHEMA Network of Excellence aims to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. In this paper, advances in the development of the SCHEMA reference system are reported, focusing on the application of region-based image retrieval using automatic segmentation. More specifically, the integration of four segmentation algorithms and the MPEG-7 eXperimentation Model with the reference system are discussed, along with the motivation behind these and various other choices that were made during the development of the reference system. Experimental results for this system, as well as results for an earlier version of it employing proprietary descriptors, are shown using a common collection of images. Comparative evaluation of these versions, both in terms of retrieval accuracy and in terms of time-efficiency, allows the evaluation of the reference system as a whole as well as the evaluation of the usability of different components integrated with it, such as the MPEG-7 eXperimentation Model. These results illustrate the efficiency of the proposed system, as well as its suitability in serving as a test-bed for evaluating and comparing different algorithms and approaches pertaining to the content-based and semantic manipulation of visual information.
This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that imageprocessing requires a huge amount of computation, mainly at low-level processing where the a...
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This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that imageprocessing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed.
This paper presents a parallel algorithm for histogram image template matching using an SIMD array processor with a hypercube interconnection network. For an N/spl times/N image and M/spl times/M template window, the ...
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This paper presents a parallel algorithm for histogram image template matching using an SIMD array processor with a hypercube interconnection network. For an N/spl times/N image and M/spl times/M template window, the time complexity is shown to be reduced to O(M+log/sup 2/M+logN) as compared to O(N/sup 2/M/sup 2/) for the sequential algorithm, for the N/sup 2/ processing element (PE) multiprocessor systems. Each PE requires only a small local memory. The algorithm is shown to be cost optimal with total cost of computation is O(N/sup 2/M+N/sup 2/log/sup 2/M+N/sup 2/logN).
Clustering is a procedure through which objects are distinguished or classified in accordance with their similarity. The fuzzy c-means method (FCM) is one of the most popular clustering methods based on minimization o...
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Clustering is a procedure through which objects are distinguished or classified in accordance with their similarity. The fuzzy c-means method (FCM) is one of the most popular clustering methods based on minimization of a criterion function. However, the FCM method is sensitive to the presence of noise and outliers in data. This paper introduces a new clustering algorithm by extending the criterion function. As a special case, this algorithm includes the well-known fuzzy c-means method. Performance of the new clustering algorithm is experimentally compared with the FCM method using synthetic data with different clusters and outliers.
In recent years, a number of visual servo control algorithms have been proposed. Most approaches try to solve the inherent problems of image-based and position based servoing by partitioning the control between image ...
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In recent years, a number of visual servo control algorithms have been proposed. Most approaches try to solve the inherent problems of image-based and position based servoing by partitioning the control between image and Cartesian spaces. However, partitioning of the control often causes the Cartesian path to become more complex, which might result in operation close to the joint limits. A solution to avoid the joint limits is to use a shortest-path approach, which avoids the limits in most cases. In this paper, two new shortest-path approaches to visual servoing are presented. First, a position-based approach is proposed that guarantees both shortest Cartesian trajectory and object visibility. Then, a variant is presented, which avoids the use of a 3D model of the target object by using homography based partial pose estimation.
Many learning algorithms have been used for data mining applications, including Support vector Classifiers (SvC), which have shown improved capabilities with respect to other approaches, since they provide a natural m...
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Many learning algorithms have been used for data mining applications, including Support vector Classifiers (SvC), which have shown improved capabilities with respect to other approaches, since they provide a natural mechanism for implementing Structural Risk Minimization (SRM), obtaining machines with good generalization properties. SvC leads to the optimal hyperplane (maximal margin) criterion for separable datasets but, in the non-separable case, the SvC minimizes the L1 norm of the training errors plus a regularizing term, to control the machine complexity. The L 1 norm is chosen because it allows to solve the minimization with a Quadratic Programming (QP) scheme, as in the separable case. But the L 1 norm is not truly an "error counting" term as the Empirical Risk Minimization (ERM) inductive principle indicates, leading therefore to a biased solution. This effect is specially severe in low complexity machines, such as linear classifiers or machines with few nodes (neurons, kernels, basis functions). Since one of the main goals in data mining is that of explanation, these reduced architectures are of great interest because they represent the origins of other techniques such as input selection or rule extraction. Training SvMs as accurately as possible in these situations (i.e., without this bias) is, therefore, an interesting goal. We propose here an unbiased implementation of SvC by introducing a more appropriate "error counting" term. This way, the number of classification errors is truly minimized, while the maximal margin solution is obtained in the separable case. QP can no longer be used for solving the new minimization problem, and we apply instead an iterated Weighted Least Squares (WLS) procedure. This modification in the cost function of the Support vector Machine to solve ERM was not possible up to date given the Quadratic or Linear Programming techniques commonly used, but it is now possible using the iterated WLS formulation. Computer experiments sh
A luminance dependent nonlinear image enhancement algorithm is proposed in this paper. Luma-dependent nonlinear enhancement (LDNE) is a luminance based multi-scale center/surround retinex algorithm, which achieves sim...
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A luminance dependent nonlinear image enhancement algorithm is proposed in this paper. Luma-dependent nonlinear enhancement (LDNE) is a luminance based multi-scale center/surround retinex algorithm, which achieves simultaneous dynamic range compression, color consistency and lightness rendition. Compared to other image enhancement algorithms, LDNE provides better color fidelity, specifies fewer parameters, enhances less noise, prevents the unwanted luminance drop at the uniform luminance areas, keeps the 'bright' background unaffected, and enhances well the 'dark' objects in 'bright' background. To support real time applications, an FPGA based co-processor is designed to perform some of the most computationally intensive operation in LDNE algorithm. Estimation techniques are introduced in the hardware algorithmic designed to achieve faster, simpler and more efficient architecture which is capable of processing more than 21 frames per second.
In this study, an appearance reconstruction method based on extraction of material reflectance properties of a three-dimensional (3D) object from its two-dimensional (2D) images is explained. One of the main advantage...
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In this study, an appearance reconstruction method based on extraction of material reflectance properties of a three-dimensional (3D) object from its two-dimensional (2D) images is explained. One of the main advantages of this system is that the reconstructed object can be rendered in real-time with photorealistic quality in varying illumination conditions. The reflectance of the object is decomposed into diffuse and specular components. White the diffuse component is stored in a global texture, the specular component is represented with a bi-directional reflectance distribution function (BRDF). While estimating the diffuse components, illumination-invariant images of the object are computed from the input images, and a global texture of the object is extracted from these images by using surface particles. The specular reflectance data are collected from the residual images obtained by taking the difference between the input images and corresponding illumination-invariant images, and a BRDF model is fitted to these data. At the rendering phase, the diffuse and specular components are blended into each other to achieve a photorealistic appearance of the reconstructed object.
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