1,720,983 research outputs found
A DISTRIBUTED ALGORITHM FOR ADAPTIVE REGULATION OF IMAGE-PROCESSING PARAMETERS
No full texthe authors describe an algorithm able to regulate and control the parameters that manage some common processing algorithms, to obtain the best data quality. The processing chain to be regulated consists of an acquisition module and two filtering processes. The system has been implemented as an expert system, which, in addition to the processing units, includes a regulation module. Each regulation module has been specialized with knowledge about the parameters regulating the related processing unit. Criteria to judge the quality of data at each level have been devised to develop methods to select and regulate the processing parameters to be adjusted. The system is described specifying the knowledge-based architecture and single units where knowledge is embedded. The functioning of each processing module is illustrated by defining the parameters used to judge data and those used to regulate the units. Results on a set of indoor images, acquired by using different camera parameters, are reported
Application to locally optimum detection of a new noise model
No full textThe authors discuss the need to provide a realistic model of a generic noise probability density function (PDF), in order to optimize the signal detection in non-Gaussian environments. The target is to obtain a model depending on a few parameters (that are quick and easy to estimate), and is so general that it is able to describe many kinds of noise (e.g., symmetric or asymmetric, with variable sharpness). To this end, a new HOS-based model is introduced, which is derived from the generalized Gaussian function, and depends on three parameters: kurtosis, for representing variable sharpness, and left and right variances (whose combination provides the same information of skewness) for describing the deviation from symmetry. This model is applied to the design of a locally optimum detection (LOD) test. Promising experimental results are presented which are derived from the application of the test to detecting signals corrupted by real underwater acoustic noise
A Hough-based matching of 2D line segments in a monocular image
No full textThe paper describes a method for detecting 2D straight segments and their correspondences in successive frames of an image sequence by means of a Hough-based matching approach. The main advantage of this method is the possibility of extracting and matching 2D straight segments directly in the feature space, without the need for complex matching operations and time-consuming inverse transformations. An additional advantage is that only four attributes of 2D straight segments are required to perform an efficient matching process: position, orientation, length, and midpoint. Tests were performed on both synthetic and real images containing complex manmade objects moving in a scene. A comparison with a well-known 2D Line matching algorithm is also made
Multisensor data fusion for autonomous vehicle navigation in risky environments
No full textThis paper describes a multisensor data-fusion system for driving an autonomous earthwork vehicle operating in a sanitary landfill. The system acquires data from a set of ultrasonic sensors, a laser range finder, and several charge-coupled device cameras, and produces as output alarms that indicate potential dangerous situations, e.g., the presence of fixed or mobile obstacles in the vehicle working area. The proposed system adds to the vehicle important functionalities such as to avoid terrain holes or down slopes or to discriminate between heaps of waste to be compacted and other man-made obstacles. Data fusion allows one to increase the system's reliability and to compensate for the inaccuracies and limited operating ranges of individual sensors. Experimental results show the system's functioning both under normal operational conditions and in the presence of dangerous situations. Moreover, the performances of the system in bad environmental situations (e.g., rain, low lighting) have been evaluated
Statistical morphological skeleton for representing and coding noisy shapes
No full textA new shape descriptor obtained by skeletonisation of noisy binary images is presented. Skeleton extraction is performed by using an algorithm based on a new class of parametrised binary morphological operators, taking into account statistical aspects. Parameters are adaptively selected during the successive iterations of the skeletonisation operation to regulate the characteristics of the shape descriptor. A probabilistic interpretation of the scheduling strategy used for parameters is proposed by analogy to stochastic optimisation techniques. Skeletonisation results on patterns extracted by a change-detection method in a visual-based surveillance application are reported. Results show the greater robustness of the proposed method as compared with other morphological approaches
A real-time model-based method for 3-D object orientation estimation in outdoor scenes
No full textThis letter presents a new method for real-time determination of the three-dimensional (3-D) orientation, i.e., the rotation angle with respect to one of the principal object axes, of a moving known object from a monocular image sequence, The method is composed by three steps: 1) extraction of the morphological skeleton from binary images, 2) projection of the skeleton function on two planes and its analytical approximation by nonuniform rational B-splines, and 3) comparison with a set of data stored into a model. database, Several experiments per-formed on real images prove the method's validity
Location determination using WLAN in conjuction with GPS network (global positioning system)
No full textThis work presents a location determination technique which is based on wireless local area networks (WLANs), in particular the IEEE 802.11b standard, in conjunction with GPS (Global positioning system). Since each of the positioning devices has its shortcomings, this paper proposes to use the data obtained from the GPS system together with the WLAN positioning data to estimate the mobile user's position in a network nearest to its true position. Some of the theoretical work that substantiate the proposed method and initial simulation results are shown as well.This work presents a location determination technique which is based on wireless local area networks (WLANs), in particular the IEEE 802.11b standard, in conjunction with GPS (Global positioning system). Since each of the positioning devices has its shortcomings, this paper proposes to use the data obtained from the GPS system together with the WLAN positioning data to estimate the mobile user's position in a network nearest to its true position. Some of the theoretical work that substantiate the proposed method and initial simulation results are shown as well
A distributed probabilistic system for adaptive regulation of image processing parameters
No full textA distributed optimization framework and its application to the regulation of the behavior of a network of interacting image processing algorithms are presented. The algorithm parameters used to regulate information extraction are explicitly represented as state variables associated with all network nodes. Nodes are also provided with message-passing procedures to represent dependences between parameter settings at adjacent levels. The regulation problem is defined as a joint-probability maximization of a conditional probabilistic measure evaluated over the space of possible configurations of the whole set of state variables (i.e., parameters). The global optimization problem is partitioned and solved in a distributed way, by considering local probabilistic measures for selecting and estimating the parameters related to specific algorithms used within the network. The problem representation allows a spatially varying tuning of parameters, depending on the different informative contents of the subareas of an image. An application of the proposed approach to an image processing problem is described. The professing chain chosen as an example consists of four modules. The first three algorithms correspond to network nodes. The topmost node is devoted to integrating information derived from applying different parameter settings to the algorithms of the chain. The nodes associated with data-transformation processes to be regulated are represented by an optical sensor and two filtering units (for edge-preserving and edge-extracting filterings), and a straight-segment detection module is used as an integration site. Each module is provided with knowledge concerning the parameters to regulate the related processing phase and with specific criteria to estimate data quality. Messages can be bidirectionally propagated among modules in order to search, in a distributed way, for the ''optimum'' set of parameters yielding the best solution. Experimental results obtained on indoor images are presented to show the validity of the proposed approach
A belief-based approach for adaptive image processing
No full textThis paper proposes a new approach to the problem of intelligently regulating image-processing parameters of a distributed network. The proposed approach is based on two-step probabilistic process: (a) belief updating, which consists in computing a functional cost at each node of the network and, (b) belief maximization, which depends on maximizing this functional cost by using a stochastic optimization algorithm. The architecture of an image processing system, consisting of three modules connected in a chain-like structure, is presented as an example showing the capabilities of the proposed approach. Each module is provided with a priori information about the set of parameters that manage a particular data transformation, and with evaluation criteria to judge data quality and to decide on the parameters to be adjusted. Experimental results obtained by using a digitally controlled camera and lens objective, are presented to show the validity of the proposed approach
Adaptive camera regulation for investigation of real scenes
No full textAn adaptive strategy for regulating the intrinsic parameters of a charge coupled device (CCD) visual camera is presented. The parameter regulation procedure is based on the computation of the ''quality'' of an acquired signal by means of a set of functions which estimate the goodness of the datum. Each function performs a quantitative evaluation bg computing several features derived from the high-frequency content of an image or from the histogram analysis. The regulation strategy uses such values to compute the actual values of the camera parameters, i.e., focusing distance, aperture diameter, electronic gain, and black level. Moreover, the same strategy can be applied both to the whole image and to smaller subareas, therefore providing a focus-of-attention mechanism. This is useful for automatic investigation and control of environments subject to dynamically changing conditions (e.g., illumination and structure of a scene), whenever it is not possible to interact directly with such environments
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