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Selected Topics in Signal Processing, IEEE Journal of

Issue 7 • Date Nov. 2012

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  • Table of contents

    Page(s): C1
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  • IEEE Journal of Selected Topics in Signal Processing publication information

    Page(s): C2
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  • Introduction to the Issue on Filtering and Segmentation With Mathematical Morphology

    Page(s): 737 - 738
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  • Tutorial on Connective Morphology

    Page(s): 739 - 752
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4846 KB) |  | HTML iconHTML  

    Morphological operators may be taken up from the two entries of dilation and of connection. This tutorial focuses on the second entry, and leads to optimum partitionings of the images under study. Five notions, which derive from each other, are successively explored. The “set connection,” which generalizes the usual connectivities, opens the series, and yields the “connective segmentation,” which associates connection with maximum partition. Then the “connected operators” allow to construct “hierarchies of partitions” whose the laws of “optimal cuts” are given. View full abstract»

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  • Random Projection Depth for Multivariate Mathematical Morphology

    Page(s): 753 - 763
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2907 KB) |  | HTML iconHTML  

    The open problem of the generalization of mathematical morphology to vector images is handled in this paper using the paradigm of depth functions. Statistical depth functions provide from the “deepest” point a “center-outward ordering” of a multidimensional data distribution and they can be therefore used to construct morphological operators. The fundamental assumption of this data-driven approach is the existence of “background/foreground” image representation. Examples in real color and hyperspectral images illustrate the results. View full abstract»

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  • Non-Local Morphological PDEs and p -Laplacian Equation on Graphs With Applications in Image Processing and Machine Learning

    Page(s): 764 - 779
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6552 KB) |  | HTML iconHTML  

    In this paper, we introduce a new class of non-local p-Laplacian operators that interpolate between non-local Laplacian and infinity Laplacian. These operators are discrete analogous of the game p -laplacian operators on Euclidean spaces, and involve discrete morphological gradient on graphs. We study the Dirichlet problem associated with the new p-Laplacian equation and prove existence and uniqueness of it's solution. We also consider non-local diffusion on graphs involving these operators. Finally, we propose to use these operators as a unified framework for solution of many inverse problems in image processing and machine learning. View full abstract»

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  • Active Contours on Graphs: Multiscale Morphology and Graphcuts

    Page(s): 780 - 794
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4131 KB) |  | HTML iconHTML  

    In this paper we propose two novel methods for formulating and implementing the methodology of geodesic active contours on arbitrary graphs, as applied to multiscale morphology and segmentation. Firstly, we propose approximations to the calculation of the gradient and the divergence of vector functions defined on graphs and use these approximations to apply the technique of geodesic active contours for object detection on graphs. To this end, we extend existing work on graph morphology to multiscale dilation and erosion and implement them recursively usinglevel sets of functions defined on the graph. Second, we propose a graphcut based solution to the geodesic active contour problem on graphs. Appropriate weights are calculated for each edge for which the Riemannian length of a contour can be approximated by the weighted sum of intersections of the contour with the edges of the graph. Finding the minimum Riemannian length contour then becomes equivalent to solving a max flow problem for which efficient solutions have been proposed in the literature. View full abstract»

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  • First Departure Algorithms and Image Decompositions Into Peaks and Wells

    Page(s): 795 - 808
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4003 KB) |  | HTML iconHTML  

    An image may be decomposed as a difference between an image of peaks and an image of wells. This decomposition depends upon the point of view, an arbitrary set from where the image is considered: a peak appears as a peak if it is impossible to reach it starting from any position in the point of view without climbing. A well cannot be reached without descending. To each particular point of view corresponds a different decomposition. The decomposition is reversible. If one applies a morphological operator to the peaks-and-wells components before applying the inverse transform, one gets a new, transformed image. The decomposition is produced by a generalized shortest path algorithm on weighted graphs; the node weights represent departure times and the arc weights represent traversal times. If a train starts at each node at a time equal to the weight of this node and crosses each arc in a time equal to the weight of the node, the outcome of the algorithm is the earliest departure time of a train from each node: equal to the initial node weight if no other train arrives earlier, and equal to the earliest train coming from another node otherwise. Reconstruction closings or floodings also belong to this family of first departure algorithms. A number of applications illustrate the method. View full abstract»

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  • Salience Adaptive Structuring Elements

    Page(s): 809 - 819
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3276 KB) |  | HTML iconHTML  

    Spatially adaptive structuring elements adjust their shape to the local structures in the image, and are often defined by a ball in a geodesic distance or gray-weighted distance metric space. This paper introduces salience adaptive structuring elements as spatially variant structuring elements that modify not only their shape, but also their size according to the salience of the edges in the image. Morphological operators with salience adaptive structuring elements shift edges with high salience to a less extent than those with low salience. Salience adaptive structuring elements are less flexible than morphological amoebas and their shape is less affected by noise in the image. Consequently, morphological operators using salience adaptive structuring elements have better properties . View full abstract»

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  • Spatially and Intensity Adaptive Morphology

    Page(s): 820 - 829
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    In this paper, spatially and intensity adaptive morphology is introduced and studied in the context of the General Adaptive Neighborhood Image Processing (GANIP) approach. The combination of GAN (General Adaptive Neighborhood)-based filtering and semi-flat morphology is particularly efficient in the sense that the filtering is adaptive to the image spatial structures (structuring elements are spatially variant) and its activity is controlled according to the image intensities (level sets are processed at different scales). The resulting morphological filters show a high image processing performance while preserving the image regions and details without damaging its transitions. The effectiveness of these adaptive operators are practically highlighted on real application examples for image background removing, image restoration and image enhancement. View full abstract»

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  • Efficient Robust d-Dimensional Path Operators

    Page(s): 830 - 839
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1852 KB) |  | HTML iconHTML  

    Path openings and closings are efficient morphological operators that use flexible oriented paths as structuring elements. They are employed in a similar way to operators with rotated line segments as structuring elements, but are more effective at detecting linear structures that are not necessarily locally perfectly straight. While their theory has always allowed paths in arbitrary dimensions, de facto implementations were only proposed in 2D. Recently, a new implementation was proposed enabling the computation of efficient d -dimensional path operators. However this implementation is limited in the sense that it is not robust to noise. Indeed, in practical applications, for path operators to be effective, structuring elements must be sufficiently long so that they correspond to the length of the desired features to be detected. Yet, path operators are increasingly sensitive to noise as their length parameter L increases. To cope with this limitation, we propose an efficient d-dimensional algorithm, the Robust Path Operator, which uses a larger and more flexible family of flexible structuring elements. Given an arbitrary length parameter G, path propagation is allowed if disconnections between two pixels belonging to a path is less or equal to G and so, render it independent of L . This simple assumption leads to constant memory bookkeeping and results in a low complexity. View full abstract»

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  • One-Dimensional Openings, Granulometries and Component Trees in {cal O}(1) Per Pixel

    Page(s): 840 - 848
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2066 KB) |  | HTML iconHTML  

    We introduce a new, efficient and adaptable algorithm to compute openings, granulometries and the component tree for one-dimensional (1-D) signals. The algorithm requires only one scan of the signal, runs in place in O(1) per pixel, and supports any scalar data precision (integer or floating-point data). The algorithm is applied to two-dimensional images along straight lines, in arbitrary orientations. Oriented size distributions can thus be efficiently computed, and textures characterized. Extensive benchmarks are reported. They show that the proposed algorithm allows computing 1-D openings faster than existing algorithms for data precisions higher than 8 bits, and remains competitive with respect to the algorithm proposed by Van Droogenbroeck when dealing with 8-bit images. When computing granulometries, the new algorithm runs faster than any other method of the state of the art. Moreover, it allows efficient computation of 1-D component trees. View full abstract»

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  • Fast Morphological Image Processing Open-Source Extensions for GPU Processing With CUDA

    Page(s): 849 - 855
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1163 KB) |  | HTML iconHTML  

    GPU architectures offer a significant opportunity for faster morphological image processing, and the NVIDIA CUDA architecture offers a relatively inexpensive and powerful framework for performing these operations. However, the generic morphological erosion and dilation operation in the CUDA NPP library is relatively naive, and performance scales expensively with increasing structuring element size. The objective of this work is to produce a freely available GPU capability for morphological operations so that fast GPU processing can be readily available to those in the morphological image processing community. Open-source extensions to CUDA (hereafter referred to as LTU-CUDA) have been produced for erosion and dilation using a number of structuring elements for both 8 bit and 32 bit images. Support for 32 bit image data is a specific objective of the work in order to facilitate fast processing of image data from 3D range sensors with high depth precision. Furthermore, the implementation specifically allows scalability of image size and structuring element size for processing of large image sets. Images up to 4096 by 4096 pixels with 32 bit precision were tested. This scalability has been achieved by forgoing the use of shared memory in CUDA multiprocessors. The vHGW algorithm for erosion and dilation independent of structuring element size has been implemented for horizontal, vertical, and 45 degree line structuring elements with significant performance improvements over NPP. However, memory handling limitations hinder performance in the vertical line case providing results not independent of structuring element size and posing an interesting challenge for further optimisation. This performance limitation is mitigated for larger structuring elements using an optimised transpose function, which is not default in NPP, and applying the horizontal structuring element. LTU-CUDA is an ongoing project and the code is freely available at https://github.com/VictorD/LTU-CUDA. View full abstract»

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  • Classification of Remote Sensing Optical and LiDAR Data Using Extended Attribute Profiles

    Page(s): 856 - 865
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3145 KB) |  | HTML iconHTML  

    Extended Attribute Profiles (EAPs), which are obtained by applying morphological attribute filters to an image in a multilevel architecture, can be used for the characterization of the spatial characteristics of objects in a scene. EAPs have proved to be discriminant features when considered for thematic classification in remote sensing applications especially when dealing with very high resolution images. Altimeter data (such as LiDAR) can provide important information, which being complementary to the spectral one can be valuable for a better characterization of the surveyed scene. In this paper, we propose a technique performing a classification of the features extracted with EAPs computed on both optical and LiDAR images, leading to a fusion of the spectral, spatial and elevation data. The experiments were carried out on LiDAR data along either with a hyperspectral and a multispectral image acquired on a rural and urban area of the city of Trento (Italy), respectively. The classification accuracies obtained pointed out the effectiveness of the features extracted by EAPs on both optical and LiDAR data for classification. View full abstract»

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  • Morphology-Based Crack Detection for Steel Slabs

    Page(s): 866 - 875
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1655 KB) |  | HTML iconHTML  

    Continuous casting is a highly efficient process used to produce most of the world steel production tonnage, but can cause cracks in the semi-finished steel product output. These cracks may cause problems further down the production chain, and detecting them early in the process would avoid unnecessary and costly processing of the defective goods. In order for a crack detection system to be accepted in industry, however, false detection of cracks in non-defective goods must be avoided. This is further complicated by the presence of scales; a brittle, often cracked, top layer originating from the casting process. We present an approach for an automated on-line crack detection system, based on 3D profile data of steel slab surfaces, utilizing morphological image processing and statistical classification by logistic regression. The initial segmentation successfully extracts 80% of the crack length present in the data, while discarding most potential pseudo-defects (non-defect surface features similar to defects). The subsequent statistical classification individually has a crack detection accuracy of over 80% (with respect to total segmented crack length), while discarding all remaining manually identified pseudo-defects. Taking more ambiguous regions into account gives a worst-case false classification of 131 mm within the 30 600 mm long sequence of 150 mm wide regions used as validation data. The combined system successfully identifies over 70% of the manually identified (unambiguous) crack length, while missing only a few crack regions containing short crack segments. The results provide proof-of-concept for a fully automated crack detection system based on the presented method. View full abstract»

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  • Multiple Luminaire Identification in Airborne Images of Airport's Approach Lighting Using Mathematical Morphology With Variable Length Structuring Element

    Page(s): 876 - 885
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    In night aviation, to land an aircraft, a pilot needs to be able to identify an airport. The approach lighting system (ALS) at an airport is used to provide identification and guidance to pilots. ALS consists of more than 100 luminaires which are installed in a defined pattern following strict guidelines by the International Civil Aviation Organization (ICAO). ICAO also has strict regulations for maintaining the performance level of the luminaires. However, to date there is no automated technique by which to monitor the performance of the lighting. We suggest using images of the lighting pattern captured using a camera placed inside an aircraft. Based on the information contained within these images, the performance of the luminaires has to be evaluated which requires identification of over 100 luminaires within the pattern of ALS image. This research proposes analysis of the pattern using morphology filters which use a variable length structuring element (VLSE). The dimension of the VLSE changes continuously within an image and varies for different images. A novel technique for automatic determination of the VLSE is proposed and it allows successful identification of the luminaires from the image data as verified through the use of simulated and real data. View full abstract»

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  • IEEE Journal of Selected Topics in Signal Processing information for authors

    Page(s): 886 - 887
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  • Open Access [advertisement]

    Page(s): 888
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  • IEEE Signal Processing Society Information

    Page(s): C3
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Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

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Meet Our Editors

Editor-in-Chief
Fernando Pereira
Instituto Superior Técnico