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Computational Intelligence Magazine, IEEE

Issue 1 • Date Feb. 2013

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Displaying Results 1 - 21 of 21
  • [Front Cover]

    Page(s): C1
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  • 2014 IEEE World Congress on Computational Intelligence

    Page(s): C2
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  • [Table of contents]

    Page(s): 1
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  • CIM Editorial Board

    Page(s): 2
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  • The "vision" of tomorrow! [Editor's Remarks]

    Page(s): 2
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  • Grand challenges of computational intelligence [President's Message]

    Page(s): 3
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  • CIS Society Officers

    Page(s): 3
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  • IEEE CIS Publications Activities Vision Statement [Society Briefs]

    Page(s): 4 - 5
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  • IEEE CIS Education Activities Vision Statement [Society Briefs]

    Page(s): 6
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  • Obituary for Evangelia Micheli-Tzanakou ("Litsa") [In Memoriam]

    Page(s): 7 - 8
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  • CIS Publication Spotlight [Publication Spotlight]

    Page(s): 9 - 11
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  • IEEE Transactions on Autonomous Mental Development Special Issue on Microdynamics in Interaction: Capturing and Modeling Early Social Learning

    Page(s): 11
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  • 2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications (IEEE CISDA 2012) [Conference Report]

    Page(s): 12 - 13
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  • Special Issue on Computational Intelligence in Computer Vision and Image Processing [Guest Editorial]

    Page(s): 14 - 15
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  • Image Segmentation Using Extended Topological Active Nets Optimized by Scatter Search

    Page(s): 16 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5206 KB) |  | HTML iconHTML  

    Image segmentation is the critical task of partitioning an image into multiple objects. Deformable Models are effective tools aimed at performing image segmentation. Among them, Topological Active Nets (TANs), and their extension, ETANs, are models integrating features of region-based and boundary-based segmentation techniques. Since the deformation of the meshes composing these models to fit the objects to be segmented is controlled by an energy functional, the segmentation task is tackled as a numerical optimization problem. Despite their good performance, the existing ETAN optimization method (based on a local search) can lead to result inaccuracies, that is, local optima in the sense of optimization. This paper introduces a novel optimization approach by embedding ETANs in a global search memetic framework, Scatter Search, thus considering multiple alternatives in the segmentation process using a very small solution population. With the aim of improving the accuracy of the segmentation results in a reasonable processing time, we introduce a global search-suitable internal energy term, a diversity function, a frequency memory population generator and two proper solution combination operators. In particular, these operators are effective in coalescing multiple meshes, a task previous global search methods for TAN optimization failed to accomplish. The proposal has been tested on a mix of 20 synthetic and real medical images with different segmentation difficulties. Its performance has been compared with two ETAN optimization approaches (the original local search and a new multi-start local search) as well as with the state-of-the-art memetic proposal for classical TAN optimization based on differential evolution. Our new method significantly outperformed the other three for the given set of images in terms of four standard segmentation metrics. View full abstract»

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  • Object Detection Using Color Entropies and a Fuzzy Classifier

    Page(s): 33 - 45
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4325 KB) |  | HTML iconHTML  

    This paper proposes a novel approach to specific object detection in complex scenes using color-based entropy features and a fuzzy classifier (FC). Appearances of the detected objects are assumed to contain multiple colors in non-homogeneous distributions that make it difficult to detect these objects using shape features. The proposed detection approach consists of two filtering phases with two different novel color-based entropy features. The first phase filters a test pattern with the entropy of color component (ECC). A self-splitting clustering (SSC) algorithm is proposed to automatically generate clusters in the hue and saturation (HS) color space according to the composing pixels of an object. The ECC value is computed from histograms of pixels in the found clusters and is used to generate object candidates. The second filtering phase uses the entropies of geometric color distributions (EGCD) to filter the object candidates obtained from the first phase. An EGCD is computed for each of the clustered composing colors of a candidate object. The EGCD values are fed to an FC to enable advanced filtering. A new FC using the SSC algorithm and support vector machine (FC-SSCSVM) for antecedent and consequent parameter learning, respectively, is proposed to improve detection performance. Experimental results on the detection of different objects and comparisons with various detection approaches and classifiers verify the advantage of the proposed detection approach using the FC-SSCSVM. View full abstract»

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  • Understanding of GP-Evolved Motion Detectors

    Page(s): 46 - 55
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1917 KB) |  | HTML iconHTML  

    Evolving solutions for machine vision applications has gained more popularity in the recent years. One area is evolving programs by Genetic Programming (GP) for motion detection, which is a fundamental component of most vision systems. Despite the good performance, this approach is not widely accepted by mainstream vision application developers. One of the reasons is that these GP generated programs are often difficult to interpret by humans. This study analyzes the reasons behind the good performance and shows that the behaviors of these evolved motion detectors can be explained. Their capabilities of ignoring uninteresting motions, differentiating fast motions from slow motions, identifying genuine motions from moving background and handling noises are not random. On simplified problems we can reveal the behaviors of these programs. By understanding the evolved detectors, we can consider evolution as a good approach for creating motion detection modules. View full abstract»

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  • Video Behavior Analysis Using Topic Models and Rough Sets [Applications Notes]

    Page(s): 56 - 67
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    The paper investigates the effectiveness of rough sets for video behavior detection. It proposes a new method to make the codebook of the motion frame. It obtains atomic activities via a simple improved topic model. It use a rough sets classifier to classify the behaviors in video, and confirm its performance through experiments. View full abstract»

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  • Variable Hidden Neuron Ensemble for Mass Classification in Digital Mammograms [Application Notes]

    Page(s): 68 - 76
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3312 KB) |  | HTML iconHTML  

    Digital Mammograms are the gold standard for the early detection and diagnosis of breast cancer. Breast cancer is one of the main causes of cancer deaths in women worldwide. One in nine women in Australia will be diagnosed with breast cancer in their lifetime. Women over 50 years of age in particular are encouraged to have screening using digital mammograms so that cancer can be detected at its early stages. Radiologists are able to examine the images by zooming in, changing contrast and brightness and flag any suspicious areas that require further checkup, however in some cases radiologists are unable to spot tumors. Another challenge for radiologists is to classify the tumors once spotted as a benign or malignant diagnosis. This challenge has brought together computer vision and computational intelligence researchers in order to develop new intelligent techniques that can help radiologists. View full abstract»

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  • Ensemble Methods: Foundations and Algorithms [Book Review]

    Page(s): 77 - 79
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  • [Conference Calendar]

    Page(s): 80
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope