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Computer Vision, IET

Issue 3 • Date 6 2015

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Displaying Results 1 - 13 of 13
  • Kernel-based sliding mode control for visual servoing system

    Publication Year: 2015 , Page(s): 309 - 320
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (946 KB)

    In this study, a new approach to design a controller for a visual servoing (VS) system is proposed. Kernel-measurement is used to track the motion of a featureless object which is defined as sum of weighted-image value through smooth kernel functions. This approach was used in kernel-based VS (KBVS). To improve the tracking error and expand the stability region, sliding mode control is integrated ... View full abstract»

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  • Cascade of forests for face alignment

    Publication Year: 2015 , Page(s): 321 - 330
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (605 KB)

    In this study, we propose a regression forests-based cascaded method for face alignment. We build on the cascaded pose regression (CPR) framework and propose to use the regression forest as a primitive regressor. The regression forests are easier to train and naturally handle the over-fitting problem via averaging the outputs of the trees at each stage. We address the fact that the CPR approaches ... View full abstract»

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  • Log-Euclidean bag of words for human action recognition

    Publication Year: 2015 , Page(s): 331 - 339
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (428 KB)

    Representing videos by densely extracted local space-time features has recently become a popular approach for analysing actions. In this study, the authors tackle the problem of categorising human actions by devising bag of words (BoWs) models based on covariance matrices of spatiotemporal features, with the features formed from histograms of optical flow. Since covariance matrices form a special ... View full abstract»

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  • Continuous camera placement using multiple objective optimisation process

    Publication Year: 2015 , Page(s): 340 - 353
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (1070 KB)

    Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility is inadequate for continuous and automated tracking. In such applications, a sufficient overlap between FOVs should be secured so that camera handoff can be executed successfully and automatically befor... View full abstract»

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  • Calibration and object correspondence in camera networks with widely separated overlapping views

    Publication Year: 2015 , Page(s): 354 - 367
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (1374 KB)

    This study contributes in two ways to the research of multi-camera object tracking in the context of visual surveillance. Firstly, a semi-automatic scene calibration method is proposed to deal with mapping a network of cameras with overlapped fields of view onto a single ground plane view, even when the overlap is not substantial. The proposed method uses a semi-supervised approach that combines t... View full abstract»

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  • Adaptive learning region importance for region-based image retrieval

    Publication Year: 2015 , Page(s): 368 - 377
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (1208 KB)

    This study addresses the issue of region representation in region-based image retrieval (RBIR). In order to reduce the user's burden of selecting the region of interest, a statistical index called visual region importance (RI) is constructed to describe the region. By learning from user's current and historical feedback information, visual RI can be automatically updated and semantic RI can be obt... View full abstract»

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  • Calibration of multiple fish-eye cameras using a wand

    Publication Year: 2015 , Page(s): 378 - 389
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (850 KB)

    Fish-eye cameras are becoming increasingly popular in computer vision, but their use for three-dimensional measurement is limited partly because of the lack of an accurate, efficient and user-friendly calibration procedure. For such a purpose, the authors propose a method to calibrate the intrinsic and extrinsic parameters (including radial distortion parameters) of two/multiple fish-eye cameras s... View full abstract»

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  • Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns

    Publication Year: 2015 , Page(s): 390 - 399
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (1215 KB)

    This study proposes an illumination-invariant face-recognition method called adaptive homomorphic eight local directional pattern (AH-ELDP). AH-ELDP first uses adaptive homomorphic filtering to reduce the influence of illumination from an input face image. It then applies an interpolative enhancement function to stretch the filtered image. Finally, it produces eight directional edge images using K... View full abstract»

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  • Active learning combining uncertainty and diversity for multi-class image classification

    Publication Year: 2015 , Page(s): 400 - 407
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (649 KB)

    In computer vision and pattern recognition applications, there are usually a vast number of unlabelled data whereas the labelled data are very limited. Active learning is a kind of method that selects the most representative or informative examples for labelling and training; thus, the best prediction accuracy can be achieved. A novel active learning algorithm is proposed here based on one-versus-... View full abstract»

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  • Using statistical characteristics of gradient phases for robust face recognition under illumination variations

    Publication Year: 2015 , Page(s): 408 - 418
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (647 KB)

    Gradient phase, which is treated as an illumination insensitive measure, is an important feature for visual detection and recognition applications, especially under illumination variations. However, fewer statistical characteristics of the gradient phase have been reported till now. First, the statistical characteristics of the gradient phase against gradient signal-to-noise ratios (gradient SNRs)... View full abstract»

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  • Recognition improvement through the optimisation of learning instances

    Publication Year: 2015 , Page(s): 419 - 427
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (1402 KB)

    Image recognition based on machine learning has been widely utilised in the computer vision field. In the image recognition process, quite a few positive and negative instances are needed for effective machine learning. However, some invalid instances selected from the instance candidates, particularly for negative instances, will result in reduced image recognition accuracy and wasted resources. ... View full abstract»

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  • Particle filter framework for salient object detection in videos

    Publication Year: 2015 , Page(s): 428 - 438
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (1107 KB)

    Salient object detection in videos is challenging because of the competing motion in the background, resulting from camera tracking an object of interest, or motion of objects in the foreground. The authors present a fast method to detect salient video objects using particle filters, which are guided by spatio-temporal saliency maps and colour feature with the ability to quickly recover from false... View full abstract»

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  • Spatial codification of label predictions in multi-scale stacked sequential learning: a case study on multi-class medical volume segmentation

    Publication Year: 2015 , Page(s): 439 - 446
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (664 KB)

    In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart d... View full abstract»

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Aims & Scope

IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in Computer Vision.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Majid Mirmehdi
University of Bristol
UK

Publisher
Editorial Assistant
IET Research Journals
Michael Faraday House
Six Hills Way
Stevenage  SG1 2AY  SG1 2AY  United Kingdom
iet_cvi@theiet.org