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IET Computer Vision

Volume 12, Issue 8, December 2018

Volume 12, Issue 8

December 2018

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    • Guest Editorial: Computer Vision in Cancer Data Analysis
      Texture-based feature extraction of smear images for the detection of cervical cancer
      Mammographic mass classification using filter response patches
      Hybrid segmentation method based on multi-scale Gaussian kernel fuzzy clustering with spatial bias correction and region-scalable fitting for breast US images
      Enhancement approach for liver lesion diagnosis using unenhanced CT images
      Automated skin lesion segmentation of dermoscopic images using GrabCut and k-means algorithms
      Deep residual network with regularised fisher framework for detection of melanoma
      Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data
    • Deep hashing network for material defect image classification
      Fine-grained recognition of maritime vessels and land vehicles by deep feature embedding
      Support vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angle
      Object sequences: encoding categorical and spatial information for a yes/no visual question answering task
      Palmprint recognition using a modified competitive code with distinctive extended neighbourhood
      High variation removal for background subtraction in traffic surveillance systems
      ResFusion: deeply fused scene parsing network for RGB-D images
      Multi-layer fusion techniques using a CNN for multispectral pedestrian detection
      Automatic multi-circle detection on images using the teaching learning based optimisation algorithm
      Improved dual-mode compressive tracking integrating balanced colour and texture features
      Robust tracking of multiple objects in video by adaptive fusion of subband particle filters
      Enhanced multi-dataset transfer learning method for unsupervised person re-identification using co-training strategy

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