Region of interest extraction in remote sensing images by saliency analysis with the normal directional lifting wavelet transform

Zhang, Libao; Chen, Jie; Qiu, Bingchang

doi:10.1016/j.neucom.2015.11.093

Cited by 37 publications

(8 citation statements)

References 30 publications

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“…The wavelet transformation 27 , which can be used to effectively analyze the features of an image at different scales, has been frequently applied in the field of image processing and image classification 27 28 . The wavelet transformation provides the time-frequency information of the images, which is a preferred method for replacing some shallow time invariant image features.…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

Wang

Zhang

Liu

et al. 2017

Sci Rep

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There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.

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Section: Methodsmentioning

confidence: 99%

Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

Wang

Zhang

Liu

et al. 2017

Sci Rep

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“…In contrast, the local textural saliency, which describes the local details in some sense, is not as crucial as the other components in all cases we have tried thus far. Nevertheless, when the textural patterns in the image are obvious, e.g., some remote sensing images [48], the textural saliency will play an important role as well.…”

Section: B Experimental Resultsmentioning

confidence: 99%

A Hybrid of Local and Global Saliencies for Detecting Image Salient Region and Appearance

Peng

Cheung

You

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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This paper presents a visual saliency detection approach, which is a hybrid of local feature-based saliency and global feature-based saliency (simply called local saliency and global saliency, respectively, for short). First, we propose an automatic selection of smoothing parameter scheme to make the foreground and background of an input image more homogeneous. Then, we partition the smoothed image into a set of regions and compute the local saliency by measuring the color and texture dissimilarity in the smoothed regions and the original regions, respectively. Furthermore, we utilize the global color distribution model embedded with color coherence, together with the multiple edge saliency, to yield the global saliency. Finally, we combine the local and global saliencies, and utilize the composition information to obtain the final saliency. Experimental results show the efficacy of the proposed method, featuring: 1) the enhanced accuracy of detecting visual salient region and appearance in comparison with the existing counterparts, 2) the robustness against the noise and the low-resolution problem of images, and 3) its applicability to multisaliency detection task.

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“…fall under the label ‘Target detection’. Several authors (Han et al, 2014, Li and Itti, 2011, Lin et al, 2016, Wang et al, 2016, Yokoya and Iwasaki, 2015, Zhang et al, 2016) provide no explicit description of the vessel detection purpose but a general one for target detection. In the case of such occurrence, the analysis in this review takes into account only vessel detection.…”

Section: Inventory Of Evaluated Studiesmentioning

confidence: 99%

Vessel detection and classification from spaceborne optical images: A literature survey

Kanjir

Greidanus

Oštir

2018

Remote Sensing of Environment

238

170

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Region of interest extraction in remote sensing images by saliency analysis with the normal directional lifting wavelet transform

Cited by 37 publications

References 30 publications

Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

A Hybrid of Local and Global Saliencies for Detecting Image Salient Region and Appearance

Vessel detection and classification from spaceborne optical images: A literature survey

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