Modified temporal difference method for change detection

Chang, Cheng-Chi; Chia, Tsorng-Lin; Yang, Chen-Kuei

doi:10.1117/1.1839893

Cited by 19 publications

(3 citation statements)

References 10 publications

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“…4a) also explained by Long and Yang, [1990] and Chang et al, [2005]. Whilst, the 'unsupervised classifier' algorithm based analysis depicted broad 'increase' or 'decrease' zones that could not explain the interchange between different wetland units.…”

Section: Wetland Change Analysesmentioning

confidence: 90%

Assessment and change analyses (1987–2002) for tropical wetland ecosystem using earth observation and socioeconomic data

Nagabhatla

Finlayson²,

Sellamuttu³

2012

European Journal of Remote Sensing

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The two components of the study reflect assessment and change analysis of a tropical wetland in Sri Lanka. The first section explains spatial classification using pixel level-disaggregated image analysis and refined aggregated image analysis and comparison of information extracted by all methods to analyse a better classifier. The second section illustrates change analysis calibrating the land change modeller (LCM) [IDRISI-Andes]. Key observations: a) visual interpretation provides comprehensive blueprint of the wetlandscape compared to supervised and unsupervised classifiers b) change in landscape pattern reflect substantial transition in wetland use. Validation using field coordinates and socioeconomic data showed kappa value (%) of 87.

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Section: Wetland Change Analysesmentioning

confidence: 90%

Assessment and change analyses (1987–2002) for tropical wetland ecosystem using earth observation and socioeconomic data

Nagabhatla

Finlayson²,

Sellamuttu³

2012

European Journal of Remote Sensing

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“…Image change analysis is of interest in various fields and many algorithms have been developed for change detection. 32,33 A survey of change detection methods can be found in Ref. 34.…”

Section: Segmentation Of Areas With Structural Changesmentioning

confidence: 99%

Retinal image restoration by means of blind deconvolution

et al. 2011

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Retinal imaging plays a key role in the diagnosis and management of ophthalmologic disorders, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Because of the acquisition process, retinal images often suffer from blurring and uneven illumination. This problem may seriously affect disease diagnosis and progression assessment. Here we present a method for color retinal image restoration by means of multichannel blind deconvolution. The method is applied to a pair of retinal images acquired within a lapse of time, ranging from several minutes to months. It consists of a series of preprocessing steps to adjust the images so they comply with the considered degradation model, followed by the estimation of the point-spread function and, ultimately, image deconvolution. The preprocessing is mainly composed of image registration, uneven illumination compensation, and segmentation of areas with structural changes. In addition, we have developed a procedure for the detection and visualization of structural changes. This enables the identification of subtle developments in the retina not caused by variation in illumination or blur. The method was tested on synthetic and real images. Encouraging experimental results show that the method is capable of significant restoration of degraded retinal images.

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“…The longitudinal changes in the retina are typically structural changes, and as such they need to be segmented and masked out. Image change analysis is of interest in various fields and many algorithms have been developed for such a task (Aach & Kaup, 1995, Chang et al, 2005. A complete overview of change detection methods can be found in the survey by Radke et al (2005).…”

Section: Longitudinal Change Detectionmentioning

confidence: 99%

Comprehensive retinal image analysis: image processing and feature extraction techniques oriented to the clinical task

Marrugo Hernández

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Medical digital imaging has become a key element of modern health care procedures. It provides a visual documentation, a permanent record for the patients, and most importantly the ability to extract information about many diseases. Ophthalmology is a field that is heavily dependent on the analysis of digital images because they can aid in establishing an early diagnosis even before the first symptoms appear. This dissertation contributes to the digital analysis of such images and the problems that arise along the imaging pipeline, a field that is commonly referred to as retinal image analysis. We have dealt with and proposed solutions to problems that arise in retinal image acquisition and longitudinal monitoring of retinal disease evolution. Specifically, non-uniform illumination, poor image quality, automated focusing, and multichannel analysis. However, there are many unavoidable situations in which images of poor quality, like blurred retinal images because of aberrations in the eye, are acquired. To address this problem we have proposed two approaches for blind deconvolution of blurred retinal images. In the first approach, we consider the blur to be space-invariant and later in the second approach we extend the work and propose a more general space-variant scheme. For the development of the algorithms we have built preprocessing solutions that have enabled the extraction of retinal features of medical relevancy, like the segmentation of the optic disc and the detection and visualization of longitudinal structural changes in the retina. Encouraging experimental results carried out on real retinal images coming from the clinical setting demonstrate the applicability of our proposed solutions.

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Modified temporal difference method for change detection

Cited by 19 publications

References 10 publications

Assessment and change analyses (1987–2002) for tropical wetland ecosystem using earth observation and socioeconomic data

Assessment and change analyses (1987–2002) for tropical wetland ecosystem using earth observation and socioeconomic data

Retinal image restoration by means of blind deconvolution

Comprehensive retinal image analysis: image processing and feature extraction techniques oriented to the clinical task

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