Multiscale image factorization

Johnson, John L.; Padgett, Mary Lou; Friday, William A.

doi:10.1109/icnn.1997.614011

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…The following are examples of types of segmentation methods: (1) thresholding-based segmentation, (2) segmentation of the image based on shape or edge detection, (3) region growth-based methods, (4) energy minimization-based segmentation and (5) dynamical image segmentation. Some of these methods are described in [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. These methods are publicly available open-source code, such as in the Python toolbox scikit-image [15].…”

Section: Introductionmentioning

confidence: 99%

Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

et al. 2023

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Monitoring variations in the extent of wet snow over space and time is essential for many applications, such as hydrology, mountain ecosystems, meteorology and avalanche forecasting. The Synthetic Aperture Radar (SAR) measurements from the Sentinel-1 satellite help detect wet snow in almost all weather conditions. Most detection methods use a fixed threshold to a winter image ratio with one or two reference images (with no snow or dry snow). This study aimed to explore the potential of image segmentation methods from different families applied to Sentinel-1 SAR images to improve the detection of wet snow over the French Alps. Several segmentation methods were selected and tested on a large alpine area of 100 × 100 km2. The segmentation methods were evaluated over one season using total snow masks from Sentinel-2 optical measurements and outputs from forecasters’ bulletins combining model and in-situ observations. Different metrics were used (such as snow probability, correlations, Hamming distance, and structure similarity scores). The standard scores illustrated that filtering globally improved the segmentation results. Using a probabilistic score as a function of altitude highlights the interest in some segmentation methods, and we show that these scores could be relevant to calibrate the parameters of these methods better.

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Section: Introductionmentioning

confidence: 99%

Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

et al. 2023

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“…This type of PCNN can transform the input image into firing frequency representation with desirable invariant properties [20]. Variations of this type of PCNN have found successful applications in image factorization [21] and may have a connection with wavelet and other transformations [33].…”

Section: B Comparison With Pulse-coupled Neural Networkmentioning

confidence: 99%

Range image segmentation using a relaxation oscillator network

Liu

Wang

1999

IEEE Trans. Neural Netw.

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Abstract-A locally excitatory globally inhibitory oscillator network (LEGION) is constructed and applied to range image segmentation, where each oscillator has excitatory lateral connections to the oscillators in its local neighborhood as well as a connection with a global inhibitor. A feature vector, consisting of depth, surface normal, and mean and Gaussian curvatures, is associated with each oscillator and is estimated from local windows at its corresponding pixel location. A context-sensitive method is applied in order to obtain more reliable and accurate estimations. The lateral connection between two oscillators is established based on a similarity measure of their feature vectors. The emergent behavior of the LEGION network gives rise to segmentation. Due to the flexible representation through phases, our method needs no assumption about the underlying structures in image data and no prior knowledge regarding the number of regions. More importantly, the network is guaranteed to converge rapidly under general conditions. These unique properties may lead to a real-time approach for range image segmentation in machine perception.Index Terms-LEGION, oscillatory correlation, pulse-coupled neural networks, range image segmentation, relaxation oscillator.

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“…One such method is a multiscale renormalization algorithm which produces a fused image by nonlinear recombination of the ratio of low-pass (ROLP) pyramidal decompositions of the original images [26]. Another method is a cyclic algorithm for shadow removal based on pulse-coupled neural networks (PCNN), an image processing algorithm derived from biologically-grounded cortical models that addressed the experimental findings of stimulusinduced synchronous bursts of pulse activity [27,28]. The PCNN-based factorization is an efficient image-processing tool for noise smoothing and elementary image segmentation that operates on local image patches.…”

Section: Introductionmentioning

confidence: 99%

Improved localization accuracy in stochastic super-resolution fluorescence microscopy by K-factor image deshadowing

Ilovitsh

Meiri

Ebeling

et al. 2013

Biomed. Opt. Express

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Localization of a single fluorescent particle with sub-diffractionlimit accuracy is a key merit in localization microscopy. Existing methods such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) achieve localization accuracies of single emitters that can reach an order of magnitude lower than the conventional resolving capabilities of optical microscopy. However, these techniques require a sparse distribution of simultaneously activated fluorophores in the field of view, resulting in larger time needed for the construction of the full image. In this paper we present the use of a nonlinear image decomposition algorithm termed K-factor, which reduces an image into a nonlinear set of contrast-ordered decompositions whose joint product reassembles the original image. The K-factor technique, when implemented on raw data prior to localization, can improve the localization accuracy of standard existing methods, and also enable the localization of overlapping particles, allowing the use of increased fluorophore activation density, and thereby increased data collection speed. Numerical simulations of fluorescence data with random probe positions, and especially at high densities of activated fluorophores, demonstrate an improvement of up to 85% in the localization precision compared to single fitting techniques. Implementing the proposed concept on experimental data of cellular structures yielded a 37% improvement in resolution for the same superresolution image acquisition time, and a decrease of 42% in the collection time of super-resolution data with the same resolution.

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Multiscale image factorization

Cited by 13 publications

References 8 publications

Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

Range image segmentation using a relaxation oscillator network

Improved localization accuracy in stochastic super-resolution fluorescence microscopy by K-factor image deshadowing

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