Fast median filters using separable sorting networks

Adams, Andrew

doi:10.1145/3450626.3459773

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Therefore, in order to make the extracted spectral information more accurately reflect the change of the sample curve, it is necessary to preprocess the original spectrum to eliminate or reduce the influence of light intensity, environmental factors and noise interference on the spectral information as much as possible. In this experiment, the Savitzky-Golay(SG) , multivariate scatter correction(MSC) (Liang et al, 2018) and median filter(MF) (Adams, 2021) methods were used to preprocess the original data.…”

Section: Spectral Preprocessing Methodsmentioning

confidence: 99%

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics

Zou

Chen²,

WU³

et al. 2023

Front. Plant Sci.

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Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.

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Section: Spectral Preprocessing Methodsmentioning

confidence: 99%

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics

Zou

Chen²,

WU³

et al. 2023

Front. Plant Sci.

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“…Morphological operations are related to the median filter in that they use statistical information within the windows. There are two main approaches to median filtering: the sorting network method [Ada21] and the histogram‐based method [Gre17]. Moroto et al [MU22] presented another approach using a wavelet matrix, and discussed its extension to polygonal windows.…”

Section: Related Workmentioning

confidence: 99%

Fast Grayscale Morphology for Circular Window

Moroto,

Umetani

2023

Computer Graphics Forum

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Morphological operations are among the most popular classic image filters. The filter assumes the maximum or minimum value within a window and is often used for light object thickening and thinning operations, which are important components of various workflows, such as object recognition and stylization. Circular windows are preferred over rectangular windows for obtaining isotropic filter results. However, the existing efficient algorithms focus on rectangular or binary input images. Efficient morphological operations with circular windows for grayscale images remain challenging. In this study, we present a fast grayscale morphology heuristic computation algorithm that decomposes circular windows using the convex hull of circles. We significantly accelerate traditional methods based on Minkowski addition by introducing new decomposition rules specialized for circular windows. As our morphological operation using a convex hull can be computed independently for each pixel, the algorithm is efficient for modern multithreaded hardware.

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“…Xu et al present an effective approach [45] for structure-texture image decomposition, leveraging the discriminative patch recurrence to develop a nonlocal transform that can better distinguish and sparsify texture components. Motivated by the rapid development of machine learning theory, many global filtering methods [3,[46][47][48][49][50][51][52][53][54][55][56][57] have been designed under the deep learning architecture. For instance, a CNN-based joint filter [46] was proposed by selectively taking advantage of the structure information consistent with both guided and input images.…”

Section: Introductionmentioning

confidence: 99%

Local Adaptive Image Filtering Based on Recursive Dilation Segmentation

Zhang,

Chen,

Chen

et al. 2023

Sensors

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This paper introduces a simple but effective image filtering method, namely, local adaptive image filtering (LAIF), based on an image segmentation method, i.e., recursive dilation segmentation (RDS). The algorithm is motivated by the observation that for the pixel to be smoothed, only the similar pixels nearby are utilized to obtain the filtering result. Relying on this observation, similar pixels are partitioned by RDS before applying a locally adaptive filter to smooth the image. More specifically, by directly taking the spatial information between adjacent pixels into consideration in a recursive dilation way, RDS is firstly proposed to partition the guided image into several regions, so that the pixels belonging to the same segmentation region share a similar property. Then, guided by the iterative segmented results, the input image can be easily filtered via a local adaptive filtering technique, which smooths each pixel by selectively averaging its local similar pixels. It is worth mentioning that RDS makes full use of multiple integrated information including pixel intensity, hue information, and especially spatial adjacent information, leading to more robust filtering results. In addition, the application of LAIF in the remote sensing field has achieved outstanding results, specifically in areas such as image dehazing, denoising, enhancement, and edge preservation, among others. Experimental results show that the proposed LAIF can be successfully applied to various filtering-based tasks with favorable performance against state-of-the-art methods.

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Fast median filters using separable sorting networks

Cited by 8 publications

References 21 publications

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics

Fast Grayscale Morphology for Circular Window

Local Adaptive Image Filtering Based on Recursive Dilation Segmentation

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