Histogram based segmentation of shadowed leaf images

Storcz, Tamás; Ercsey, Zsolt; Várady, Géza

doi:10.1556/606.2018.13.1.2

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…When applying a generic pixel statistics-based shadow removing method [14,15] or color correction [16] as an image preprocessing step, the system efficiency was not improved. The reasons were that the objects and also the noise have almost 2-dimensional shape, thus do not drop significant shadow.…”

Section: Structurementioning

confidence: 99%

Detection of feature-poor, small sized objects on noisy images

Storcz

Ercsey

2022

Pollack

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Visual identification of objects is an important challenge today. Main target of frequently applied methods is to identify or classify complex objects. These methods are far less effective when objects are small and less complex, and thus less descriptor features are on hand. The main reason for this is that these features can significantly change on object occlusion or appearance of noise. The presented solution performs identification of simple, small (size is 17 × 13 pixels) objects with elliptical shape. High pass filtered normalized cross correlation is used for region of interest detection and a simple deep neural network is used for classification of selected regions. The proposed method detected objects on a noisy image with accuracy of 96.2%.

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Section: Structurementioning

confidence: 99%

Detection of feature-poor, small sized objects on noisy images

Storcz

Ercsey

2022

Pollack

Self Cite

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“…In [25] Otsu published a global statistical method applied on intensity histograms to threshold grayscale images. [26,27] combined color and intensity based methods and published a basic statistical method to separate background and foreground independently from cast shadows.…”

Section: Related Workmentioning

confidence: 99%

Identification of Shadowed Areas to Improve Ragweed Leaf Segmentation

2021

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As part of a project targeting geometrical structure analysis and identification of ragweed leaves, sample images were created. Even though images were taken under near optimal conditions, the samples still contain noise of cast shadow. The proposed method improves chromaticity based primary shape segmentation efficiency by identification and re-classification of the shadowed areas. The primary classification of each point is done generally based on thresholding the Hue channel of Hue/Saturation/Value color space. In this work, the primary classification is enhanced by thresholding an intra-class normalized weight computed from the class specific Value channel. The corrective step is the removal of areas marked as shadow from the object class. The idea is based on the assumption that the image contains a single, flat leaf in front of a homogeneous background, but there are no color and illumination restrictions. Thus, parameters of the imaging system and the light sources have influence on homogeneity of image parts; however vague shadows differ only in intensity, and hard shadows can only be dropped on the background.

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“…An evaluation of railway ballast consolidation with discriminant and cluster analysis is proposed in [12]. A histogram-based image segmentation method, which is proposed in [13], is a promising technique for pre-processing HRPI images of the rail rolling surface. Raster image processing methods are used in [14] for the extraction of objects of interest from point clouds and their automatic classification.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods

et al. 2019

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In this paper, an application of computer vision and machine learning algorithms for common crossing frog diagnostics is presented. The rolling surface fatigue of frogs along the crossing lifecycle is analysed. The research is based on information from high-resolution optical images of the frog rolling surface and images from magnetic particle inspection. Image processing methods are used to pre-process the images and to detect the feature set that corresponds to objects similar to surface cracks. Machine learning methods are used for the analysis of crack images from the beginning to the end of the crossing lifecycle. Statistically significant crack features and their combinations that depict the surface fatigue state are found. The research result consists of the early prediction of rail contact fatigue.

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Histogram based segmentation of shadowed leaf images

Cited by 6 publications

References 13 publications

Detection of feature-poor, small sized objects on noisy images

Detection of feature-poor, small sized objects on noisy images

Identification of Shadowed Areas to Improve Ragweed Leaf Segmentation

Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods

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