• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.111-115
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• 2009

This paper is related to a skinny smudge tool based on the image segmentation for a master shape. The smudge tool is the popular graphic tool embedded in Adobe Photoshop. The smudge tool is used to smear paint on your canvas. The effect is much like finger painting. You can use the smudge tool by clicking on the smudge icon and clicking on the canvas and while holding the mouse button down, dragging in the direction you want to smudge. A disadvantage of previous smudge tool is to also smear pixels in the undesired region according to generating the target image as blending all pixels in a diameter of the master. In this paper to reduce the disadvantage, the skinny smudge tool based on the image segmentation for a master shape is proposed. The proposed skinny smudge tool has the advantage of applying the smudge effect to the desired regions regardless of the background as the master shape adhered closely to the contour shape is extracted by color image segmentation.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.333-338
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• 2012

This paper is related to a skinny smudge blending method using the arbitrary-shaped master adhered closely to the contour shape. The smudge tool is the popular graphic tool embedded in Adobe Photoshop CS6. The smudge tool is used to smear paint on your canvas. The effect is much like finger painting. We can use the smudge tool by selecting its icon on the toolbox of Adobe Photoshop CS6 and dragging in the direction you want to smudge while holding the mouse button down on the image. As the smudge tool blends all the pixels within a radius of the master to generate the result image, its disadvantages are to smudge even the pixels in the undesired region. In this paper to reduce the disadvantage, the skinny smudge blending method using arbitrary-shaped master is proposed. The proposed blending method has the advantage of applying the smudge effect to the desired regions regardless of the background as the arbitrary-shaped master adhered closely to the contour shape is extracted by color image segmentation.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.776-783
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• 2009

This paper is related to a 2D virtual color hairstyler using skinny smudge tool. The smudge tool is the popular graphic tool embedded in Adobe Photoshop. The smudge tool is used to smear paint on your canvas. The effect is much like finger painting. You can use the smudge tool by clicking on the smudge icon and clicking on the canvas and while holding the mouse button down, dragging in the direction you want to smudge. A disadvantage of previous smudge tool is to also smear pixels in the undesired region according to generating the target image as blending all pixels in a diameter of the master. In this paper to reduce the disadvantage, the skinny smudge tool based on the image segmentation for a master shape is proposed. The proposed skinny smudge tool has the advantage of applying the smudge effect to the desired regions regardless of the background as the master shape adhered closely to the contour shape is extracted by color image segmentation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3708-3728
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• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.