Two additional features are particularly useful in pixelwise satellite data segmentation using neural networks: one results from local window averaging around each pixel (MWA) and another uses a standard deviation est...Two additional features are particularly useful in pixelwise satellite data segmentation using neural networks: one results from local window averaging around each pixel (MWA) and another uses a standard deviation estimator (MWSD) instead of the average. While the former’s complexity has already been solved to a satisfying minimum, the latter did not. This article proposes a new algorithm that can substitute a <i><span style="font-family:Verdana;">naive</span></i><span style="font-family:Verdana;"> MWSD, by making the complexi</span><span><span style="font-family:Verdana;">ty of the computational process fall from </span><i><span style="font-family:Verdana;">O</span></i><span style="font-family:Verdana;">(</span><i><span style="font-family:Verdana;">N</span></i><sup><span style="font-family:Verdana;">2</span></sup><i><span style="font-family:Verdana;">n</span></i><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">) to </span><i><span style="font-family:Verdana;">O</span></i><span><span style="font-family:Verdana;">(</span><i><span style="font-family:Verdana;">N</span></i></span><sup><span style="font-family:Verdana;">2</span></sup><i><span style="font-family:Verdana;">n</span></i><span style="font-family:Verdana;">)</span><span style="font-family:Verdana;">, where </span><i><span style="font-family:Verdana;">N</span></i><span style="font-family:Verdana;"> is a square</span></span><span style="font-family:Verdana;"> input array side, and </span><i><span style="font-family:Verdana;">n</span></i><span style="font-family:Verdana;"> is the moving window’s side length. The Num</span><span style="font-family:Verdana;">ba python compiler was used to make python a competitive high-performance</span> <span style="font-family:Verdana;">computing language in our optimizations. Our results show efficiency benchmars</span>展开更多
In this work, we propose an approach for the separation of coumarins from thin-layer morphological segmentation based on the acquisition of multicomponent images integrating different types of coumarins. The first ste...In this work, we propose an approach for the separation of coumarins from thin-layer morphological segmentation based on the acquisition of multicomponent images integrating different types of coumarins. The first step is to make a segmentation by region, by thresholding, by contour, etc. of each component of the digital image. Then, we proceeded to the calculations of parameters of the regions such as the color standard deviation, the color entropy, the average color of the pixels, the eccentricity from an algorithm on the matlab software. The mean color values at<sub>R</sub> = 91.20 in red, at<sub>B</sub> = 213.21 in blue showed the presence of samidin in the extract. The color entropy values H<sub>G</sub> = 5.25 in green and H<sub>B</sub> = 4.04 in blue also show the presence of visnadine in the leaves of Desmodium adscendens. These values are used to consolidate the database of separation and discrimination of the types of coumarins. The relevance of our coumarin separation or coumarin recognition method has been highlighted compared to other methods, such as the one based on the calculation of frontal ratios which cannot discriminate between two coumarins having the same frontal ratio. The robustness of our method is proven with respect to the separation and identification of some coumarins, in particular samidin and anglicine.展开更多
文摘Two additional features are particularly useful in pixelwise satellite data segmentation using neural networks: one results from local window averaging around each pixel (MWA) and another uses a standard deviation estimator (MWSD) instead of the average. While the former’s complexity has already been solved to a satisfying minimum, the latter did not. This article proposes a new algorithm that can substitute a <i><span style="font-family:Verdana;">naive</span></i><span style="font-family:Verdana;"> MWSD, by making the complexi</span><span><span style="font-family:Verdana;">ty of the computational process fall from </span><i><span style="font-family:Verdana;">O</span></i><span style="font-family:Verdana;">(</span><i><span style="font-family:Verdana;">N</span></i><sup><span style="font-family:Verdana;">2</span></sup><i><span style="font-family:Verdana;">n</span></i><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">) to </span><i><span style="font-family:Verdana;">O</span></i><span><span style="font-family:Verdana;">(</span><i><span style="font-family:Verdana;">N</span></i></span><sup><span style="font-family:Verdana;">2</span></sup><i><span style="font-family:Verdana;">n</span></i><span style="font-family:Verdana;">)</span><span style="font-family:Verdana;">, where </span><i><span style="font-family:Verdana;">N</span></i><span style="font-family:Verdana;"> is a square</span></span><span style="font-family:Verdana;"> input array side, and </span><i><span style="font-family:Verdana;">n</span></i><span style="font-family:Verdana;"> is the moving window’s side length. The Num</span><span style="font-family:Verdana;">ba python compiler was used to make python a competitive high-performance</span> <span style="font-family:Verdana;">computing language in our optimizations. Our results show efficiency benchmars</span>
文摘In this work, we propose an approach for the separation of coumarins from thin-layer morphological segmentation based on the acquisition of multicomponent images integrating different types of coumarins. The first step is to make a segmentation by region, by thresholding, by contour, etc. of each component of the digital image. Then, we proceeded to the calculations of parameters of the regions such as the color standard deviation, the color entropy, the average color of the pixels, the eccentricity from an algorithm on the matlab software. The mean color values at<sub>R</sub> = 91.20 in red, at<sub>B</sub> = 213.21 in blue showed the presence of samidin in the extract. The color entropy values H<sub>G</sub> = 5.25 in green and H<sub>B</sub> = 4.04 in blue also show the presence of visnadine in the leaves of Desmodium adscendens. These values are used to consolidate the database of separation and discrimination of the types of coumarins. The relevance of our coumarin separation or coumarin recognition method has been highlighted compared to other methods, such as the one based on the calculation of frontal ratios which cannot discriminate between two coumarins having the same frontal ratio. The robustness of our method is proven with respect to the separation and identification of some coumarins, in particular samidin and anglicine.
