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10.3390/s16020173 http://scihub22266oqcxt.onion/10.3390/s16020173 C4801551!4801551!26840313     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sensors+(Basel) 2016 ; 16 (2): ä Nephropedia Template TP {{tp|p=26840313|t=2016. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion |pdf=|usr=}}{{26840313}} gab.com Text Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=26840313 #FOAvip Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. Twit Text FOAVip Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=26840313 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26840313 #FOAvip English Wikipedia <ref>{{Cite pmid|26840313}}</ref> Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion #MMPMID26840313 Zhao Y; Gong L; Huang Y; Liu C Sensors (Basel) 2016[Feb]; 16 (2): ä PMID26840313show ga Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. äRobust Tomato Recognition for Robotic Harvesting Using Feature Images (epmc).pdf DeepDyve Pubget Overpricing