Automated Fruit Grading System Using Image Fusion

Author(s):  
Neha Janu ◽  
Ankit Kumar

This work proposed a recognition system capable of identifying an Indian fruit from among a set, established in a database, using computer vision techniques. The investigation made it possible to compare the image color models, together with the size and shape characteristics previously used by different researcher. For the class of fruits defined in this investigation, it was determined that the characteristics that best described them were the average values of the RGB channels and the length of the major and minor axes when the image fusion technique is used, a process that allowed obtaining results with an accuracy equal to 92% in the tests carried out, finding that not always selecting a greater number of variables to form the descriptor vector allows the classifiers to deliver a more accurate response. In this sense it is important to consider that among the study variables a low dependency or correlation value.

2021 ◽  
Vol 11 (21) ◽  
pp. 10040
Author(s):  
Yu Lei ◽  
Bing Lei ◽  
Yubo Cai ◽  
Chao Gao ◽  
Fujie Wang

To improve the robustness of current polarimetric dehazing scheme in the condition of low degree of polarization, we report a polarimetric dehazing method based on the image fusion technique and adaptive adjustment algorithm which can operate well in many different conditions. A splitting focus plane linear polarization camera was employed to grab the images of four different polarization directions, and the haze was separated from the hazy images by low-pass filtering roughly. Then the image fusion technique was used to optimize the method of estimating the transmittance map. To improve the quality of the dehazed images, an adaptive adjustment algorithm was introduced to adjust the illumination distribution of the dehazed images. The outdoor experiments have been implemented and the results indicated that the presented method could restore the target information obviously, and both the visual effect and quantitative evaluation have been enhanced.


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