USPLeaf: Automatic leaf area determination using a computer vision system

Meira, Luiz Antonio; Pereira, Lilian Elgalise Techio; Santos, Manoel Eduardo Rozalino; Tech, Adriano Rogério Bruno

doi:10.5935/1806-6690.20200073

Cited by 3 publications

(2 citation statements)

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“…Considering the maneuverability of track enhancement in the actual process, this paper reduced the resolution of the original track map, converted RGB color space into YCbCr color space, and carried out edge detection by Y component, Cb component, and Cr component, respectively, to extract edge feature information of the image and mark edge points. Then, the pixels to be interpolated and replaced are divided into textured direction pixels and weakly textured pixels, that is, the points in the edge region and the inner region of the contour [27]. The lost pixels estimated by the first scan and interpolation were denoted as I h ð2i, 2jÞ, the lost pixels estimated by the second scan and interpolation were denoted as I h ð2i − 1, 2jÞ, and the lost pixels estimated by the third scan and interpolation were denoted as I h ð2i, 2j − 1Þ.…”

Section: Extracting Edge Information Of Sports Video Images Inmentioning

confidence: 99%

Motion Track Enhancement Method of Sports Video Image Based on OTSU Algorithm

Ling

2022

Wireless Communications and Mobile Computing

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Current sports video image tracking methods cannot get the average gray information of pixels, which leads to the low segmentation accuracy of target trajectory. Therefore, this paper proposes a method of enhancing the motion track of sports video object based on OTSU algorithm. The OTSU algorithm is introduced to segment the unsupervised image automatically based on threshold calculation. Combined with 3D histogram, the OTSU algorithm is optimized to overcome the influence of noise and nonuniform illumination and to improve the segmentation performance. Using the optimized OTSU algorithm, the preprocessing of sports video image is realized by gray level transform, median filter, and binarization. Based on this, the object motion model of sports video image is constructed, and the edge information of sports video image is extracted. Using binocular stereo vision technology, according to the image target motion model and the current space coordinates, calculate the motion track, and obtain the parameters of sports video image. Experimental results show that the proposed method can segment the target accurately in a shorter time, and the enhancement effect of target trajectory is obvious, and the error is small.

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Section: Extracting Edge Information Of Sports Video Images Inmentioning

confidence: 99%

Motion Track Enhancement Method of Sports Video Image Based on OTSU Algorithm

Ling

2022

Wireless Communications and Mobile Computing

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“…Comparisons of leaf area measurements errors between methods on a set of 145 leaves belonging to 69 species.The accuracy of FAMeLeS on images from field sampling mixed with web images attests to the efficiency of the method even with images from multiple devices (scanner or photographs). While some authors warned about the limitation of their method depending on image quality(Meira et al, 2020) or brightness(Haqqiman Radzali et al, 2016), FAMeLeS has proven to be very tolerant of the di-versity of image sizes (compressed or not), resolutions and lighting conditions which makes it possible to work on existing leaf image databases or to aggregate data sets from different sources. The combination of such a high tolerance to image type and leaf characteristics offers the opportunity to build standardized database of leaf binary images from any biome, and to produce high amounts of data to address functional ecology questions, to study biogeographical patterns and drivers of morphological leaf traits and to feed allometric models and algorithms of plant leaf classification.…”

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confidence: 99%

FAMeLeS: A multispecies and fully automated method to measure morphological leaf traits

Montès,

Tosini,

Laffont‐Schwob

et al. 2024

Methods Ecol Evol

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Leaf shape parameters are of key importance to explain the role of energy balance and water economy in plant species distribution, plant productivity and, more generally, in plant–environment interactions. Yet, leaf shape measurements based on image processing are still challenging due to the high diversity of leaf shapes, colours and sizes leading to the development of time‐consuming methods with a narrow field of applicability, sometimes species‐specific or often limited to a few species. We developed a fully automated method for measuring multiple leaf shape parameters (area, perimeter, length, width, circularity and solidity) based on a large image sampling of leaf diversity (including litter) belonging to 587 species and spread over 232 countries worldwide. To evaluate the accuracy of the method to detect small objects, the sampling particularly targeted Mediterranean ecosystems (32 species and 25,205 leaves), in which small leaves often represent methodological challenges. We compared our approach and found that its mean error in leaf area measurement (+0.46%) was 1.7–148 times lower than four existing methods. It was also the only one capable of detecting and measuring all leaves in the test data set, even variegated and small leaves (less than 1 mm2). Its reliability was extensively checked on the largest and most diversified data set ever used. Our method, accessible to the broader scientific community, was simple, rapid and effective on multiple image file types and on a high diversity of leaf size, shape and colour. As such, our approach allows measurement not only on fresh leaves but also on dry leaves, such as leaf litter or leaves from herbaria. This tolerance to leaf characteristics is crucial to increase large‐scale sampling efforts and paves the way for a standardized multispecies approach to measuring leaf morphological traits for ecological and agricultural studies.

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Intelligent Recognition Method of Athlete Wrong Movement Based on Image Vision

Wang

Hou

2021

Scientific Programming

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Current methods of human body movement recognition neglect the depth denoising and edge restoration of movement image, which leads to great error in athletes’ wrong movement recognition and poor application intelligence. Therefore, an intelligent recognition method based on image vision for sports athletes’ wrong actions is proposed. The basic principle, structure, and 3D application of computer image vision technology are defined. Capturing the human body image and point cloud data, the three-dimensional dynamic model of sports athletes action is constructed. The color camera including CCD sensor and CMOS sensor is selected to collect the wrong movement image of athlete and provide image data for the recognition of wrong movement. Wavelet transform coefficient and quantization matrix threshold are introduced to denoise the wrong motion images of athletes. Based on this, the feature of sports athlete’s motion contour image is extracted in spatial frequency domain, and the edge of the image is further recovered by Canny operator. Experimental results show that the proposed method can accurately identify the wrong movements of athletes, and there is no redundancy in the recognition results. Image denoising effect is good and less time-consuming and can provide a reliable basis for related fields.

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USPLeaf: Automatic leaf area determination using a computer vision system

Cited by 3 publications

References 0 publications

Motion Track Enhancement Method of Sports Video Image Based on OTSU Algorithm

Motion Track Enhancement Method of Sports Video Image Based on OTSU Algorithm

FAMeLeS: A multispecies and fully automated method to measure morphological leaf traits

Intelligent Recognition Method of Athlete Wrong Movement Based on Image Vision

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