Quantum genetic algorithm for adaptive image multi-thresholding segmentation

Zhang, Jian; Li, Huanzhou; Tang, Zhangguo; Liu, Chang

doi:10.1504/ijcat.2015.069334

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…For these advantages, QGA has been further studied in many areas, such as path planning for aircrafts, vehicles, robots, and cruise missiles [14], network dispatching and coverage [24], optimisation of neural networks [25], and so on. Furthermore, in image processing field, QGA is also applied widely in image restoration [26], image segmentation [27, 28], and image enhancement [29] etc. However, it has not been applied in the field of visual tracking yet.…”

Section: Related Workmentioning

confidence: 99%

Target tracking approach via quantum genetic algorithm

Jin

Hou

et al. 2017

IET Computer Vision

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Aiming at an efficient feature match and similarity search in visual tracking, this study proposes a tracking algorithm based on quantum genetic algorithm. Therein, the global optimisation ability of quantum genetic algorithm is utilised. In the framework of quantum genetic algorithm, the positions of pixels are taken as individuals in population, while scale-invariant feature transform and colour features are taken as target model. Via defining the objective function, individual's fitness values can be measured. Visual tracking is realised when the pixel point with the biggest fitness value is searched and its corresponding position is returned. The experiment results show that the tracking algorithm the authors proposed performs more efficiently when it is compared with the state-of-the-art tracking algorithms.

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Section: Related Workmentioning

confidence: 99%

Target tracking approach via quantum genetic algorithm

Jin

Hou

et al. 2017

IET Computer Vision

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“…2(a), we selected a typical objective image during snapping as the original image in this example with the spatial resolution of 700 × 700, containing intact ears, damaged ears, impurities, and backgrounds. The well-known image segmentation method, thresholding, was used for the objective image [31][32][33][34]. The red, the green, and the blue bands were For the field of corn ear harvest, damage forms are various in different processes, such as whole ear broken, kernel damage, and peeling ear damage.…”

Section: Introductionmentioning

confidence: 99%

Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image

Yuan

Zhao

et al. 2020

Applied Sciences

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Corn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field of agriculture. Conventional image processing methods are difficult to be used for recognizing corn ear damage caused by peeling in field harvesting. To address the this problem, in this paper, we propose a peeling damage recognition method based on RGB image. For our method, we develop a dictionary-learning-based method to recognize corn kernels and a thresholding method to recognize ear damage regions. To obtain better performance, we also develop the corroding algorithm and the expanding algorithm for the post-processing of recognized results. The experimental results demonstrate the practicality and accuracy of the proposed method. This study could provide the theoretical basis to develop online peeling damage detection system for corn ear harvesters.

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