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Detection of early bruises on apples using near-infrared camera imaging technology combined with adaptive threshold segmentation algorithm

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JOURNAL OF FOOD PROCESS ENGINEERING 2024年第1期47卷 e14500-e14500页

作者： Tian, Mimi Zhang, Jiachuang Yang, Zengrong Li, Mei Li, Junhui Zhao, Longlian China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China Minist Educ Key Lab Modern Precis Agr Syst Integrat Res Beijing Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

Apples are highly susceptible to bruising during picking, packing, and transportation, and early detection of apple bruises allows timely screening and treatment to reduce food safety risks. However, early bruises on apples, especially bruises of 30 min or less, with similar appearance to sound tissue, making manual sorting difficult. To address this problem, a near-infrared (NIR) camera imaging technology combined with adaptive threshold segmentation algorithm for early apple bruise detection is proposed in this article. First, a NIR camera imaging system is built to acquire full-band images of fruit face, fruit stem face and calyx face of sound apples and apples with single bruise and multiple bruises at 30 min of occurrence. Then, an adaptive threshold segmentation algorithm is proposed and an image processing algorithm, which includes high-pass filtering, feature extraction, adaptive threshold segmentation and Hough circle detection, is developed and applied to full-band images in response to the interference of apple stem and calyx on early bruising detection in this article. The results show that the proposed algorithm has an accuracy of 95.56% for the detection of sound apple samples and an F1-score of 94.70% for the detection of early bruised samples, which is better than the commonly used image segmentation algorithms such as Otsu and fixed thresholding. This study shows that the combination of NIR camera imaging and the adaptive threshold segmentation algorithm proposed in this article can effectively detect early bruises on apples, providing a new idea for rapid and nondestructive detection of agricultural product *** applicationsDetecting early bruises on apples can achieve effective monitoring and control of apple supply chain, and guarantee the quality and safety of agricultural products. Most studies are based on conventional machine vision with RGB camera or hyperspectral imaging technology, while conventional machine vision based on RG

关键词： apple early bruises NIR camera imaging adaptive threshold segmentation fruit stem calyx

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Improved adaptive threshold segmentation of Ultrasound Medical Images 21

Improved Adaptive Threshold Segmentation of Ultrasound Medic...

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3rd International Conference on Intelligent Medicine and Image Processing (IMIP)

作者： Khalid, Sana Zhu, Haijiang Yang, Tengfei Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781450390057

Ultrasound Medical Image (UMI) segmentation has usually been a challenging hassle in the field of image segmentation. Since it is difficult to segment target effectively by manually adjusting parameters in UMI, the aim is to find automatic parameter selection approach for the improved adaptive threshold segmentation. This paperwork is divided into three phases to present an improved segmentation algorithm. In first pre-processing phase, Region of Interest (ROI) is extracted from UMI and converted into integral image using computational method. In second phase, the improved method finds the best optimal parameters using grid search approach for the segmentation of extracted ROI whereas third phase focuses on obtaining contours for automated and manual extracted ROI and computes the overlapping area between contours for the final re-definition of segmented results. This overlapping area is directly proportional to segmented result, more is the overlapping area better is the segmentation result. Comparison table shows that the values of the improved method gives the better segmented results as compared to similar techniques. Experimental results verify the feasibility and effectiveness of the approach in the field of medical imaging alongside computational methods.

关键词： adaptive threshold segmentation Ultrasound Medical Image Integral Image Grid search

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Use of unmanned aerial vehicle imagery and a hybrid algorithm combining a watershed algorithm and adaptive threshold segmentation to extract wheat lodging

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PHYSICS AND CHEMISTRY OF THE EARTH 2021年 123卷 103016-103016页

作者： Cao, Wenxuan Qiao, Zeyu Gao, Zeyu Lu, Saihong Tian, Fei China Agr Univ Ctr Agr Water Res China Beijing 100083 Peoples R China Minist Agr & Rural Affairs Wuwei Expt Stn Efficient Water Use Agr Wuwei 733000 Peoples R China

Lodging is a major yield-limiting factor in wheat, so we need a method to collect wheat lodging information efficiently. Traditional extraction of crop lodging is cost intensive and time consuming;satellite remote sensing technology is vulnerable to the cloud and is limited by its temporal and spatial resolution. Therefore, a new method for wheat lodging extraction based on a watershed algorithm combined with an adaptive threshold segmentation hybrid algorithm is proposed. An unmanned aerial vehicle equipped with a high-resolution thermal infrared and visible sensor was used to acquire images of lodged and nonlodged wheat. Results indicate that there are morphological differences between lodged and nonlodged wheat and that the spectral reflectance and temperature of lodged wheat is higher than that of nonlodged wheat. The watershed algorithm is sensitive to the edge response and can reflect the contour characteristics of the lodging area. However, the impact of noise is obvious;there are salt-and-pepper patches. The wheat lodging ratios were calculated as 17.81%, with an accuracy of 80.26%. With adaptive threshold segmentation, the wheat lodging ratios were calculated as 18.78%, with an accuracy of 84.65%. If these two methods are fused, the influence of noise can be reduced, and an extraction accuracy of 93.58% can be achieved. This research provides a new idea for highly efficient crop lodging data collection, which can be used for precision farming and the selection of crop genotypes with high lodging resistance.

关键词： Wheat lodging Watershed algorithm adaptive threshold segmentation Hybrid algorithm UAV

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Research on Cloud Computing Modeling Based on Fusion Difference Method and Self-adaptive threshold segmentation

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2018年第6期32卷 1859010-1859010页

作者： Sun, Hong Chen, Shi-Ping Xu, Li-Ping Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Minist Educ Shanghai Key Lab Modern Opt Syst Shanghai 200093 Peoples R China Minist Educ Engn Res Ctr Opt Instrument & Syst Shanghai 200093 Peoples R China

The traditional Gaussian mixture background model failed to build a reasonable background in complex scenarios, so this paper proposes an improved self-adaptive Gaussian mixture background modeling which integrates the difference method and adaptive threshold segmentation to improve the traditional one. In the proposed model, the difference method is applied to achieve the segmentation of changing area and background area, and different weight update policies are used for different areas. Background area updates background model with a fixed update rate. The changing region is divided into moving target area and background show area with the fusion of adaptive threshold;the background show area has a large update rate, allowing the previously obscured parts to recover rapidly;the moving target area won't build new Gaussian components for the Gaussian mixture model. Experiments show that the video sequence algorithm with uncertainties of building background model has good adaptability. It helps to improve computing speed to a large extent and it can also respond to the change of the actual scene quickly.

关键词： Mixture model difference method adaptive threshold segmentation moving target detection

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Aerial infrared small target detection algorithm combined structure tensor and local contrast

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OPTICA APPLICATA 2024年第3期54卷 365-381页

作者： Wang, Zhonghu He, Bangsheng He, Wenjie Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Peoples R China

To solve the problem of false alarm rate in detecting infrared small targets under complex cloud backgrounds, a novel algorithm combining structure tensor and local contrast is proposed. The structure tensor can better describe the gradient distributions in the local image area, and its eigenvalues can also depict the characteristics of the area. Combining the weighted local contrast with eigenvalues, the small targets can be enhanced and the background can be suppressed. In addition, to highlight the target, the regional complexity is further used for weighting local contrast. The presented algorithm steps are as follows: firstly, Gaussian filtering is performed on the original image;secondly, the larger eigenvalue of the structure tensor matrix is used to calculate the local contrast through the difference operation;thirdly, the regional complexity is calculated by the gray difference between the central and surrounding regions for weighting the local contrast to generate a saliency map;finally, an adaptive threshold segmentation is performed on the saliency map to extract the real target. The comparative experiments show that the proposed algorithm can achieve the highest detection rate, lowest false alarm rate, and shortest running time.

关键词： adaptive threshold segmentation local contrast regional complexity small target structure tensor

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A real-time framework for HD video defogging using modified dark channel prior

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2024年第2期21卷 55-55页

作者： Wu, Xinchun Chen, Xiangyu Wang, Xiao Zhang, Xiaojun Yuan, Shuxuan Sun, Biao Huang, Xiaobing Liu, Lintao Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610097 Peoples R China Qianghua Times Chengdu Technol Co Ltd Chengdu 610095 Peoples R China Chengdu Univ Informat Technol Coll Commun Engn Chengdu 610225 Peoples R China

Foggy weather reduces the quality of video capture and seriously affects the normal work of video surveillance, remote sensing monitoring, and intelligent driving. Many methods have been proposed to remove video haze. However, under the premise of ensuring real-time performance, their defogging effect needs to be further improved. This paper improves the dark channel prior (DCP) dehazing algorithm, and designs a defogging framework that takes into account good dehazing effect and real-time processing. First, an adaptive threshold segmentation algorithm is proposed, which can well solve the serious color cast problem in brighter areas in DCP. Second, an algorithm for preserving image details using gradients is proposed, which achieves a good balance between detail preservation and computational efficiency. Then, each frame of video is evenly divided into a plurality of sub-areas, and the sub-areas are sequentially processed in a pipeline manner, which improves calculation efficiency. Finally, a high-definition real-time video defogging framework with a resolution of 1920 x 1080 and 60 frames/s is realized on the ZYNQ 7035.

关键词： Video defogging High definition Real time adaptive threshold segmentation DCP

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Using improved DeepLabv3+network integrated with normalized difference water index to extract water bodies in Sentinel-2A urban remote sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2021年第1期15卷

作者： Su, Houcheng Peng, Yuhao Xu, Chao Feng, Ao Liu, Tao Sichuan Agr Univ Coll Informat Engn Yaan Peoples R China

Efficient and accurate extraction of water areas from remote sensing images is a popular research topic. Currently, researchers have attempted to use neural networks to extract water from remote sensing images. However, most of these studies used computer vision techniques to improve the model results without considering the multi-band information unique to remote sensing images. Thus our study proposes an improved DeepLabv3+ network to increase the water body extraction accuracy in urban remote sensing images. The DeepLabv3+ network has the characteristics of extracting image features at multiple scales. We improved the network structure to incorporate multi-band features. By comparing several multi-band input methods, the feature map calculated by the normalized difference water index (NDWI) was converted into an input suitable for the neural network by comparing several multi-band input methods. Simultaneously, we developed a parallel convolution structure to combine the NDWI feature map with a standard false color remote sensing image during feature extraction. This allows the network to focus more on image areas that may be water bodies. We used atmospherically corrected Sentinel-2A L2A-level data, divided the training set at multiple scales, and conducted several experiments. The results show that the proposed network can improve water extraction accuracy when training subregions are unified from different sizes to 512 x 512. Finally, we used the model to extract water bodies from remote sensing images from different regions. We combined the images with visual interpretation to verify the reliability of the model results. Moreover, the model scores of four types of multi-scale neural networks in the two categories are compared, which proves the effectiveness of the method. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： water body extraction water body index adaptive threshold segmentation improved DeepLabv3+ multi-scale input model convolutional feature cascade

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Robust Detection of Lighting LEDs by Analyzing the Geometric Structure of the Tunnel Interior Environment in Vehicle-Mounted Video Sequences 40

Robust Detection of Lighting LEDs by Analyzing the Geometric...

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40th Chinese Control Conference (CCC)

作者： Xin, Le Shi, Zeyu Chen, Yangzhou Beijing Univ Technol Informat Dept Coll Artificial Intelligence & Automat Beijing 100124 Peoples R China Beijing Univ Technol Coll Metropolitan Transportat Beijing 100124 Peoples R China

ISBN: (纸本)9789881563804

Tunnel LED lighting system is an important infrastructure to ensure the safety and comfort of driving in highway tunnels. In this paper, an automatic robust detection of lighting LED lamps in highway tunnels based on vehicle-mounted video processing is proposed. By analyzing the geometric structure of the interior road environment in tunnels, this method includes the following three aspects. Firstly, the front-vehicle vanishing point is detected by using the M-estimator sample and consensus (MSAC) algorithm, just with the set of significant parallel lines extracted using the LSD (Line Segment Detector) operator. Secondly, after the dark region on the top of the tunnel is accurately segmented, an image segmentation algorithm based on adaptive threshold is adopted, realizing the LED lamp candidate region detection. Thirdly, this paper proposes post-processing methods to ensure the accurate detection of LED lamps in complex scenes with highlight-interference in the tunnel. The experimental results show that this method can robustly detect the LED lamps in the tunnel under complex conditions with high accuracy, which can meet the needs of efficient acceptance inspection and daily maintenance of tunnel LED lighting system.

关键词： Tunnel Lighting System Robust Detection of Lighting LEDs On-board Video Processing Front-vehicle Vanishing Point adaptive threshold segmentation

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A method for automatic segmentation of Hela cell imaging in digital holography

A method for automatic segmentation of Hela cell imaging in ...

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Conference on AOPC - Optical Sensing and Imaging Technology

作者： Wei, Jiang Li, Zhuoshi Guo, Zhenyan Nanjing Univ Sci & Technol Smart Computat Imaging SCI Lab Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligent Se Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Smart Computat Imaging Res Insitute SCRI Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9781510650060;9781510650053

The analysis of Hela cells are very dependent on the segmentation of cell images, but the difficulty of cell image segmentation comes from two aspects: cell itself and imaging technology. we present a method for automatic segmentation of Hela cell imaging in digital holography(DH) in this paper. By using a DH imaging system and a cell segmentation algorithm based on adaptive threshold segmentation and watershed algorithm, cells that adhere to each other or have irregular shapes in the image can be segmented.

关键词： Cell image segmentation Digital holography adaptive threshold segmentation Watershed

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Research on Roundness Detection and Sorting of Oil Cooling Pipe Based on Machine Vision 18

Research on Roundness Detection and Sorting of Oil Cooling P...

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IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Li Junjie Wang Chunguang Wu Jin Wang Xiaoming Zhu Yaqiao Zhang Shihui Tianjin Univ Sci & Technol Tianjin 300384 Peoples R China Tianjin Sino German Univ Appl Sci Tianjin 300350 Peoples R China

ISBN: (纸本)9781665441018

In the production process of the oil cooling pipe, the roundness error of the pipe is mainly measured manually, which has the problem of low detection efficiency and inability to detect in real-time. Based on machine vision, this paper proposes a method for roundness detection and sorting of oil cooling pipes. By establishing the kinematics model of the robot SR7CL, then using Adams and Matlab to simulate kinematics and dynamics to verify the correctness of the model and ensure the accuracy of grasping. Taking the nozzle of the oil cooling pipe as the research object, the characteristics of the nozzle are highlighted through adaptive threshold segmentation, and the contour information are extracted by the Canny algorithm. Finally, used the least square method to detected the roundness error, and the cooling pipes are determined according to the roundness error. This system not only improves the quality and efficiency of detection, but also wives the real-time problems.

关键词： Kinematics Machine vision Median filtering adaptive threshold segmentation Least squares method

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