A novel method of fish tail fin removal for mass estimation using computer vision

2022 ◽  
Vol 193 ◽  
pp. 106601
Author(s):  
Yinfeng Hao ◽  
Hongjian Yin ◽  
Daoliang Li
Keyword(s):  
Computer Vision ◽  
Mass Estimation ◽  
Novel Method

scholarly journals Real-Time Monitoring of Jet Trajectory during Jetting Based on Near-Field Computer Vision

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 690 ◽  
Author(s):  
Jinsong Zhu ◽  
Wei Li ◽  
Da Lin ◽  
Ge Zhao
Keyword(s):  
Computer Vision ◽  
Prediction Model ◽  
Real Time ◽  
Processing Time ◽  
Near Field ◽  
Image Correction ◽  
Noise Elimination ◽  
Sensor Device ◽  
Jet Trajectory ◽  
Novel Method

A novel method of near-field computer vision (NFCV) was developed to monitor the jet trajectory during the jetting process, which was used to precisely predict the falling point position of the jet trajectory. By means of a high-resolution webcam, the NFCV sensor device collected near-field images of the jet trajectory. Preprocessing of collected images was carried out, which included squint image correction, noise elimination, and jet trajectory extraction. The features of the jet trajectory in the processed image were extracted, including: start-point slope (SPS), end-point slope (EPS), and overall trajectory slope (OTS) based on the proposed mean position method. A multiple regression jet trajectory range prediction model was established based on these trajectory characteristics and the reliability of the model was verified. The results show that the accuracy of the prediction model is not less than 94% and the processing time is less than 0.88s, which satisfy the requirements of real-time online jet trajectory monitoring.

A Novel Neutrosophic Image Segmentation Based on Improved Fuzzy C-Means Algorithm (NIS-IFCM)

2019 ◽  
Vol 34 (05) ◽  
pp. 2055011
Author(s):  
Jing Zhao ◽  
Xiaoli Wang ◽  
Ming Li
Keyword(s):  
Computer Vision ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Classical Problem ◽  
Local Optimum ◽  
The Novel ◽  
Swarm Optimization ◽  
Novel Method ◽  
Fuzzy C Means Algorithm ◽  
Novel Algorithm

Image segmentation is a classical problem in the field of computer vision. Fuzzy [Formula: see text]-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary segmentation effect. A novel method is proposed to solve this problem. In the proposed method, first, the image is transformed into a neutrosophic image. In order to improve the ability of global search, a combined FCM based on particle swarm optimization (PSO) is proposed. Finally, the proposed algorithm is applied to the neutrosophic image segmentation. The results of experiments show that the novel algorithm can eliminate image noise more effectively than the FCM algorithm, and make the boundary of the segmentation area clearer.

Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model

2019 ◽  
Vol 263 ◽  
pp. 288-298 ◽  
Author(s):  
Innocent Nyalala ◽  
Cedric Okinda ◽  
Luke Nyalala ◽  
Nelson Makange ◽  
Qi Chao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cherry Tomato ◽  
Mass Estimation

Automatic mass estimation of Jade perch Scortum barcoo by computer vision

2015 ◽  
Vol 64 ◽  
pp. 42-48 ◽  
Author(s):  
S. Viazzi ◽  
S. Van Hoestenberghe ◽  
B.M. Goddeeris ◽  
D. Berckmans
Keyword(s):  
Computer Vision ◽  
Mass Estimation ◽  
Automatic Mass

scholarly journals A NOVEL METHOD FOR INSPECTION DEFECTS IN COMMERCIAL EGGS USING COMPUTER VISION

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 809-816
Author(s):  
Y. M. Valencia ◽  
J. J. Majin ◽  
V. B. Taveira ◽  
J. D. Salazar ◽  
M. E. Stivanello ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Inspection ◽  
Working Hours ◽  
Inspection System ◽  
Learning Approaches ◽  
Visual Fatigue ◽  
Long Working Hours ◽  
Novel Method ◽  
Classical Image

Abstract. The objective of this work is to compare the use of classical image processing approaches with deep learning approaches in a visual inspection system for defects in commercial eggs. Currently, many industries perform the detection of defects in eggs manually, this implies a large number of workers with long working hours who are exposed to visual fatigue and physical and mental discomfort. As a solution, this work proposes to develop an automatic inspection technique for defects in eggs using computer vision, capable of being operable in the industry. Different image processing approaches were evaluated in order to determine the best solution in terms of performance and processing time.

Research on Volume Measurement Technology for Rail Tanker Based on Computer Vision

2012 ◽  
Vol 157-158 ◽  
pp. 646-651
Author(s):  
Ji Bin Zhao ◽  
Ren Bo Xia ◽  
Wei Jun Liu ◽  
Tao Fu ◽  
Yi Jun Huang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Volume Measurement ◽  
Scattered Data ◽  
High Accuracy ◽  
Measurement Technology ◽  
Identification Algorithm ◽  
Localization Algorithm ◽  
Complex Environment ◽  
Accurate Identification ◽  
Novel Method

In this paper, a computer vision based volume measurement system for rail tanker is proposed. Considering the complex environment, we propose an accurate identification algorithm of coded point and a precise localization algorithm. By investigating the epipolar geometry among three views, this paper develops a novel method for metric reconstruction of a scene based on the trilinear relations. Through the method of ordering incomplete scattered data presented, we construct a series of cross-section contours for a tanker and achieve a precise reconstruction of the surface of the tanker. Experiments show that, the volume measurement method for rail tanker based on computer vision can be operated with simplicity and high accuracy.

scholarly journals A New Dew and Frost Detection Sensor Based on Computer Vision

2014 ◽  
Vol 31 (12) ◽  
pp. 2692-2712 ◽  
Author(s):  
Lei Zhu ◽  
Zhiguo Cao ◽  
Wen Zhuo ◽  
Ruicheng Yan ◽  
Shuqing Ma
Keyword(s):  
Computer Vision ◽  
Field Experiments ◽  
Visual Features ◽  
Analysis Method ◽  
Glass Substrates ◽  
Observation Systems ◽  
Novel Method ◽  
Weather Stations ◽  
Passive Microwave Devices ◽  
Beijing China

Abstract Many weather features such as precipitation and snow depth can be recorded using automatic surface observation systems. However, automatically observing dew and frost presents several problems. Many studies have used various wetness sensors and passive microwave devices to detect dew. Unfortunately, several of these sensors are complex, and only a few are capable of detecting frost. This paper proposes a novel method for indirectly detecting dew and frost based on computer vision. The setup is simple, inexpensive, and only requires images of several glass substrates near the underlying surface. Images taken during dew or frost formation exhibit distinct changes in hierarchical visual features. These changes are detected by tracking the variations of several low-level statistical features that are extracted from the images in time. Additionally, an effective texture analysis method is proposed to describe the morphology of frost. Field experiments were conducted at several weather stations in Beijing, China. The validation of the method for measuring the onset and duration of dew/frost on short grass shows that 1) the proposed computer-vision-based algorithm achieves an accuracy of approximately 90% in discriminating among dewy, frosty, and dry nights based on the hourly manual observations of the grass surface and 2) the algorithm is also capable of measuring the duration of dew and frost on grass with about 70% accuracy.

scholarly journals Cast Shadow Detection for Surveillance System Based on Tricolor Attenuation Model

2011 ◽  
Vol 2-3 ◽  
pp. 635-639
Author(s):  
Lin Lin Zhu ◽  
Jian Dong Tian ◽  
Yan Dong Tang ◽  
Jian Da Han
Keyword(s):  
Computer Vision ◽  
Surveillance System ◽  
Moving Target ◽  
Target Area ◽  
Attenuation Model ◽  
Cast Shadow ◽  
Outdoor Scenes ◽  
Novel Method ◽  
Relationship Of ◽  
The Relationship

Shadows bring some undesirable problems in computer vision, such as object detecting in outdoor scenes. In this paper, we propose a novel method for cast shadow detecting for moving target in surveillance system. This measure is based on tricolor attenuation model, which describes the relationship of three color channel’s attenuation in image when shadow happens. According to this relationship, the cast shadow is removed from the detected moving area, only the target area is left. Some experiments were done, and their results validate the performance of our method.

scholarly journals Assessment of locomotion in chlorine exposed mice by computer vision and neural networks

2012 ◽  
Vol 112 (6) ◽  
pp. 1064-1072 ◽  
Author(s):  
Aristotelis S. Filippidis ◽  
Sotirios G. Zarogiannis ◽  
Alan Randich ◽  
Timothy J. Ness ◽  
Sadis Matalon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Two Dimensions ◽  
Analgesic Agent ◽  
Network Algorithms ◽  
Novel Method ◽  
Noxious Stimuli ◽  
Underlying Mechanisms

Assessment of locomotion following exposure of animals to noxious or painful stimuli can offer significant insights into underlying mechanisms of injury and the effectiveness of various treatments. We developed a novel method to track the movement of mice in two dimensions using computer vision and neural network algorithms. By using this system we demonstrated that mice exposed to chlorine (Cl2) gas developed impaired locomotion and increased immobility for up to 9 h postexposure. Postexposure administration of buprenorphine, a common analgesic agent, increased locomotion and decreased immobility times in Cl2- but not air-exposed mice, most likely by decreasing Cl2-induced pain. This method can be adapted to assess the effectiveness of various therapies following exposure to a variety of chemical and behavioral noxious stimuli.

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