Robot Visual Localization Through Local Feature Fusion: An Evaluation of Multiple Classifiers Combination Approaches

Campos, Francisco M.; Correia, Luís; Calado, João Manuel Ferreira

doi:10.1007/s10846-013-0016-3

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…It offers image classification and retrieval [3][4][5][6], object recognition and matching [7][8][9], 3D scene reconstruction [10], robot localization [11], object detection and tracking and video processing. All of these processing systems rely on the presence of stable and meaningful features in the image.…”

Section: Computer Vision Systemmentioning

confidence: 99%

Machine Learning framework for image classification

Loussaief¹,

Abdelkrim²

2018

Adv. sci. technol. eng. syst. j.

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Hereby in this paper, we are going to refer image classification. The main issue in image classification is features extraction and image vector representation. We expose the Bag of Features method used to find image representation. Class prediction accuracy of varying classifiers algorithms is measured on Caltech 101 images. For feature extraction functions we evaluate the use of the classical Speed Up Robust Features technique against global color feature extraction. The purpose of our work is to guess the best machine learning framework techniques to recognize the stop sign images.The trained model will be integrated into a robotic system in a future work.

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Section: Computer Vision Systemmentioning

confidence: 99%

Machine Learning framework for image classification

Loussaief¹,

Abdelkrim²

2018

Adv. sci. technol. eng. syst. j.

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“…Many theories and experimental studies have shown that multiple classifier fusion has obvious advantages over single classifier techniques. [16][17][18] Campos et al 19 studied multiple classifier combination approaches of different local feature combiners for the purpose of robot visual localisation. Bigdeli et al 20 presented the fusion of hyperspectral and LIDAR (light detection and ranging) data using a decisiontemplate-based fuzzy multiple classifier system to improve classification accuracy.…”

Section: Discriminatedmentioning

confidence: 99%

Application of Multiple Classifier Fusion in the Discriminant Analysis of near Infrared Spectroscopy for Agricultural Products

Luan

Wang

et al. 2016

Journal of Near Infrared Spectroscopy

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Near infrared spectroscopy combined with chemometrics and pattern recognition has become a primary focus in the discriminant analysis of agricultural products. To date, most studies have focused on using a single classifier to discriminate the origins, varieties and grades of products. Others have focused on using multiple classifier fusion by weighted voting. Due to their attributes of continuity and internal similarity, discriminant models sometimes present poor performance. In this study, we achieved better performance by applying multiple classifier fusion models, including support vector machine (SVM), discriminant partial least squares (DPLS) and principal component and Fisher criterion (PPF). PPF showed continuity and similarity among different parts of tobacco leaves [i.e. upper (B), cutter (C) and lug (X)]. The similarities between each class and the others were quantified to values, and the sum of the similarity values of each class was defined as its similarity. SVM-DPLS-PPF fusion by voting and similarity constraint for decision resulted in better performance, with the correct discriminant rate improved on average by 14.1%, 8.2%, 17.3% and 4.6%compared with those achieved using SVM, DPLS, PPF and SVM-DPLS-PPF fusion by weighted voting for decision, respectively; in addition, the incorrect discriminant rate between B and X was reduced to zero. Therefore, we demonstrated the feasibility of using SVM-DPLS-PPF fusion by voting and similarity constraint for decision to discriminate between different parts of tobacco leaves.This technique could provide a new method for tobacco quality management, computer-aided grading and intelligent acquisition. It also provides a new discriminant method for analysing the attributes of continuity and similarity of agricultural products using near infrared spectroscopy .

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“…In addition, the voting method previously used for robot visual positioning was evaluated. 4 TaeKoo Kang proposed a new visual tracking framework and verified its advantages by mobile robot experiments. During the moving process of the mobile robot, the image sequence generated by the vision system is not static but will slide and vibrate.…”

Section: Introductionmentioning

confidence: 97%

“…According to the principle of basketball motion capture system, the vision system of picking robot can also use feature capture technology to locate the fruit target, so as to realize autonomous operation. 3,4 Since the 1970s, motion capture has been an important method of photographic image analysis in biomechanics research, and it has been increasingly applied in animation production, robot control, human-computer interactive games, sports training, and other fields. 5,6 As a result, a growing number of research institutions and companies have discovered the value of human motion capture, bringing the technology from the laboratory to the market and diversifying the options available.…”

Section: Introductionmentioning

confidence: 99%

Badminton motion capture with visual image detection of picking robotics

2020

International Journal of Advanced Robotic Systems

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In the process of strawberry easily broken fruit picking, in order to reduce the damage rate of the fruit, improves accuracy and efficiency of picking robot, field put forward a motion capture system based on international standard badminton edge feature detection and capture automation algorithm process of night picking robot badminton motion capture techniques training methods. The badminton motion capture system can analyze the game video in real time and obtain the accuracy rate of excellent badminton players and the technical characteristics of badminton motion capture through motion capture. The purpose of this article is to apply the high-precision motion capture vision control system to the design of the vision control system of the robot in the night picking process, so as to effectively improve the observation and recognition accuracy of the robot in the night picking process, so as to improve the degree of automation of the operation. This paper tests the reliability of the picking robot vision system. Taking the environment of picking at night as an example, image processing was performed on the edge features of the fruits picked by the picking robot. The results show that smooth and enhanced image processing can successfully extract edge features of fruit images. The accuracy of the target recognition rate and the positioning ability of the vision system of the picking robot were tested by the edge feature test. The results showed that the accuracy of the target recognition rate and the positioning ability of the motion edge of the vision system were far higher than 91%, satisfying the automation demand of the picking robot operation with high precision.

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Robot Visual Localization Through Local Feature Fusion: An Evaluation of Multiple Classifiers Combination Approaches

Cited by 20 publications

References 22 publications

Machine Learning framework for image classification

Machine Learning framework for image classification

Application of Multiple Classifier Fusion in the Discriminant Analysis of near Infrared Spectroscopy for Agricultural Products

Badminton motion capture with visual image detection of picking robotics

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