Visual Recognition System for Cleaning Tasks by Humanoid Robots

Attamimi, Muhammad; Araki, Takaya; Nagaoka, Tomoaki; Nagai, Takayuki

doi:10.5772/56629

Cited by 18 publications

(14 citation statements)

References 26 publications

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“…Support Vector Machine (SVM) [37] is employed in all prior mentioned feature extraction methods to classify the apple images into defective and non-defective. The SVM technique is chosen due to its high accuracy performance and well established efficiency in many image recognition fields [38,39,40,41,42]. To optimize the performance of SVM, the libsvm [43] grid search algorithm is used to determine the kernel parameters.…”

Section: Methodsmentioning

confidence: 99%

Vision-Based Apple Classification for Smart Manufacturing

Ismail

Idris

Ayub

et al. 2018

Sensors

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Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s.

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Section: Methodsmentioning

confidence: 99%

Vision-Based Apple Classification for Smart Manufacturing

Ismail

Idris

Ayub

et al. 2018

Sensors

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“…The Naïve Bayes are also an efficient and effective machine learning classifier [81][82][83], while Softmax classifier is one of the most commonly-used logistic regressions classifier especially for multi-class classification [84,85]. Finally, the SVM classifier are also selected due to it is well established technique in many image recognition tasks and its high accuracy performance [78,[86][87][88][89]. Their performance is evaluated and a test decision of the best performing classifier on the proposed method determine the suitable classifier for the proposed method.…”

Section: Classificationmentioning

confidence: 99%

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

et al. 2019

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Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called Curvelet Wavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAW with a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy.

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“…9. We used the method proposed in [17] to perform object recognition. We compared the recognition results of the following three cases.…”

Section: B the Influence Of Occlusion On Object Recognitionmentioning

confidence: 99%

A Visual Sensor for Domestic Service Robots

Attamimi

Nagai

2018

JAREE

Self Cite

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In this study, we present a visual sensor for domestic service robots, which can capture both color information and three-dimensional information in real time, by calibrating a time of flight camera and two CCD cameras. The problem of occlusions is solved by the proposed occlusion detection algorithm. Since the proposed sensor uses two CCD cameras, missing color information of occluded pixels is compensated by one another. We conduct several evaluations to validate the proposed sensor, including investigation on object recognition task under occluded scenes using the visual sensor. The results revealed the effectiveness of proposed visual sensor.

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Visual Recognition System for Cleaning Tasks by Humanoid Robots

Cited by 18 publications

References 26 publications

Vision-Based Apple Classification for Smart Manufacturing

Vision-Based Apple Classification for Smart Manufacturing

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

A Visual Sensor for Domestic Service Robots

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