Apple tree branch segmentation from images with small gray-level difference for agricultural harvesting robot

Ji, Wei; Qian, Zhijie; Xu, Bo; Yun, Tao; Zhao, Debin; Ding, Shihong

doi:10.1016/j.ijleo.2016.09.044

Cited by 37 publications

(18 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main reason for the generation of false branch is that the edge of the recognized binary image is not smooth due to some small bumps. 1 The following definitions are proposed before the skeleton pruning:…”

Section: Skeleton Feature Extraction For Apple Tree Branchmentioning

confidence: 99%

“…Therefore, the accurate positioning of branch is one of the key techniques in the obstacle perception of fruit harvesting robot. In our prior research of branch recognition, 1 we mainly focused on identifying the two-dimensional space of branches and did not included the depth information of branches. This work is different from the above 1 in that it focuses on branch localization including depth information based on the branch recognized.…”

Section: Introductionmentioning

confidence: 99%

“…The branch can be regarded as the connection of many feature points which perform the structure of the branch, and therefore the localization of these feature points can represent the localization of branch. According to earlier studies, 1,[10][11][12] it is difficult to process stereo matching of the branch images because of the large number of image pixels which are not only various but complex. In this condition, it is necessary to extract the related parameters of the corresponding feature regions or the feature points of two images as the characteristic parameters of the stereo matching.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Branch localization method based on the skeleton feature extraction and stereo matching for apple harvesting robot

Meng

Qian

et al. 2017

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

Aiming at the problem of apple branch obstacle localization in fruit picking process of harvesting robot manipulator, in order to obtain three-dimensional information of the apple branch obstacle, the binocular stereo vision localization method of apple branch obstacle is proposed. Firstly, branch skeleton is extracted by morphological thinning method and then the feature skeleton is obtained after removing the false branch and recovering the occluded branch. After that, the endpoints and bifurcation points regarded as match feature points are extracted from skeleton, and the stereo matching algorithm based on features is adopted. Then, the depth information of branch obstacle is obtained on the basis of triangulation theory. Finally, the experiment results for apple tree branches localization show that the error lies in ±6.2 mm. Moreover, the error is merely ±1.5 mm when the distance between the object and the binocular camera is 1000 mm, which meets with localization accuracy requirements of apple harvesting robot visual system.

show abstract

Section: Skeleton Feature Extraction For Apple Tree Branchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Branch localization method based on the skeleton feature extraction and stereo matching for apple harvesting robot

Meng

Qian

et al. 2017

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the medical field, it was successfully applied in breast ultrasound and mammography image enhancement [11,12], in cell image segmentation [13,14], in retinal vessel image processing [15,16], and in enhancement of bone fracture images [17]. Beyond medical field, CLAHE was applied to enhance underwater images [18,19], to perform fruit segmentation in agricultural systems [20,21], and to assist driving systems to improve vehicle detection [22], traffic sign detection [23], and pedestrian detection [24].…”

Section: Introductionmentioning

confidence: 99%

Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization

Campos

Mastelini

Aguiar

et al. 2019

J Image Video Proc.

View full text Add to dashboard Cite

Contrast enhancement algorithms have been evolved through last decades to meet the requirement of its objectives. Actually, there are two main objectives while enhancing the contrast of an image: (i) improve its appearance for visual interpretation and (ii) facilitate/increase the performance of subsequent tasks (e.g., image analysis, object detection, and image segmentation). Most of the contrast enhancement techniques are based on histogram modifications, which can be performed globally or locally. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a method which can overcome the limitations of global approaches by performing local contrast enhancement. However, this method relies on two essential hyperparameters: the number of tiles and the clip limit. An improper hyperparameter selection may heavily decrease the image quality toward its degradation. Considering the lack of methods to efficiently determine these hyperparameters, this article presents a learning-based hyperparameter selection method for the CLAHE technique. The proposed supervised method was built and evaluated using contrast distortions from well-known image quality assessment datasets. Also, we introduce a more challenging dataset containing over 6200 images with a large range of contrast and intensity variations. The results show the efficiency of the proposed approach in predicting CLAHE hyperparameters with up to 0.014 RMSE and 0.935 R 2 values. Also, our method overcomes both experimented baselines by enhancing image contrast while keeping its natural aspect.

show abstract

“…Methods of visually identifying the fruit on a tree include monocular vision identification, binocular vision identification, static fruit identification, dynamic fruit identification, single fruit identification, overlapped & blocked fruit identification, and apple identification at night [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Apple Binocular Visual Identification and Positioning System

Liu¹,

Xin²,

Shi³

et al. 2019

RIA

View full text Add to dashboard Cite

In order to realize the autonomous recognition and location of apple, an apple recognition and location system based on LabVIEW software, IMAQ Vision kit and binocular Vision was designed in the work. The system identified apples on the trees by background subtraction based on difference of surface color, object identification based on circle and binocular stereo measurement of the apples on trees. The results showed that the system has accomplished image acquisition, preprocessing, recognition and depth recovery, realizing the positioning of apple on LabVIEW. The system can be transplanted into the fully automatic picking system to pick apples more accurately and quickly.

show abstract

Apple tree branch segmentation from images with small gray-level difference for agricultural harvesting robot

Cited by 37 publications

References 9 publications

Branch localization method based on the skeleton feature extraction and stereo matching for apple harvesting robot

Branch localization method based on the skeleton feature extraction and stereo matching for apple harvesting robot

Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization

Apple Binocular Visual Identification and Positioning System

Contact Info

Product

Resources

About