Center-based clustering for line detection and application to crop rows detection

Vidović, Ivan; Scitovski, Rudolf

doi:10.1016/j.compag.2014.10.014

Cited by 33 publications

(12 citation statements)

References 37 publications

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“…For solving multiple line detection problem (2.5), we can find a modification of the well-known incremental algorithm in the literature [1,2,18,25] known as the Incremental Method for Line Detection (IMLD).…”

Section: Modification Of the Incremental Methodmentioning

confidence: 99%

“…The multiple line detection problem finds application in various fields, such as computer vision and image processing [5,11], robotics [22], laser range measurements [6], and crop row detection in agriculture [10,25], etc.…”

Section: Introductionmentioning

confidence: 99%

“…In relation thereto, [1] considers the clusterwise linear regression problem by using the incremental method (see also [2,18]), and a line detection algorithm based on probabilistic clustering is proposed in [3] and compared to the fuzzy clustering approach in applications to real-world images. [25] proposes a combination of center-based clustering and total least squares for the purpose of solving the crop row detection problem in agriculture.…”

Section: Introductionmentioning

confidence: 99%

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A fast and efficient method for solving the multiple line detection problem

Scitovski¹,

Radojicic²,

Sabo³

2019

Rad Hrvatske akademije znanosti i umjetnosti. Matematičke znano

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In this paper, we consider the multiple line detection problem on the basis of a data points set coming from a number of lines not known in advance. A new and efficient method is proposed, which is based upon center-based clustering, and it solves this problem quickly and precisely. The method has been tested on 100 randomly generated data sets. In comparison to the incremental algorithm, the method gives significantly better results. Also, in order to identify a partition with the most appropriate number of clusters, a new index has been proposed for the case of a cluster whose lines are cluster-centers. The index can also be generalized for other geometrical objects.

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Section: Modification Of the Incremental Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A fast and efficient method for solving the multiple line detection problem

Scitovski¹,

Radojicic²,

Sabo³

2019

Rad Hrvatske akademije znanosti i umjetnosti. Matematičke znano

Self Cite

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“…About the last step, a variety of navigation line detection methods have been proposed in recent years. Generally, these methods are classified into several categories according to their detection principles, such as HT (Hough transform), LR (linear regression), SA (speckle analysis), SV (stereo vision), and HF (horizontal fringes) [16][17][18][19][20]. It is worth noting that the crop row is approximated as a straight line in the above methods.…”

Section: Introductionmentioning

confidence: 99%

Research on Vision-Based Navigation for Plant Protection UAV under the Near Color Background

et al. 2019

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GPS (Global Positioning System) navigation in agriculture is facing many challenges, such as weak signals in orchards and the high cost for small plots of farmland. With the reduction of camera cost and the emergence of excellent visual algorithms, visual navigation can solve the above problems. Visual navigation is a navigation technology that uses cameras to sense environmental information as the basis of an aircraft flight. It is mainly divided into five parts: Image acquisition, landmark recognition, route planning, flight control, and obstacle avoidance. Here, landmarks are plant canopy, buildings, mountains, and rivers, with unique geographical characteristics in a place. During visual navigation, landmark location and route tracking are key links. When there are significant color-differences (for example, the differences among red, green, and blue) between a landmark and the background, the landmark can be recognized based on classical visual algorithms. However, in the case of non-significant color-differences (for example, the differences between dark green and vivid green) between a landmark and the background, there are no robust and high-precision methods for landmark identification. In view of the above problem, visual navigation in a maize field is studied. First, the block recognition method based on fine-tuned Inception-V3 is developed; then, the maize canopy landmark is recognized based on the above method; finally, local navigation lines are extracted from the landmarks based on the maize canopy grayscale gradient law. The results show that the accuracy is 0.9501. When the block number is 256, the block recognition method achieves the best segmentation. The average segmentation quality is 0.87, and time is 0.251 s. This study suggests that stable visual semantic navigation can be achieved under the near color background. It will be an important reference for the navigation of plant protection UAV (Unmanned Aerial Vehicle).

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“…Several papers about image analysis in case of data missing in an image or a partial missing edge image can be found in the proceedings [1]. In [21] and [36], the problem of finding guidance information from the crop row structure in case of missing plants and weeds is considered.…”

Section: Introductionmentioning

confidence: 99%

Searching for an Optimal Partition of Incomplete Data with Application in Modeling Energy Efficiency of Public Buildings

2018

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In this paper, we consider the problem of searching for an optimal partition with the most appropriate number of clusters for an incomplete data set A ⊂ R n in which several outliers might occur. Special attention is given to the application of the Least Squares distance-like function. The procedure of preparing the incomplete data set and the outlier elimination procedure are proposed such that the clustering process gives acceptable solutions. Appropriate justifications with proof are provided for these procedures. An incremental algorithm for searching for optimal partitions with 2, 3,. .. clusters is applied on the prepared data set. After that, by using the Davies-Bouldin and the Calinski-Harabasz index the most appropriate number of clusters is determined. The whole procedure is organized as an algorithm given in the paper. In order to illustrate its applicability, the above steps are applied on the real data set of public buildings and their energy efficiency data, providing clear clusters that could be used for further modeling procedures.

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Center-based clustering for line detection and application to crop rows detection

Cited by 33 publications

References 37 publications

A fast and efficient method for solving the multiple line detection problem

A fast and efficient method for solving the multiple line detection problem

Research on Vision-Based Navigation for Plant Protection UAV under the Near Color Background

Searching for an Optimal Partition of Incomplete Data with Application in Modeling Energy Efficiency of Public Buildings

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