Graph representation for two-dimensional scene understanding by the cognitive vision module

Bielecki, Andrzej; Śmigielski, Piotr

doi:10.1177/1729881416682694

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…It can be potentially effective in each problem in which contour analysis according to its shape properties is one of the key tasks. Scene analysis by cognitive vision module of autonomous robots can be put as an example of such a problem [ 41 – 43 ]. In the mentioned papers the industrial scene is represented by using only polygonal shapes.…”

Section: Discussionmentioning

confidence: 99%

The shape language in application to the diagnosis of cervical vertebrae pathology

2018

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In this paper the possibility of classification of X-ray images of the cervical vertebrae is studied. The images should be classified into one of the following classes—the images of healthy vertebrae and the images of vertebrae with syndesmophytes. The vertebra contours, described unambiguously by using the generalized shape language, are the basis of the analysis. As a result, the contour is represented as a chain of sinquads that determine switches. The found switches are the characteristic points of the analyzed contour. In these points additional features of the contour are determined. On the basis of these features two aforementioned classes of images are defined as fuzzy sets. Such an approach allows us to create a hierarchical algorithm of classification based on the syntactic and fuzzy description of the contour.

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Section: Discussionmentioning

confidence: 99%

The shape language in application to the diagnosis of cervical vertebrae pathology

2018

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“…Its vision system should perform two mutually independent tasks. First of all, it should ensure navigation in the environment on the basis of the algorithm presented by Bielecki and Śmigelski [ 44 ]. Precise determination of the location of the fruit outside the plant and inside it, i.e., fruit obscured by the shoots of the plant and its leaves, is the second task.…”

Section: Cognitive Vision System Of the Robotmentioning

confidence: 99%

“…Since the greenhouse environment is a human-made environment, the worked-out algorithm [ 44 , 55 ] can be almost strictly adapted. The only modification is connected to the fact that the harvesting robot will operate inside the bounded area.…”

Section: The Described System and An Autonomous Robotmentioning

confidence: 99%

The Concept of the Constructional Solution of the Working Section of a Robot for Harvesting Strawberries

Kurpaska

Bielecki

Sobol

et al. 2021

Sensors

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Strawberry fruits are products of high commercial and consumption value, and, at the same time, they are difficult to harvest due to their very low mechanical strength and difficulties in identifying them within the bush. Therefore, robots collecting strawberries should be equipped with four subsystems: a video object detection system, a collecting arm, a unit for the reception and possible packaging of the fruit, and a traction system unit. This paper presents a concept for the design and operation of the working section of a harvester for strawberry fruit crops grown in rows or beds, in open fields, and/or under cover. In principle, the working section of the combine should meet parameters comparable with those of manually harvested strawberries (efficiency, quality of harvested fruit) and minimise contamination in the harvested product. In order to meet these requirements, in the presented design concept, it was assumed that these activities would be performed during harvesting with the natural distribution of fruits within the strawberry bush, and the operation of the working head arm maneuvering in the vicinity of the picked fruit, the fruit receiving unit, and other obstacles was developed on the basis of image analysis, initially general, and in detail in the final phase. The paper also discusses the idea of a vision system in which the algorithm used has been positively tested to identify the shapes of objects, and due to the similarity of space, it can be successfully used for the correct location of strawberry fruit.

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“…An autonomous robot designed for such missions needs to be equipped with cognitive autonomy [5]. It contains certain tools for understanding the scene as well as means of precise, collision-free navigation between obstacles [6,7].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Outdoor Analysis of Single Synthetic Building Structures by an Unmanned Flying Agent Using Monocular Vision

Bielecki

Śmigielski²

2021

Sensors

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An algorithm designed for analysis and understanding a 3D urban-type environment by an autonomous flying agent, equipped only with a monocular vision, is presented. The algorithm is hierarchical and is based on the structural representation of the analyzed scene. Firstly, the robot observes the scene from a high altitude to build a 2D representation of a single object and a graph representation of the 2D scene. The 3D representation of each object arises as a consequence of the robot’s actions, as a result of which it projects the object’s solid on different planes. The robot assigns the obtained representations to the corresponding vertex of the created graph. The algorithm was tested by using the embodied robot operating on the real scene. The tests showed that the robot equipped with the algorithm was able not only to localize the predefined object, but also to perform safe, collision-free maneuvers close to the structures in the scene.

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Graph representation for two-dimensional scene understanding by the cognitive vision module

Cited by 8 publications

References 19 publications

The shape language in application to the diagnosis of cervical vertebrae pathology

The shape language in application to the diagnosis of cervical vertebrae pathology

The Concept of the Constructional Solution of the Working Section of a Robot for Harvesting Strawberries

Three-Dimensional Outdoor Analysis of Single Synthetic Building Structures by an Unmanned Flying Agent Using Monocular Vision

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