Dynamic mosaicking: combining A* algorithm with fractional Brownian motion for an optimal seamline detection

Laaroussi, Saadeddine; Baataoui, Aziz; Halli, Akram; Satori, Khalid

doi:10.1049/iet-ipr.2019.1619

Cited by 5 publications

(5 citation statements)

References 38 publications

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“…The results of the proposed approach were compared with Uyttendaele et al [15]'s method that uses regions of difference (RoD method), the combined SIFT with dynamic programming (CSDP method) [18], the optimal seamline detection via graph cut's method [19] (Graphcut method) and the modified fractional Brownian motion with A* algorithm method [24] (Modified fBm). The results were then shown visually and compared by using similarity measures: the structure similarity index measure (SSIM) [31], the multi‐scale structure similarity index measure (MS‐SSIM) [32]), the blind/referenceless image spatial quality evaluator (BRISQUE) and the computation time.…”

Section: Analysis and Experimental Resultsmentioning

confidence: 99%

“…The modified fBm combined with A* algorithm method [24] is a seamline based method. This approach computes its energy map by using a modified fBm with the similarity measure zero normalised cross‐correlation instead of a random noise function.…”

Section: Analysis and Experimental Resultsmentioning

confidence: 99%

“…In some of our previous works [23, 24], we worked on the creation of seamline based methods. At first [23], we tried to control the seamline by defining areas in the energy map that could create artefacts and areas that are static and added a weight to them so that the seamline will go through the static areas.…”

Section: Introductionmentioning

confidence: 99%

“…At first [23], we tried to control the seamline by defining areas in the energy map that could create artefacts and areas that are static and added a weight to them so that the seamline will go through the static areas. Then we proposed another method [24] where an energy map was computed in which each pixel has a unique value using a modified fractional Brownian motion so that it is possible to use the A* algorithm as a seamline.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the approaches in this field, deal with the objects in motion two images at a time [7–11, 14, 15, 17–19, 21–26] or at best three images [16] which can be time consuming when dealing with a large number of images.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Item‐to‐item recommender system with simultaneous use of multiple images for image mosaicking creation in dynamic scenes

Laaroussi

Baataoui

Halli³

et al. 2020

IET image process

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Image mosaicking is a series of successive algorithms that use a sequence of images or a video of a scene to create a single image with a wider field of view of the whole scene. In most cases, when dynamic objects exist in the input data, issues, such as ghosting, parallax effects or object duplication, are visible in the resulting mosaic. These technical errors appear when the objects in motion aren't properly treated. In order to create good result mosaics, a new method is presented in this paper. The proposed approach uses all the images at the same time to divide the images into different areas by using k‐means clustering to create categories, then each category is recommended from the original images with a recommender system. In fact, by considering each image as a user, each pixel as an item, and each item belonging to a category, it is possible to use a recommender system by computing scores with the item profiles. The resulting mosaic will then be a new user to the system. Furthermore, by clustering the images, projection errors are avoided and a better quality mosaic can be created as is seen in the obtained results.

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Section: Analysis and Experimental Resultsmentioning

confidence: 99%

Section: Analysis and Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Item‐to‐item recommender system with simultaneous use of multiple images for image mosaicking creation in dynamic scenes

Laaroussi

Baataoui

Halli³

et al. 2020

IET image process

Self Cite

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Obstacle Avoidance Path Planning for Apple Picking Robotic Arm Incorporating Artificial Potential Field and A^* Algorithm

Zhuang,

Li,

Ding

2023

IEEE Access

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With the development and maturity of the automated robotics industry, more and more apple plantations are introducing automated picking robotic arms for fruit picking. However, the complex environment in which apple fruit is picked has made it an urgent problem to optimise the robot's picking performance through obstacle avoidance path planning. The experiment selects the Six degrees of freedom manipulator as the research object, and on the basis of its Kinematics analysis, introduces the introduction of artificial potential field (APF) to carry out the path planning of the manipulator. At the same time, it integrates it with A * algorithm to jointly achieve the optimization of the parameters of the obstacle avoidance path of the manipulator. In addition, in order to avoid parameter optimization falling into local extremum during the path planning process, the IRRT algorithm is incorporated to re plan the path, improve the smoothness of the path, and finally verify its obstacle avoidance effect through simulation experiments. The results showed that in the convergence comparison, the research method had the minimum loss function value and the stable fitness value as soon as the iteration proceeded to the 50th and 20th generation, respectively. A On the dataset, the research method had the minimum MAPE value when the iteration proceeded to the 45th generation, with a value close to 0. At the same moment, the MAPE values of the IAPF algorithm, the IRRT algorithm and the literature were 0.052%, 0.108% and 0.218%, respectively. In the practical application analysis, when the robot arm starts running in three different starting positions a, b and c, the IRRT algorithm's obstacle avoidance path has a larger arc and tends to reach the target location through a longer path, while the research method tends to find a relatively closer obstacle avoidance path that can be passed smoothly. The above results show that the research method is highly adaptable to robotic arm path avoidance planning and can complete obstacle avoidance path planning faster and more reasonably, providing new technical support for optimising the path planning system of apple picking robots.INDEX TERMS Robotic arm, obstacle avoidance path planning, APF, A * algorithm.

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A Single Source Optimal Reachable Path Query Method for Gene Regulatory Network

Zhang

Wang

Ding

et al. 2021

The 2nd International Conference on Computing and Data Science

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Dynamic mosaicking: combining A* algorithm with fractional Brownian motion for an optimal seamline detection

Cited by 5 publications

References 38 publications

Item‐to‐item recommender system with simultaneous use of multiple images for image mosaicking creation in dynamic scenes

Item‐to‐item recommender system with simultaneous use of multiple images for image mosaicking creation in dynamic scenes

Obstacle Avoidance Path Planning for Apple Picking Robotic Arm Incorporating Artificial Potential Field and A^* Algorithm

A Single Source Optimal Reachable Path Query Method for Gene Regulatory Network

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Dynamic mosaicking: combining A* algorithm with fractional Brownian motion for an optimal seamline detection

Cited by 5 publications

References 38 publications

Item‐to‐item recommender system with simultaneous use of multiple images for image mosaicking creation in dynamic scenes

Item‐to‐item recommender system with simultaneous use of multiple images for image mosaicking creation in dynamic scenes

Obstacle Avoidance Path Planning for Apple Picking Robotic Arm Incorporating Artificial Potential Field and A* Algorithm

A Single Source Optimal Reachable Path Query Method for Gene Regulatory Network

Contact Info

Product

Resources

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Obstacle Avoidance Path Planning for Apple Picking Robotic Arm Incorporating Artificial Potential Field and A^* Algorithm