Machine learning meets advanced robotic manipulation

Nahavandi, Saeid; Alizadehsani, Roohallah; Nahavandi, Darius; Lim, Chee Peng; Kelly, Kevin; Bello, Fernando

doi:10.1016/j.inffus.2023.102221

Cited by 5 publications

(1 citation statement)

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“…Many traditional image recognition algorithms are limited in their ability to handle variations in scale, orientation, illumination, and viewpoint. They also often struggle with classification tasks when cluttered or occluded objects are present in images 5 . To overcome these challenges, researchers have turned to more advanced image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Enhancing computer image recognition with improved image algorithms

Huang,

Yao,

Zhang

et al. 2024

Sci Rep

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Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved.

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Section: Introductionmentioning

confidence: 99%