Lingyun Xiang¹, Hang Fu¹, Chunfang Yang^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 325-345, 2025, DOI:10.32604/cmc.2025.062834 - 09 June 2025

Abstract In the Visual Place Recognition (VPR) task, existing research has leveraged large-scale pre-trained models to improve the performance of place recognition. However, when there are significant environmental differences between query images and reference images, a large number of ineffective local features will interfere with the extraction of key landmark features, leading to the retrieval of visually similar but geographically different images. To address this perceptual aliasing problem caused by environmental condition changes, we propose a novel Visual Place Recognition method with Cross-Environment Robust Feature Enhancement (CerfeVPR). This method uses the GAN network to generate similar… More >

Shaoqi Hou^1,2,3,*, Zebang Qin², Chenyu Wu², Guangqiang Yin², Xinzhong Wang¹, Zhiguo Wang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5019-5033, 2025, DOI:10.32604/cmc.2025.058233 - 06 March 2025

Abstract Visual Place Recognition (VPR) technology aims to use visual information to judge the location of agents, which plays an irreplaceable role in tasks such as loop closure detection and relocation. It is well known that previous VPR algorithms emphasize the extraction and integration of general image features, while ignoring the mining of salient features that play a key role in the discrimination of VPR tasks. To this end, this paper proposes a Domain-invariant Information Extraction and Optimization Network (DIEONet) for VPR. The core of the algorithm is a newly designed Domain-invariant Information Mining Module (DIMM)… More >

Kazeem Oyebode¹, Shengzhi Du^2,*, Barend Jacobus van Wyk³

Computer Systems Science and Engineering, Vol.38, No.2, pp. 229-238, 2021, DOI:10.32604/csse.2021.015372 - 23 April 2021

Abstract Context-aware facial recognition regards the recognition of faces in association with their respective environments. This concept is useful for the domestic robot which interacts with humans when performing specific functions in indoor environments. Deep learning models have been relevant in solving facial and place recognition challenges; however, they require the procurement of training images for optimal performance. Pre-trained models have also been offered to reduce training time significantly. Regardless, for classification tasks, custom data must be acquired to ensure that learning models are developed from other pre-trained models. This paper proposes a place recognition model… More >