Multi-Feature Fusion Algorithm in VR Panoramic Image Detail Enhancement Processing

Abstract

VR panoramic image is an image imaging technology that covers a wide range of scenes. Its imaging range is much larger than that of traditional imaging systems, and it can fully reflect all the information of the imaging space. However, the current VR panoramic images have the problems that the details are not obvious enough and the processing is not comprehensive enough. In view of the shadow problem in VR panoramic images, this paper proposes a multi-feature fusion VR panoramic image shadow elimination algorithm, which uses HSV color features and LBP / LSFP texture features to obtain shadow detection results, and then obtains the final detection results by fusion. The experimental results prove that while ensuring a low missed detection rate, the false detection rate is greatly reduced. The comprehensive evaluation index Avg in this paper is improved by 3.4% compared with the shadow elimination algorithm based on a single feature. This paper proposes an image saliency detection algorithm and image detail enhancement algorithm based on multi-feature fusion. The final saliency map is obtained through linear fusion. Experiments prove that the image detail enhancement algorithm based on multi-feature fusion mentioned in this paper has achieved very good results. This paper compares the performance of single-feature and multi-feature fusion algorithms. HSV, LBP and LSFP obtain 93.39% accuracy and 0.8708 correlation coefficient. The effect of multi-fusion fusion is better than that of single feature.

Multimodal Biometrics Fusion Algorithm Using Deep Reinforcement Learning

“…However, the algorithm has the problem of a low error rate. M. Zhu and X. Yu [5] proposed a multifeature fusion algorithm in detail enhancement of VR panoramic images. e shadow detection results are obtained by using HSV color features and texture features, and then the final detection results are obtained by fusion.…”

MM-LMF: A Low-Rank Multimodal Fusion Dangerous Driving Behavior Recognition Method Based on FMCW Signals

“…In the process of FMCW radar frame signal preprocessing, features such as Doppler, velocity, scattering point difference, and amplitude are usually extracted separately, and two kinds of excellent features are randomly combined [13][14][15], which may not solve the problem of extracting features given the coupling relationship between each feature and the balance of feature space. In the literature [16][17][18], other methods explore the high-dimensional representation for multi-feature fusion, use the multi-feature fusion algorithm of HSV, LBP, and LSFP to improve high-dimensional fusion representation, and binarize the DM image constructed by multi-feature fusion. Human complex targets monitor small target features, perform algorithm fusion for local entropy, average gradient strength, and other features, and perform peak normalization processing in vector space (MFVS).…”

Integrated Architecture Model of Tourism Information Service in Smart Scenic Spots Based on Hybrid Cloud

“…In reference [10], the method of three-dimensional measurement and reconstruction of panoramic three-dimensional perception is used by using the optical characteristics of quadric hyperboloid mirror. Reference [11] proposes a multifunctional fusion VR panoramic image shadow elimination algorithm, which uses HSV color features and LBP/ LSFP texture functions to obtain image detection results. Literature [12] considers 360 degree panoramic images in the graphic design of virtual museums and solves graphic design problems.…”