Salient Object Detection based on Panoramic Images

Abstract

This paper focuses on the problem of salient object detection in panoramic images. Firstly, it expounds the conceptual characteristics of panoramic images and the definition and role of salient object detection, analyzes the relationship between the two and key technologies. By reviewing existing panoramic image preprocessing techniques, feature extraction methods, and saliency model algorithms, the research status and challenges in this field are systematically summarized. On this basis, an improved salient object detection method for panoramic images is proposed, and its theoretical basis, algorithm design, and implementation process are elaborated. Experimental results show that this method has significant improvements in detection accuracy and efficiency. The research further explores the potential applications of this technology in multiple fields, and verifies its practical effects through case studies. The innovation of this paper lies in optimizing the salient object detection algorithm based on the characteristics of panoramic images, providing new technical ideas for related applications. Finally, the limitations of the research are summarized, and future research directions are prospected, including algorithm optimization and multimodal data fusion.