The widespread dissemination and sharing of digital images have led to an urgent need for image copyright protection. To address this challenge, this study proposes a comprehensive image copyright protection system based on blockchain and image processing algorithms. Firstly, image features are extracted through binarization processing and the ORB algorithm. The GMS algorithm is then employed for feature selection to enhance the accuracy of feature matching. Subsequently, the InterPlanetary File System (IPFS) is utilized to store images and related data in a decentralized network, ensuring the immutability and traceability of information. Finally, image copyright management throughout its lifecycle, including registration, querying, and transfer, is implemented through Ethereum smart contracts.
Reliable human detection is important for a wide range of applications. In this paper, a particular designed method for real-time human detection has been developed. The method is robustly in cluttered and dynamic environments, and deals with depth images. The method has two steps, first the plausible candidate positions are localized by a super-pixel based segmentation and merging approach. Then we utilize a descriptor encoding the joint of depth difference information and 3D geometric characteristics of human upper body to refine the candidates by a deep randomized decision forest classifier. Our approach, which detects human in depth images, allows very fast speed and high accuracy in three publicly available datasets.
Reliable human detection and tracking is important for a wide range of applications. In this paper, a particular designed method for real-time human detection has been proposed. The method is robustly in cluttered and dynamic environments, and deals with depth images. The method has two steps, first the hypothesis human head regions are localized by a superpixel based segmentation and merging approach. Then we utilize a multi-channel measurement and employ neural network for classification between human and non-human region refinement. Our approach, which detects human in depth images, allows very fast speed and high accuracy in three publicly available datasets.
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