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[This corrects the article DOI: 10.1371/journal.pone.0166772.].

Decoupling shading and reflectance from complex scene-images is a long-standing problem in computer vision. We introduce a framework for decomposing an image into the product of an illumination component and a reflectance component. Due to the ill-posed nature of the problem, prior information on shading and reflectance is mandatory. The proposed method adopts the premise that pixels in a region with similar chromaticity values should have the same reflectance. This assumption was used to minimize the l2 norm of the local per-pixel reflectance gradients to extract the shading and reflectance components. To obtain smooth chromatic regions, texture was treated in a new style. Texture was removed in the first step of the algorithm and the smooth image was processed for intrinsic decomposition. In the final step, texture details were added to the intrinsic components based on the material of each pixel. In addition, user-assistance was used to further refine the results. The qualitative and quantitative evaluation on the MIT intrinsic dataset indicated that the quality of intrinsic image decomposition was improved in comparison with previous methods.

Social networks are specific types of social media which consolidate the ability of omnipresent connection for users and devices to share user-centric data objects among interested users. Taking advantage of the characteristics of both mobile social networks (MSNs) and online social networks (OSNs), MSNs are capable of providing an efficient and effective mobile environment for users to access, distribute, and share data. OSNs provide capability of search, data sharing, and online social interactions for users through Internet sites. However, the lack of a protective infrastructure in these networks has turned them into convenient targets for various risks. This is the main purpose why social networks including MSNs and OSNs carry disparate and intricate safety concerns specially privacy-preserving challenges and what has been done to improve these challenges. In addition, what types of data should be protected and what are the different architectures provided for each of these networks? In this paper, we aim to provide a clear categorization on privacy challenges and a deep exploration over some recent solutions in MSNs and OSNs. In particular, in MSNs, proposed scheme to protect data types is categorized, and in OSNs, all types of proposed architectures, along with the proposed mechanisms for privacy, are classified. To conclude, several major open research issues are discussed, and future research directions are outlined.