IEEE 2022-2023 : Image Processing Projects

Abstract : Reversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the encrypted domain. An original image is encrypted with a secret key and during or after its transmission, it is possible to embed additional information in the encrypted image, without knowing the encryption key or the original content of the image. During the decoding process, the secret message can be extracted and the original image can be reconstructed. In the last few years, RDHEI has started to draw research interest. Indeed, with the development of cloud computing, data privacy has become a real issue. However, none of the existing methods allows us to hide a large amount of information in a reversible manner. In this paper, we propose a new reversible method based on MSB (most significant bit) prediction with a very high capacity. We present two approaches, these are: high capacity reversible data hiding approach with correction of prediction errors (CPE-HCRDH) and high capacity reversible data hiding approach with embedded prediction errors (EPE-HCRDH). With this method, regardless of the approach used, our results are better than those obtained with current state of the art methods, both in terms of reconstructed image quality and embedding capacity                                                                                                                                                                                                                                                            

Abstract : The aim of this paper is to maximize the range of the access control of visual secret sharing (VSS) schemes encrypting multiple images. First, the formulation of access structures for a single secret is generalized to that for multiple secrets. This generalization is maximal in the sense that the generalized for-mulation makes no restrictions on access structures; in particular, it includes the existing ones as special cases. Next, a sufficient condition to be satisfied by the encryption of VSS schemes realizing an access structure for multiple secrets of the most general form is introduced, and two constructions of VSS schemes with encryption satisfying this condition are provided. Each of the two constructions has its advantage against the other; one is more general and can generate VSS schemes with strictly better contrast and pixel expansion than the other, while the other has a straightforward implementation. Moreover, for threshold access structures, the pixel expansions of VSS schemes generated by the latter construction are estimated and turn out to be the same as those of the existing schemes called the threshold multiple-secret visual cryptographic schemes. Finally, the optimality of the former construction is examined, giving that there exist access structures for which it generates no optimal VSS schemes.                                                                                                                                                                               

Abstract : One of the becoming popular bio metric modalities  is the palm print. This bio metric modality is rich with  information, such as minutiae, ridges, wrinkles, and creases.  This research team is interested to investigate the creases for  bio metric identification. The palm print images in this research  have been captured by using a commercially available consumer  scanner. For each palm print image, two square regions on the  palm print image are extracted for bio metric identification  purpose. One of the regions is from the hyperthyroid region,  while the another is from the inter digital region. Due to  misalignment of the hand, the process of extraction of these  regions is tedious and time-consuming. Therefore, in this paper,  a computer-aided method has been proposed to simplify the  extraction process. The user only needs to mark two points on  the palm print image. Based on these points, the palm print image  will be aligned, and those two regions are extracted  automatically.                                                                                                                                                                                     

Abstract: This paper presents fast categorization or  classification of images on an animal data set using different  classification algorithm in combination with manifold learning  algorithms. The paper will focus on comparing the effects of  different non-linear dimensional reduction algorithms on  speed and accuracy of different classification algorithms. It  examines how manifold learning algorithms can improve  classification speed by reducing the number of features in the  vector representation of images while keeping the classification  accuracy high.