Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Image similarity using dynamic time warping of fractal features

Hashing algorithms such as MD/SHA variants have been used for years by forensic investigators to look for known artefacts of interest such as malicious files. However, such hashing algorithms are not effective when their hashes change with the slightest alteration in the file. Fuzzy hashing overcame this limitation to a certain extent by providing a close enough measure for slight modifications. As such, image forensics is an essential part of any digital crime investigation, especially in cases involving child pornography. Unfortunately such hashing algorithms can be thwarted easily by operations as simple as saving the original file in a different image format. This paper introduces a novel technique for measuring image similarity using Dynamic Time Warping (DTW) of fractal features taken from the frequency domain. DTW has traditionally been used successfully for speech recognition. Our experiments have shown that it is also effective for measuring image similarity while tolerating minor modifications, which is currently not capable by state-of-the-art tools

Average Hashing for Perceptual Image Similarity in Mobile Phone Application

Common problem occurs in almost all mobile devices was duplicated data or files. Such as duplicated images that often happen by event like capturing perceptually similar photos by the user, or images that shared several times in messaging applications chat groups. This common problem can be solved by manually search and remove the duplicated images one by one by the users, but better solutions is by building automated application that search perceptually similar images then provide the result to the users. We study and implementing Average Hashing and Hamming distance for perceptual image similarity into application under mobile phone platform to realize the solution for the problem. The result was very promising in speed and accuracy for finding perceptually similar images under limited resources device like mobile phone

A Novel Approach for Enhancement of Blowfish Algorithm by using DES, DCT Methods for Providing, Strong Encryption and Decryption Capabilities

Data safety has evolved into a critical requirement and a duty in modern life. Most of our systems are designed in such a way that it can get hacked, putting our private information at danger. As a result, for numerous safety motives, we utilize various approaches to save as much as possible on this data, regardless of its varied formats, words, photographs, videos, and so on. The data storage capacity of mobile devices is restricted owing to insufficient data storage and processing. In order to develop a safe MCC environment, security concerns must be studied and analysed. This study compares the most widely used symmetric key encryption algorithms, including DES (Data Encryption Standard), Blowfish, TDES (Triple Data Encryption Standard), PRESENT, and KLEIN. The assessment of algorithms is based on attacks, key size, and block size, with the best outcomes in their field