837 research outputs found
Optimal Coded Diffraction Patterns for Practical Phase Retrieval
Phase retrieval, a long-established challenge for recovering a complex-valued
signal from its Fourier intensity measurements, has attracted significant
interest because of its far-flung applications in optical imaging. To enhance
accuracy, researchers introduce extra constraints to the measuring procedure by
including a random aperture mask in the optical path that randomly modulates
the light projected on the target object and gives the coded diffraction
patterns (CDP). It is known that random masks are non-bandlimited and can lead
to considerable high-frequency components in the Fourier intensity
measurements. These high-frequency components can be beyond the Nyquist
frequency of the optical system and are thus ignored by the phase retrieval
optimization algorithms, resulting in degraded reconstruction performances.
Recently, our team developed a binary green noise masking scheme that can
significantly reduce the high-frequency components in the measurement. However,
the scheme cannot be extended to generate multiple-level aperture masks. This
paper proposes a two-stage optimization algorithm to generate multi-level
random masks named that can also significantly reduce
high-frequency components in the measurements but achieve higher accuracy than
the binary masking scheme. Extensive experiments on a practical optical
platform were conducted. The results demonstrate the superiority and
practicality of the proposed over the existing masking
schemes for CDP phase retrieval
Face recognition in low resolution video sequences using super resolution
Human activity is a major concern in a wide variety of applications, such as video surveillance, human computer interface and face image database management. Detecting and recognizing faces is a crucial step in these applications. Furthermore, major advancements and initiatives in security applications in the past years have propelled face recognition technology into the spotlight. The performance of existing face recognition systems declines significantly if the resolution of the face image falls below a certain level. This is especially critical in surveillance imagery where often, due to many reasons, only low-resolution video of faces is available. If these low-resolution images are passed to a face recognition system, the performance is usually unacceptable. Hence, resolution plays a key role in face recognition systems. In this thesis, we address this issue by using super-resolution techniques as a middle step, where multiple low resolution face image frames are used to obtain a high-resolution face image for improved recognition rates. Two different techniques based on frequency and spatial domains were utilized in super resolution image enhancement. In this thesis, we apply super resolution to both images and video utilizing these techniques and we employ principal component analysis for face matching, which is both computationally efficient and accurate. The result is a system hat can accurately recognize faces using multiple low resolution images/frames
Dynamic block encryption with self-authenticating key exchange
One of the greatest challenges facing cryptographers is the mechanism used
for key exchange. When secret data is transmitted, the chances are that there
may be an attacker who will try to intercept and decrypt the message. Having
done so, he/she might just gain advantage over the information obtained, or
attempt to tamper with the message, and thus, misguiding the recipient.
Both cases are equally fatal and may cause great harm as a consequence.
In cryptography, there are two commonly used methods of exchanging secret
keys between parties. In the first method, symmetric cryptography, the key is
sent in advance, over some secure channel, which only the intended recipient
can read. The second method of key sharing is by using a public key exchange
method, where each party has a private and public key, a public key is shared
and a private key is kept locally. In both cases, keys are exchanged between
two parties.
In this thesis, we propose a method whereby the risk of exchanging keys
is minimised. The key is embedded in the encrypted text using a process
that we call `chirp coding', and recovered by the recipient using a process
that is based on correlation. The `chirp coding parameters' are exchanged
between users by employing a USB flash memory retained by each user. If the
keys are compromised they are still not usable because an attacker can only
have access to part of the key. Alternatively, the software can be configured
to operate in a one time parameter mode, in this mode, the parameters
are agreed upon in advance. There is no parameter exchange during file
transmission, except, of course, the key embedded in ciphertext.
The thesis also introduces a method of encryption which utilises dynamic blocks, where the block size is different for each block. Prime numbers are
used to drive two random number generators: a Linear Congruential Generator
(LCG) which takes in the seed and initialises the system and a Blum-Blum
Shum (BBS) generator which is used to generate random streams to encrypt
messages, images or video clips for example. In each case, the key created is
text dependent and therefore will change as each message is sent.
The scheme presented in this research is composed of five basic modules. The
first module is the key generation module, where the key to be generated is
message dependent. The second module, encryption module, performs data
encryption. The third module, key exchange module, embeds the key into
the encrypted text. Once this is done, the message is transmitted and the
recipient uses the key extraction module to retrieve the key and finally the
decryption module is executed to decrypt the message and authenticate it.
In addition, the message may be compressed before encryption and decompressed
by the recipient after decryption using standard compression tools
Machine Learning in Sensors and Imaging
Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens
Recent Application in Biometrics
In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers
- …