Mohammed Yakoob Siyal

An image detection technique based on morphological edge detection and background differencing for real-time traffic analysis

The real-time vehicle detection from a traffic scene is the major process in image processing based traffic data collection and analysis techniques. The most common algorithm used for real-time vehicle detection is based on background differencing and thresholding operations. The efficiency of this method of image detection is heavily dependent on the background updating and threshold selection techniques. In this paper, a new background updating and a dynamic threshold selection technique is presented. An alternative image detection technique used in image processing is based on edge detection techniques. However, an edge detector extracts the edges of the objects of a scene irrespective of whether it belongs to the background details or the objects. Therefore, to separate these two, extra information is required. We have developed a new image detection method based on background differencing and edge detection techniques, which separates the objects from their backgrounds and works well under various lighting and weather conditions. This image detection technique together with other techniques for calculating traffic parameters e.g. counting number of vehicles, works in real-time on an 80386-based microcomputer operating at a clock speed of 33 MHz.

A secure and robust hash-based scheme for image authentication

To authenticate an image using a hash function is a challenging task since several core issues like tamper detection, security and robustness needs to be addressed. In this paper, we propose a hash-based image authentication scheme that simultaneously attempts to address these core issues. Unlike most of the existing schemes that use secret key in the feature extraction stage, we use secret key to randomly modulate image pixels to create a transformed feature space. The key-dependent transformed feature space is then used to calculate the image hash. To reduce the size of the hash, a 4-bit quantization scheme is also proposed. The experimental results reported in this paper reveals that the proposed scheme offers good robustness against JPEG compression, low-pass and high-pass filtering. Besides being robust, the proposed hashing scheme can detect minute tampering with localization of the tampered area. These results along with the receiver operating curve (ROC) and security analysis presented in this work makes the proposed technique a candidate for practical digital image signature systems where the transmitted or stored image might undergo JPEG compression, low-pass or high-pass filtering.

An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI

The segmentation of magnetic resonance images (MRI) is a challenging problem that has received an enormous amount of attention lately. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms have produced better results compared to other methods. In this paper, we present a modified FCM algorithm for bias (also called intensity in-homogeneities) estimation and segmentation of MRI. Normally, the intensity in-homogeneities are attributed to imperfections in the radio-frequency coils or to the problems associated with the image acquisition. Our algorithm is formulated by modifying the objective function of the standard FCM and it has the advantage that it can be applied at an early stage in an automated data analysis before a tissue model is available. The proposed method can deal with the intensity in-homogeneities and Gaussian noise effectively. We have conducted extensive experimental and have compared our results with other reported methods. The results using simulated images and real MRI data show that our method provides better results compared to standard FCM-based algorithms and other modified FCM-based techniques.

Novel biometric digital signatures for Internet‐based applications

Personal identification numbers, passwords, smart cards and digital certificates are some of the means employed for user authentication in various electronic commerce applications. However, these means do not really identify a person, but only knowledge of some data or belonging of some determined object. This paper introduces the notion of biometric signature – a new approach to integrate biometrics with public key infrastructure, using biometric based digital signature generation which is secure, efficacious, fast, convenient, non‐invasive and correctly identifies the maker of a transaction. It also suggests two schemes for biometric signature using two existing and widely used digital signature algorithms, RSA and DSA, and discusses the problems associated with them individually. Speed of both schemes (based on iris recognition) is measured and compared with the help of JAVA implementation for both approaches.

A Window-based Edge Detection Technique for Measuring Road Traffic Parameters in Real-Time

The real-time measurement and analysis of various traffic parameters such as volume, speed and types of vehicles are increasingly required for traffic control and management. Image processing technique is now considered as an attractive and flexible method for automatic analysis and data collections in traffic engineering. Various algorithms based on background differencing techniques have been applied to vehicle detection. However, these techniques have not yielded good results due to inefficiency of background updating techniques. This paper describes a new method for vehicle detection based on edge detection techniques. This method eliminates the background updating and has been implemented in real-time on a low-cost 80386 based microcomputer system. This new vehicle detection algorithm works under various lighting and weather conditions and measures other traffic parameters in real-time as well.

A neural-vision based approach to measure traffic queue parameters in real-time

Abstract The real-time measurement of queue parameters is required in many traffic situations such as accident and congestion monitoring and adjusting the timings of the traffic lights. Previous methods proposed by researchers for queue detection are based on traditional image processing algorithms. The method proposed here is based on applying the combination of edge detection and neural network algorithms. The edge detection technique is used to detect vehicles and estimate the motion, while neural network is used to measure the queue parameters. The neural network is trained for various road traffic conditions and is able to provide better results than the traditional image processing algorithms.

A Noisy Channel Tolerant Image Encryption Scheme

In this paper, we present an image encryption scheme that has the capability to tolerate noisy effects of a wireless channel. This means if the encrypted image data is corrupted by channel noise up to a certain level, correct decryption is possible with some distortion. The proposed image encryption scheme relies on some very interesting properties of orthogonal matrices containing columns that form a set of orthonormal basis vectors. Besides being tolerant to noisy channels, the proposed scheme also provides good security against well-known cryptographic attacks as demonstrated in this paper by a number of experimental results and security analysis.

A Parallel Pipeline Based Multiprocessor System For Real-Time Measurement of Road Traffic Parameters

Real-time measurement and analysis of road traffic flow parameters such as volume, speed and queue are increasingly required for traffic control and management. Image processing is considered as an attractive and flexible technique for automatic analysis of road traffic scenes for the measurement and data collection of road traffic parameters. In this paper, the authors describe a novel image processing based approach for analysis of road traffic scenes. Combined background differencing and edge detection techniques are used to detect vehicles and measure various traffic parameters such as vehicle count and the queue length. A RISC based multiprocessor system was designed to enable real-time execution of the authors algorithm. The multiprocessor system has nine processing modules connected in a parallel pipeline fashion. Results shows that the authors multiprocessor system is able to provide measurement of traffic parameters in real-time. Results are presented for real tests of our system by analysing traffic scenes on the highways of Singapore.

Image Processing Techniques For Real-Time Qualitative Road Traffic Data Analysis

Real-time qualitative road traffic data analysis is the cornerstone for any modern transport system. So far, most of the analysis is done manually and the use of image processing techniques for qualitative analysis is at its early stage. In this paper we describe novel image processing algorithms together with the results, which assign a qualitative description to a traffic scene. The qualitative description of a traffic scene can be used for controlling traffic lights and putting hazard signals on the road side, thereby warning drivers to slow down or direct them to alternative routes. We analyse a wider view of the path and evaluate the whole description of traffic status. To approach this, we considered two major parameters of traffic status: the percentage of road occupied by vehicles and the percentage of moving and stationary parts of this occupancy. The alogrithm developed for traffic analysis automatically divides the scene into a number of blocks, based on camera parameters and the number of lanes. This full frame image processing application requires a low-cost frame grabber and a Pentium-based computer system for on-line real-time operations.

SOCIO-ECONOMIC FACTORS AND THEIR INFLUENCE ON THE ADOPTION OF E-COMMERCE BY COMSUMERS IN SINGAPORE

For the past few years, a steady growth has been observed in the Internet-based commerce activities in Singapore and Asia-Pacific region. However, there has been very limited study that empirically investigates the socio-economic factors and their influence on the adoption of electronic commerce (e-commerce) by consumers in Singapore. This study seeks to fill some of the gaps in this area. Viewing such a medium as a form of new innovation, the five socio-economic characters namely gender, age, income level, education level and the exposure to the Internet were hypothesized to see whether there was any relationship between these five factors and the consumers willingness to adopt e-commerce. The results indicate that income level; education level and exposure to the Internet were significant predictors in explaining the rate of adoption of e-commerce. However, contrary to general beliefs, this study showed that the gender and the age of consumers are not important factors for the adoption of e-commerce in Singapore. This study may help the e-merchants and policy makers to better understand consumer behavior and attitude towards e-commerce so that suitable changes can be made to make e-commerce more attractive and popular in Singapore and the Asia-Pacific region.