M. Abdullah-Al-Wadud

A skin detection approach based on the Dempster--Shafer theory of evidence

Skin detection is an important step for a wide range of research related to computer vision and image processing and several methods have already been proposed to solve this problem. However, most of these methods suffer from accuracy and reliability problems when they are applied to a variety of images obtained under different conditions. Performance degrades further when fewer training data are available. Besides these issues, some methods require long training times and a significant amount of parameter tuning. Furthermore, most state-of-the-art methods incorporate one or more thresholds, and it is difficult to determine accurate threshold settings to obtain desirable performance. These problems arise mostly because the available training data for skin detection are imprecise and incomplete, which leads to uncertainty in classification. This requires a robust fusion framework to combine available information sources with some degree of certainty. This paper addresses these issues by proposing a fusion-based method termed Dempster-Shafer-based Skin Detection (DSSD). This method uses six prominent skin detection criteria as sources of information (SoI), quantifies their reliabilities (confidences), and then combines their confidences based on the Dempster-Shafer Theory (DST) of evidence. We use the DST as it offers a powerful and flexible framework for representing and handling uncertainties in available information and thus helps to overcome the limitations of the current state-of-the-art methods. We have verified this method on a large dataset containing a variety of images, and achieved a 90.17% correct detection rate (CDR). We also demonstrate how DSSD can be used when very little training data are available, achieving a CDR as high as 87.47% while the best result achieved by a Bayesian classifier is only 68.81% on the same dataset. Finally, a generalized DSSD (GDSSD) is proposed achieving 91.12% CDR.

Region-of-Interest Selection for Skin Detection Based Applications

In this paper we propose a simple and reliable approach for skin region segmentation to generate region-of-interest (ROI) for various human-computer interaction based applications. Performance of this approach is much better than existing approaches in terms of segmenting solid skin regions without generating much noisy segments. Moreover, it does not need any prior training session and it can adapt to detect skin pixels from images of people from different races taken at different imaging conditions.

A skin detection approach based on color distance map

We propose a reliable approach to detect skin regions that can be used in various human-related image processing applications. We use a color distance map, which itself is a grayscale image making the process simple, but still containing color information. Based on this map, we generate some skin as well as nonskin seed pixels, and then grow them to capture the appropriate regions. This approach outperforms the existing approaches in terms of segmenting solid and perfect skin regions. It does not generate much noisy segments. Moreover, it does not need any prior training session and can adapt to detect skin regions from images taken at different imaging conditions.

Skin Segmentation Using Color Distance Map and Water-Flow Property

A new approach for skin region segmentation is proposed. It uses color distance map (CDM) and an algorithm based on the property of flow of water. The CDM itself is a grayscale image, which makes the algorithm very simple. However, it is still capable of providing color information based on which some skin and non-skin seed regions can be determined reliably. Then a water-flow based procedure determines skin and non-skin segments completely. The color distance map is robust against variations in imaging conditions and the water-flow procedure efficiently uses the region information to extract solid skin segments without generating much noisy segments.

Intelligent energy allocation strategy for PHEV charging station using gravitational search algorithm

Recent researches towards the use of green technologies to reduce pollution and increase penetration of renewable energy sources in the transportation sector are gaining popularity. The development of the smart grid environment focusing on PHEVs may also heal some of the prevailing grid problems by enabling the implementation of Vehicle-to-Grid (V2G) concept. Intelligent energy management is an important issue which has already drawn much attention to researchers. Most of these works require formulation of mathematical models which extensively use computational intelligence-based optimization techniques to solve many technical problems. Higher penetration of PHEVs require adequate charging infrastructure as well as smart charging strategies. We used Gravitational Search Algorithm (GSA) to intelligently allocate energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time.

A Schedule-based Multi-channel MAC Protocol for Wireless Sensor Networks

Due to the half-duplex property of the sensor radio and the broadcast nature of wireless medium, limited bandwidth remains a pressing issue for wireless sensor networks (WSNs). The design of multi-channel MAC protocols has attracted the interest of many researchers as a cost effective solution to meet the higher bandwidth demand for the limited bandwidth in WSN. In this paper, we present a scheduled-based multi-channel MAC protocol to improve network performance. In our protocol, each receiving node selects (schedules) some timeslot(s), in which it may receive data from the intending sender(s). The timeslot selection is done in a conflict free manner, where a node avoids the slots that are already selected by others in its interference range. To minimize the conflicts during timeslot selection, we propose a unique solution by splitting the neighboring nodes into different groups, where nodes of a group may select the slots allocated to that group only. We demonstrate the effectiveness of our approach thorough simulations in terms of performance parameters such as aggregate throughput, packet delivery ratio, end-to-end delay, and energy consumption.

Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles

Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA) has been applied and compared with another member of swarm family, particle swarm optimization (PSO), considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.

A spatially controlled histogram equalization for image enhancement

Image enhancement mostly means handling with the contrast of the pixels. However, conventional histogram equalization (HE) methods works based on the global histogram information only. Hence they often make an overall enhancement of the whole image without taking any intensive care of the spatial relationship among the pixels. This often leads to a number of annoying artifacts. In this paper we propose to incorporate spatial information in histogram equalization process to apply the appropriate amount of contrast enhancement for a visually pleasing output. This method outperforms other present HE approaches by enhancing the contrast well without any severe side affects such as washed out appearance (caused by over-enhancing some of the features and under-enhancing some others), checkerboard effects etc.

A scalable and elastic cloud-assisted publish/subscribe model for IPTV video surveillance system

In this paper, we present a scalable and elastic content-based publish/subscribe model over cloud computing platform to support a smart, flexible and ubiquitous IPTV video surveillance system. Through this system, users of a surveillance system can subscribe to many surveillance events and receive video streams as a notification of new event occurring. This has direct impact on the way surveillance activities are carried out in different application domains including public safety and security, healthcare surveillance, etc. In the publish/subscribe model, it is challenging to match the events with the subscriptions efficiently that contains a large number of live contents. Existing algorithms on event matching are not very effective in the case of range predicates in subscriptions that are commonly used in IPTV video surveillance-based healthcare system and other areas. This paper addresses the aforementioned issue and propose an elastic and scalable algorithm for event matching in IPTV video surveillance over cloud platform. We also show the performance assessment of the proposed event matching algorithm in cloud-based IPTV video surveillance scenario and compare with various state-of-the-art approaches.

Optimisation of PHEV/EV charging infrastructures: a review

Plug-in hybrid electric vehicle (PHEV) or electric vehicle (EV) has the potential to facilitate the energy and environmental aspects of personal transportation, but face a hurdle of access to charging system. The charging infrastructure has its own complexities when it is compared with petrol stations because of the involvement of the different charging alternatives. As a result, the topic related to optimisation of PHEV/EV charging infrastructure has attracted the attention of researchers from different communities in the past few years. Recently introduced smart grid technology has brought new challenges and opportunities for the development of electric vehicle infrastructure facilities. This paper is a review of different computational approaches and techniques used for the optimisation of charging infrastructure for electric vehicles.