Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensities. In the third stage, the boundary of the target object is extracted, and in the fourth and fifth stages, respectively, the region of interest (ROI) is highlighted and reconstructed. Our model was tested and evaluated using realistic scenarios which include outdoor and indoor scenes. The results reflect the ability of our approach to detect and remove shadows and reconstruct a shadow free image with a small error of approximately 6%.
The increase in cloud computing services and the large-scale construction of data centers led to excessive power consumption. Datacenters contain a large number of servers where the major power consumption takes place. An efficient virtual machine placement algorithm is substantial to attain energy consumption minimization and improve resource utilization through reducing the number of operating servers. In this paper, an enhanced discrete particle swarm optimization (EDPSO) is proposed. The enhancement of the discrete PSO algorithm is achieved through modifying the velocity update equation to bound the resultant particles and ensuring feasibility. Furthermore, EDPSO is assisted by two heuristic algorithms random first fit (RFF) a
... Show MoreWith the high usage of computers and networks in the current time, the amount of security threats is increased. The study of intrusion detection systems (IDS) has received much attention throughout the computer science field. The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection. This paper presents an overview of different intrusion detection systems and a detailed analysis of multiple techniques for these systems, including their advantages and disadvantages. These techniques include artificial neural networks, bio-inspired computing, evolutionary techniques, machine learning, and pattern recognition.
Most Internet-tomography problems such as shared congestion detection depend on network measurements. Usually, such measurements are carried out in multiple locations inside the network and relied on local clocks. These clocks usually skewed with time making these measurements unsynchronized and thereby degrading the performance of most techniques. Recently, shared congestion detection has become an important issue in many computer networked applications such as multimedia streaming and
peer-to-peer file sharing. One of the most powerful techniques that employed in literature is based on Discrete Wavelet Transform (DWT) with cross-correlation operation to determine the state of the congestion. Wavelet transform is used as a de-noisin
Humans use deception daily since it can significantly affect their life and provide a getaway solution for any undesired situation. Deception is either related to low-stakes (e.g. innocuous) or high-stakes (e.g. with harmful situations). Deception investigation importance has increased, and it became a critical issue over the years with the increase of security levels around the globe. Technology has made remarkable achievements in many human life fields, including deception detection. Automated deception detection systems (DDSs) are widely used in different fields, especially for security purposes. The DDS is comprised of multiple stages, each of which should be built/trained to perform intelligently so that the whole system can give th
... Show MoreThere are growing concerns over the possibility of transfer genetically modified
sequences from genetically modified feed component (GM feed) to animals and
their products, moreover, affect these sequences on animal and human health. This
study was implemented to detect P35S in modified feed by using PCR technique by
detecting presence P35S promoter, which responsible for the regulation of gene
expression for most of the transgenic genes. Thirty eight feed samples were
collected from different sources of Baghdad markets, which have been used for
feeding livestock, comprise 21 coarse mixes feed, 13 pelleted feed, and 4 expanded
feed. Genomic DNA was extracted by using two methods, CTAB method and
Wizard kit. In
There are growing concerns over the possibility of transfer genetically modified sequences from genetically modified feed component (GM feed) to animals and their products, moreover, affect these sequences on animal and human health. This study was implemented to detect P35S in modified feed by using PCR technique by detecting presence P35S promoter, which responsible for the regulation of gene expression for most of the transgenic genes. Thirty eight feed samples were collected from different sources of Baghdad markets, which have been used for feeding livestock, comprise 21 coarse mixes feed, 13 pelleted feed, and 4 expanded feed. Genomic DNA was extracted by using two methods, CTAB method and Wizard kit. In order to verify the presenc
... Show Morein this paper we adopted ways for detecting edges locally classical prewitt operators and modification it are adopted to perform the edge detection and comparing then with sobel opreators the study shows that using a prewitt opreators
In this paper, we devoted to use circular shape sliding block, in image edge determination. The circular blocks have symmetrical properties in all directions for the mask points around the central mask point. Therefore, the introduced method is efficient to be use in detecting image edges, in all directions curved edges, and lines. The results exhibit a very good performance in detecting image edges, comparing with other edge detectors results.
Skin detection is classification the pixels of the image into two types of pixels skin and non-skin. Whereas, skin color affected by many issues like various races of people, various ages of people gender type. Some previous researchers attempted to solve these issues by applying a threshold that depends on certain ranges of skin colors. Despite, it is fast and simple implementation, it does not give a high detection for distinguishing all colors of the skin of people. In this paper suggests improved ID3 (Iterative Dichotomiser) to enhance the performance of skin detection. Three color spaces have been used a dataset of RGB obtained from machine learning repository, the University of California Irvine (UCI), RGB color space, HSV color sp
... Show MoreAbstract
The aim of this research is to formulate a proposed strategy for developing graduate studies at King Khalid University to support the achievement of the Kingdom's vision (2030). The research used the descriptive survey approach. The research community consisted of all graduate students at King Khalid University. The research was conducted on a random sample included (623) Male and female students, this research relied on the questionnaire as the main tool for collecting its data. The results revealed that the research sample believes that all the proposals included in this research are very important for the development of postgraduate studies at King Khalid University to support the achievement of the K
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