Human detection represents a main problem of interest when using video based monitoring. In this paper, artificial neural networks, namely multilayer perceptron (MLP) and radial basis function (RBF) are used to detect humans among different objects in a sequence of frames (images) using classification approach. The classification used is based on the shape of the object instead of depending on the contents of the frame. Initially, background subtraction is depended to extract objects of interest from the frame, then statistical and geometric information are obtained from vertical and horizontal projections of the objects that are detected to stand for the shape of the object. Next to this step, two types of neural networks are used to classify the extracted objects. Tests have been performed on a sequence of frames, and the simulation results by MATLAB showed that the RBF neural network gave a better performance compared with the MLP neural network where the RBF model gave a mean squared error (MSE) equals to 2.36811e-18 against MSE equals to 2.6937e-11 achieved by the MLP model. The more important thing observed is that the RBF approach required less time to classify the detected object as human compared to the MLP, where the RBF took approximately 86.2% lesser time to give the decision.
This paper presents a combination of enhancement techniques for fingerprint images affected by different type of noise. These techniques were applied to improve image quality and come up with an acceptable image contrast. The proposed method included five different enhancement techniques: Normalization, Histogram Equalization, Binarization, Skeletonization and Fusion. The Normalization process standardized the pixel intensity which facilitated the processing of subsequent image enhancement stages. Subsequently, the Histogram Equalization technique increased the contrast of the images. Furthermore, the Binarization and Skeletonization techniques were implemented to differentiate between the ridge and valley structures and to obtain one
... Show MoreThe Flanagan Aptitude Classification Tests (FACT) assesses aptitudes that are important for successful performance of particular job-related tasks. An individual's aptitude can then be matched to the job tasks. The FACT helps to determine the tasks in which a person has proficiency. Each test measures a specific skill that is important for particular occupations. The FACT battery is designed to provide measures of an individual's aptitude for each of 16 job elements.
The FACT consists of 16 tests used to measure aptitudes that are important for the successful performance of many occupational tasks. The tests provide a broad basis for predicting success in various occupational fields. All are paper and pen
... Show MorePurpose: to review in detail various aspects of odontogenic keratocyst, emphasizing recent nomenclature, clinical, histopathological, recurrence, and management of odontogenic keratocyst.
Methods: To achieve the objective of this review, a manual search was done in hard copy books of oral and maxillofacial pathology, and an electronic search was done in the google website, oral and maxillofacial pathology E-books, virtual database sites, such as PubMed, Research Gate, Academia, and Google scholar using the descriptors: odontogenic cyst, kerato odontogenic tumor, odontogenic keratocyst, and jaws cystic lesion. The eligibility criteria for selecting articles were: to be in the English language, stu
... Show MorePhishing is an internet crime achieved by imitating a legitimate website of a host in order to steal confidential information. Many researchers have developed phishing classification models that are limited in real-time and computational efficiency. This paper presents an ensemble learning model composed of DTree and NBayes, by STACKING method, with DTree as base learner. The aim is to combine the advantages of simplicity and effectiveness of DTree with the lower complexity time of NBayes. The models were integrated and appraised independently for data training and the probabilities of each class were averaged by their accuracy on the trained data through testing process. The present results of the empirical study on phishing websi
... Show MoreThe reaction of 2-amino-benzothiazole with bis [O,O-2,3,O,O – 5,6 – (chloro(carboxylic) methiylidene) ] – L – ascorbic acid (L-AsCl2) gave new product 3-(Benzo[d]Thaizole-2-Yl) – 9-Oxo-6,7,7a,9-Tertrahydro-2H-2,10:4,7-Diepoxyfuro [3,2-f][1,5,3] Dioxazonine – 2,4 (3H) – Dicarboxylic Acid, Hydro-chloride (L-as-am)), which has been insulated and identified by (C, H, N) elemental microanalysis (Ft-IR),(U.v–vis), mass spectroscopy and H-NMR techniques. The (L-as am) ligand complexes were obtained by the reaction of (L-as-am) with [M(II) = Co,Ni,Cu, and Zn] metal ions. The synthesized complexes are characterized by Uv–Visible (Ft –IR), mass spectroscopy molar ratio, molar conductivity, and Magnetic susceptibility techniques. (
... Show MoreThe current work concerns preparing cobalt manganese ferrite (Co0.2Mn0.8Fe2O4) and decorating it with polyaniline (PAni) for supercapacitor applications. The X-ray diffraction findings (XRD) manifested a broad peak of PAni and a cubic structure of cobalt manganese ferrite with crystal sizes between 21 nm. The pictures were taken with a field emission scanning electron microscope (FE-SEM), which evidenced that the PAni has nanofibers (NFs) structures, grain size 33 – 55 nm, according to the method of preparation, where the hydrothermal method was used. The magnetic measurements (VSM) that were conducted at room temperature showed that the samples had definite magnetic properties. Additionally, it was noted that the saturation magnetizatio
... Show MoreThe extracting of personal sprite from the whole image faced many problems in separating the sprite edge from the unneeded parts, some image software try to automate this process, but usually they couldn't find the edge or have false result. In this paper, the authors have made an enhancement on the use of Canny edge detection to locate the sprite from the whole image by adding some enhancement steps by using MATLAB. Moreover, remove all the non-relevant information from the image by selecting only the sprite and place it in a transparent background. The results of comparing the Canny edge detection with the proposed method shows improvement in the edge detection.
This paper presents an approach to license plate localization and recognition. A proposed method is designed to control the opening of door gate based on the recognition of the license plates number in Iraq. In general the system consists of four stages; Image capturing, License plate cropping, character segmentation and character recognition. In the first stage, the vehicle photo is taken from standard camera placed on the door gate with a specific distance from the front of vehicle to be processed by our system. Then, the detection method searches for the matching of the license plate in the image with a standard plate. The segmentation stage is performed by is using edge detection. Then character recognition, done by comparing with templ
... Show MoreDigital images are open to several manipulations and dropped cost of compact cameras and mobile phones due to the robust image editing tools. Image credibility is therefore become doubtful, particularly where photos have power, for instance, news reports and insurance claims in a criminal court. Images forensic methods therefore measure the integrity of image by apply different highly technical methods established in literatures. The present work deals with copy move forgery images of Media Integration and Communication Center Forgery (MICC-F2000) dataset for detecting and revealing the areas that have been tampered portion in the image, the image is sectioned into non overlapping blocks using Simple
... Show MoreA new approach presented in this study to determine the optimal edge detection threshold value. This approach is base on extracting small homogenous blocks from unequal mean targets. Then, from these blocks we generate small image with known edges (edges represent the lines between the contacted blocks). So, these simulated edges can be assumed as true edges .The true simulated edges, compared with the detected edges in the small generated image is done by using different thresholding values. The comparison based on computing mean square errors between the simulated edge image and the produced edge image from edge detector methods. The mean square error computed for the total edge image (Er), for edge regio
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