Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers. The experiment’s result indicates that the J48 classifier with LDA achieves the highest performance with 96.0001% accuracy.
Demodex species are external parasites; they are transmitted via direct contact, and when present in elevated numbers it may induce several ocular diseases. However, the symptoms are very similar to other diseases; hence, its’ role is often neglected. Therefore, an accurate diagnosis is important in order to avoid mistreatment. In this study, infestation rates in both blepharitis and chalazia were compared to an asymptomatic group, with relation to gender, age, personal hygiene, time of year, and residency. All specimens were examined immediately after collection. The aim of this study was to investigate the relationship between Demodex mites and ocular diseases. These mites were predominantly found in patie
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Face recognition is one of the most applications interesting in computer vision and pattern recognition fields. This is for many reasons; the most important of them are the availability and easy access by sensors. Face recognition system can be a sub-system of many applications. In this paper, an efficient face recognition algorithm is proposed based on the accuracy of Gabor filter for feature extraction and computing the Eigen faces. In this work, efficient compressed feature vector approach is proposed. This compression for feature vector gives a good recognition rate reaches to 100% and reduced the complexity of computing Eigen faces. Faces94 data base was used to test method.
In this research, an adaptive Canny algorithm using fast Otsu multithresholding method is presented, in which fast Otsu multithresholding method is used to calculate the optimum maximum and minimum hysteresis values and used as automatic thresholding for the fourth stage of the Canny algorithm. The new adaptive Canny algorithm and the standard Canny algorithm (manual hysteresis value) was tested on standard image (Lena) and satellite image. The results approved the validity and accuracy of the new algorithm to find the images edges for personal and satellite images as pre-step for image segmentation.
Object detection in real time is considered as a challenging problem. However, it is very important in a wide range of applications, especially in field of multimedia. The players and ball are the most important objects in soccer game videos and detecting them is a challenging task because of many difficulties, such as shadow and illumination, ball size, ball occluded by players or merged with lines, and similar appearance of players. To overcome these problems, we present a new system to detect the players and ball in real-time by using background subtraction and Sobel detection. The results were more accurate and approximately two times faster than those using only background subtraction.
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The financial fraud considers part of large concept to management and financial corruption, the financial fraud is appeared especially after corporate, that is Emerge agency theory, that is because recognize relationship between the management company and stakeholder, that is through group from constriction in order to block the management to fraud practice, that on the basis was choose another party in order fraud this practice and give opinion on financial statement, that consider basis decision making from stakeholder to basis the report auditor about creditability this is statement that reflect real activity for the company.The Auditor in order to lead work him Full professionalism to must using group from control Techniques, that is
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The effectiveness of detecting and matching of image features using multiple views of a specified scene using dynamic scene analysis is considered to be a critical first step for many applications in computer vision image processing. The Scale invariant feature transform (SIFT) can be applied very successfully of typical images captured by a digital camera.
In this paper, firstly the SIFT and its variants are systematically analyzed. Then, the performances are evaluated in many situations: change in rotation, change in blurs, change in scale and change in illumination. The outcome results show that each algorithm has its advantages when compared with other algorithms
This paper include the problem of segmenting an image into regions represent (objects), segment this object by define boundary between two regions using a connected component labeling. Then develop an efficient segmentation algorithm based on this method, to apply the algorithm to image segmentation using different kinds of images, this algorithm consist four steps at the first step convert the image gray level the are applied on the image, these images then in the second step convert to binary image, edge detection using Canny edge detection in third Are applie the final step is images. Best segmentation rates are (90%) obtained when using the developed algorithm compared with (77%) which are obtained using (ccl) before enhancement.
There is various human biometrics used nowadays, one of the most important of these biometrics is the face. Many techniques have been suggested for face recognition, but they still face a variety of challenges for recognizing faces in images captured in the uncontrolled environment, and for real-life applications. Some of these challenges are pose variation, occlusion, facial expression, illumination, bad lighting, and image quality. New techniques are updating continuously. In this paper, the singular value decomposition is used to extract the features matrix for face recognition and classification. The input color image is converted into a grayscale image and then transformed into a local ternary pattern before splitting the image into
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Features is the description of the image contents which could be corner, blob or edge. Corners are one of the most important feature to describe image, therefore there are many algorithms to detect corners such as Harris, FAST, SUSAN, etc. Harris is a method for corner detection and it is an efficient and accurate feature detection method. Harris corner detection is rotation invariant but it isn’t scale invariant. This paper presents an efficient harris corner detector invariant to scale, this improvement done by using gaussian function with different scales. The experimental results illustrate that it is very useful to use Gaussian linear equation to deal with harris weakness.
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Electrocardiography (ECG or EKG) is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. The main idea is how to detect activity of the heart from skin that appears in video without using electrodes. This paper, proposes an algorithm that works on analyzing video frames to detect heartbeats from tiny changes that happen in a skin color luminance (brightness) and then using them to amplifying heartbeat and drawing ECG. The results show that the heartbeat was detected and amplified and ECG was drawing from any part of the human body in different situations and from different video.