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Today, the manipulation on the digital images has become a very easy and professional process using the capabilities of photo editing tools with the wide and rapid development in information technology and computer industry. Image forgery can be used to hide or manipulate important information of official documents, which can be considered as a digital crime. Many algorithms can be used to detect...
This paper focuses on detecting a pedestrian in an image. This real time application aims for high detection accuracy as well as faster computation. For higher accuracy and detection rate Histogram of Oriented Gradients (HOG) algorithm is used. Further, Linear Support Vector Machine (LSVM) classification is used for faster and reliable classification. Since the HOG algorithm is compute expensive several...
The paper presents an algorithm allowing for automatic detection of squat flaws in railway rails. These flaws can pose a threat to the safety of railway traffic. A Gabor filter bank along with SVM classifier were used in the detection process. The optimal number of features used to discriminate between squat and the area without squat as well as the parameters for classifier were selected with the...
This paper addresses the problem of on-line learning for object tracking. Although a variety of techniques have been proposed in literature, a recent benchmark reveals that none of them can work well in all scenarios due to numerous practical challenges, such as illumination variations, motion blur, etc. These challenges occur at different time frames making it hard to design a tracker. In this paper,...
In these days, music genre classification (MGC) is a quite popular research field. The main goal of the MGC studies is automatically detecting music genre (eg., rap, rock). In literature, features are generally extracted from the music's melodic content or lyrics for this task. In this study, we have performed lyrics based MGC on a Turkish dataset. We have just used lyrics as feature source and considered...
The traditional Brain-computer Interface (BCI) obtains parameters from the offline analysis and applies them to online experiments. However, due to non-stationary characte-ristic of electroencephalography (EEG), static classification of algorithms are hard to be used in practical BCI. In this paper, we propose a new algorithm that combines the adaptation of preferable new incoming data with the incremental...
Human detection plays a crucial role in a number of real world applications. Because of the popularity of smart car, Virtual Reality (VR) and other applications, strong demand of real-time detecting rises. The efficiency of a human detection algorithm becomes more crucial than ever before. In this work, a novel human detection framework combining the Histograms of Oriented Gradients (HOG) feature,...
Simple quality metrics such as PSNR are known to not correlate well with subjective quality when tested across a wide spectrum of video content or quality regime. Recently, efforts have been made in designing objective quality metrics trained on subjective data, demonstrating better correlation with video quality perceived by human. Clearly, the accuracy of such a metric heavily depends on the quality...
To improve the accuracy rate of radar signal recognition in increasingly sophisticated electronic countermeasure environment, a modified Semi-supervised SVM (S3VM) algorithm based on HSS (Heuristic Sampling Search) is put forward. Aiming at disadvantages of traditional semi-supervised support vector machines, the classifier constructed by modified S3VM is used for classifying the radar signal, then...
Medical image analysis is a pioneer research domain due to the challenges posed by different kinds of images and the complexities in attaining the accurate prediction of abnormalities presence. Brain MRI classification into normal and abnormal has received increasing attention because of the high level of difficulty in handling those huge numbers of images. Recently, many computational techniques...
Object detection is an important task in computer vision and machine intelligence systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an algorithm of fast and accurate object detection. By sampling particle windows (PWs) from a proposal distribution (PD), MPW avoids exhaustively scanning the image. Despite its success, it is unknown how to determine the number of stages and the...
In this paper, a novel feature extraction method based on Non-convex Robust Principal Component Analysis (NRPCA) and minimum noise fraction is proposed for hyperspectral image classification. The proposed method consists of following steps. First, the spectral dimension of the hyperspectral image is reduced with averaging-based image fusion. Then, each band of the hyperspectral image which dimension...
Nowadays, the consumer reviews for various products are playing a very important role not only for consumers but also for the firms. A large collection of consumer reviews is now available on the internet. These reviews are very helpful to get quality information about the products. The consumer reviews are used as a feedback by the firms in their product development strategies and consumer relationship...
Machine Learning has a wide array of applications in the healthcare domain and has been used extensively for analyzing data. Apnea of Prematurity is a breathing disorder commonly observed in preterm infants. This paper compares the usage of Support Vector Machines and Random Forests, which are supervised learning algorithms, to predict Apnea of Prematurity at the end of the first week of the child's...
Spatial image classification meant to the mechanism of extracting meaningful knowledge information classes from spatial images dataset. Many traditional pixel based image classification techniques such as Support Vector Machines (SVM), ANN, Fuzzy methods, Decision Trees (DT) etc. exist. The performance and accuracy of these image classification methods depends upon the network structure and number...
This work seeks to improve upon the accuracy of birdsong analysis based species recognition. We intend to accomplish this by creating a more effective bird syllable segmentation algorithms (MIRS), Support Vector machine based classifiers are used to train the features of IRS and MIRS. The experimental results show the effectiveness of the proposed algorithm.
Ventricular tachyarrhythmia, in particular Ventricular Fibrillation (VF), is the primary arrhythmic event to cause Sudden Cardiac Death (SCD). Thus, the quick and accurate detection of a VF event is crucial for capturing the life-threat cardiac arrhythmias in real time. However, almost all existing VF detection algorithms are challenged by low accuracy or/and high complexity. To address this challenge,...
Feature representation and matching are two challenging problems for person e-identification problem. Designing a suitable feature representation method, and the according high efficiency matching scheme is meaningful. In this paper, a new person re-identification method was put forward. First, an improved BOF method was proposed, it use SURF algorithm to extract the preliminary feature and generate...
Exercise is considered as an effective mean against overweight and obesity-related diseases. In this paper, a real-time activity recognition and counting approach is proposed to evaluate amount of exercise only using a wearable smart watch. First, accelerometer and gyroscope data are collected to extract efficient features. Then Support Vector Machine classifiers are trained to recognize nine common...
In this paper, we try to make an author identification of two ancient Arabic religious books dating from the 6th century: The holy Quran and the Hadith. The authorship identification process is achieved through four phases which are: documents collection, text preprocessing, features extraction and classification model building. Thus, two series of experiments are undergone and commented. The first...
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