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In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude...
Plant recognition from their leaves has become a popular area in the machine learning and image processing. In this study 7 different types of apricot trees were determined and classified by using their leaves. At first leaves images were pre-processed. After than each image was scanned by 5×5 overlapping filter and median values of each filter process were recorded to represent the leaves. After...
Production of high quality wheat has a great importance especially in the solution of nutrition problems. It is necessary to make decomposition for specifying the quality. Here, high quality and unclassified wheat recognition are realized. The most distinctive feature between high quality and poor quality wheat is the shape difference. In this study, Bag of Contour Fragments (BCF) was used as a shape...
The use of depth sensors in activity recognition is a technology that emerges in human computer interaction and motion recognition. In this study, an approach to identify single-person activities using deep learning on depth image sequences is presented. First, a 3D volumetric template is generated using skeletal information obtained from a depth video. The generated 3D volume is used for extracting...
This paper develops an advanced method to classify load-pull contours for the design of a broadband high-efficiency power amplifiers (PA) using the technique of a support vector machine (SVM). The classifier models for load-pull contours are verified through their accuracy in test and design validation. Comparisons reveal that the proposed method significantly outperforms commercial electronic design...
In this work, we develop a video surveillance system to detect the disappearance of the objects selected by an operator. Proposed method is optimized to minimize the effects of shadows, partial occlusions and changes in illumination to minimize false alarms. The system is trained by extracting local features from the object to produce an alarm in the case of the disappearance.
In this study, gender prediction is investigated for the face images. To extract the features of the images, Local Binary Pattern (LBP) is used with its different parameters. To classify the images male or female, K-Nearest Neighbors (KNN) and Discriminant Analysis (DA) methods are used. Their performances according to the LBP parameters are compared. Also classification methods' parameters are changed...
We propose a systematic frame work for the automatic detection of multiple human actions within the same frame in realistic and diverse video settings. One of the major challenges is the process of recognizing and understanding of human actions from videos with large variations resulting from camera motions, changes in human appearance, pose changes, scale changes and back ground clutter etc. In this...
The proximal classifier with consistency (PCC) isan improvement of generalized eigenvalue proximal support vector machine (GEPSVM), ensuring consistency ignored inGEPSVM. However, similar to many other machine learning methods, PCC uses only the global information and the eigenvalue problem need to be solved, which can not classify small sample size (SSS) problem effectively. By exploiting both global...
We present a reduced dimensionality, information rich (RDIR) visual representation for scene information that distills the most distinguishing elements in an image, enabling scene classification by humans and computers under reduced dimensionality conditions. The representation utilizes the Gist model [1] to convey scene information in low bandwidth conditions, exhibiting enhanced classification performance...
Processing surges are fast and unexpected changes in the processing demand that commonly occur in cloud computing. The cloud elasticity enables to handle processing surges, increasing and decreasing resources as required. However, a surge can be very fast, so that the overhead to provide more resource is greater than the processing benefit. On the other hand, if the surge is slow and continuous, and...
Abnormal activity detection plays an important role in many areas such as surveillance, military installations, and sports. Existing abnormal activity detectors mostly rely on motion data obtained over a number of frames to characterize abnormality. However, only motion may not be able to capture all forms of abnormality, in particular, poses that do not amount to motion "outliers". In this...
Word level Script and language identification is a process of separating the script and language of each word present in a printed or handwritten multi-script document. It is an essential part of a multi-lingual Optical Character Recognizer (OCR). Most of the OCRs are solely designed for a single script. So it can't convert a document which is written in more than one script. This paper explained...
Pedestrian detection is an important area in computer vision with key applications in intelligent vehicle and surveillance systems. One of the main challenges in pedestrian detection is occlusion. In this paper, we propose a novel pedestrian detection approach capable of handling partial occlusion. Three stage cascaded classifier is used in the proposed approach. Global classifier based on HOG features...
This paper proposes a Network Shaped Cascade Classifier(NSCC) based on potential functions for pedestrian detection. Potential function is exploited to capture the nonlinear information in the training set based on the multiple sample centers. A flexible structure in NSCC is used to combine the base classifier and potential function into a nonlinear cascade classifier, and NSCC can well inherit the...
Visual surveillance is widely used in monitoring, entertainment and public security in recent years. This arouses the growing demand of automatic analysis system to deal with large amount of data produced by video cameras. Human action recognition is one of the most popular topics in video analysis. However, human activities are extremely complex and the dimensions of features extracted from a video...
Classifier fusion methods are usually used to combine multiple classification decisions and generate better classification results than any single classifier. In order to improve object classification accuracy, it is a common method to assign weights to classifiers based on their importance in a multiple decision system. In this paper we put forward a method to weight different classifiers in classifier...
Indexing requirement for efficient accessing to visual data has been increased with the widespread use of multimedia applications. Satisfaction of this requirement mostly depends on the automatic extraction of objects in the visual data. In this study, component-based object extraction method is compared with object extraction in its entirety. Applied method, implemented system and conducted tests...
Usage of 3th dimension information obtained from depth sensors in human action recognition has gained importance in the recent years. In this study, basic human actions are tried to recognize on a human model derived from RGBD sensor. Joint angles and joint displacements used as time series and feature extraction from times series is applied to recognize actions. Actions are classified with the random...
Recognition of video scenes is a challenging problem due to the unconstrained structure of the video content. Here, we propose a spatial pyramid based method for the recognition of video scenes and explore the effect of parameter optimization to the recognition accuracy. In the experiments different sampling methods, dictionary sizes, kernel methods, and pyramid levels are examined. Support Vector...
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