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The development of moving object detection systems has become of great interest in the video surveillance field. Although many foreground detection methods have been proposed, the problems of incomplete object shapes and misclassified shadow regions remain. We propose a new approach by combining bit-planes representation with gray-level co-occurrence matrix (GLCM). This allows the system to exploit...
In this paper, we combine Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) in order to detect the cranium and its components; namely, the brain, eyes and mouth. Furthermore, Deformable Part Model (DPM) algorithm is paired with the AdaBoost for training and classification. We use a CT/PET database acquired from the National Biomedical Imaging Archive (NBIA) in order to train and...
Moving object extraction is the core of event detection in video surveillance. Although many related methods have been proposed to extract moving objects, even advanced applications still encounter cavity problems, which are false detection and deficiencies resulting from cavities inside the body or fragmented foreground objects. In this paper, an entirely new structure for extracting moving objects...
In this paper, we present an automatic system of mine like object detection and recognition for sonar videos. This system is implemented with two main methods. One is the object detection and segmentation with intrackability, another is object recognition of mine like based on improved BOW algorithm and Support Vector Machine (SVM). Intrackability is defined by the concept of entropy, and can reflect...
In video surveillance, moving object detection has become one of the core techniques to understand video content. Many detection methods exist to detect objects, but cannot effectively compensate for the effect of shadows or incomplete shapes of moving objects. In this paper, we apply image bit-planes and hysteresis thresholding to compensate for the loss of spatial and temporal information. The experimental...
This paper presents an algorithm for obstacle classification and lane line identification using the laser range finder (LRF) sensors, which is used to warn the driver to watch the situation of environment when the obstacle appear in the front. The classification of detecting objects is essential to reduce the danger in traffic. Nevertheless, there may be a noise (or road surface) in far distance....
Background construction is the base of object detection and tracking for the machine vision system. Traditional background modeling methods often require complicated computations and are sensitive to illumination changes and shadow interference. In this paper, we propose a block-based background modeling method, which fully utilizes the color and texture characteristics of each incoming frame. The...
Facial paralysis can be on one or both sides of face and one side is much more common. This disease can impose significant psychological and functional impairment to patients. Traditionally, patients with facial paralysis are evaluated and examined by physicians based on manually measurement of certain difference between the two facial sides. In this paper, we describe a new approach for quantitatively...
In this paper, a comprehensive solution for managing abandoned objects is proposed, which means that the system can deal with the objects that are abandoned and removed. The system contains two adaptive abandoned object detection (AOD) methods that are both based on the Gaussian mixture model for real environments. The first method is more efficient than the second one, but the latter is more robust...
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