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Convolutional neural network (CNN) has been widely used in computer vision tasks recently and achieved remarkable success. This paper presents a novel video-based recognition approach using CNN for Chinese Sign Language (CSL). The proposed method extracts upper body images directly from videos, and employs a pre-training convolutional network model to recognize the gesture in the image. The new method...
In modern society, with the economic conditions getting better, more and more vehicles are produced. So many vehicles bring about the rapid development of traffic transportation and the traffic accidents happen frequently. With the fast development of computer technology, more and more interests are focused on vision navigation technology in Intelligent Vehicle System. At the same time, lane detection...
With a view to dealing with three dimensional (hereafter referred to simply as 3D) model of indoor environment based on RGB-D data, this study proposes a method of simultaneous localization and mapping of the mobile sensor. By using the color data and depth data collated from the sensor to produce 3D point cloud data of each frame, and this study uses siftGPU to extract and match the feature points...
A matching method for optical and synthetic aperture radar (SAR) images, robust to speckle noise, is presented. Firstly, a coarse correction to eliminate rotation and scale change between images is performed. Secondly, features robust to speckle noise of SAR image are detected by improving the original phase congruency based method. Then, feature descriptors are constructed by combining the Gaussian-Gamma-Shaped...
A gender classification system uses human face from a given image to tell the gender of the given person. An effective gender classification approach is able to promote the improvement of many other applications, including image/video retrieval, security monitor, human-computer interaction, etc. In this paper, a method for gender classification task in frontal face images based on stacked-autoencoders...
In this paper, a face detector based on Gentle AdaBoost algorithm and nesting cascade structure is proposed. Nesting cascade structure is introduced to avoid that too many weak classifiers in a cascade classifier will slow down the face detection speed of this cascade classifier. Gentle AdaBoost algorithm is used to train node classifiers on a Haar-like feature set to improve the generalization ability...
Internet has become an excellent ecommerce platform for bringing together large numbers of buyers and sellers across wide geographic regions. Trust and reputation systems represent a. significant trend in decision support for Internet mediated service provision. However, most existing work assumes that all users have the same trust metrics, but in real life different users often have different preference...
The extraction of object features from massive unstructured point clouds with different local densities, especially in the presence of random noisy points, is not a trivial task even if that feature is a planar surface. Segmentation is the most important step in the feature extraction process. In practice, most segmentation approaches use geometrical information to segment the 3D point cloud. The...
Recently many applications require an automatic processing of massive unstructured 3D point clouds in order to extract planar surfaces of man-made objects. While segmentation is the essential step in feature extracting process, but bad-segmentation results (i.e. under and over-segmentation) are still standing as a big obstacle to extract planar surfaces with best fit reality. In this paper, we propose...
High resolution satellite images offer abundance information of the earth surface for remote sensing applications. For two decades, the detection and monitoring of change using high resolution satellite images has been a topic of interest in remote sensing. Recent investigations have shown that the pixel-based analysis of change detection for high resolution images has explicit limits. Using object-based...
High resolution satellite images offer abundance information of the earth surface for remote sensing applications. Using change detection technology to extract the target area changes from high resolution remote sensing images and rapidly update map database has become a focus research of remote sensing information processing. However the traditional methods of change detection are not suitable for...
Abstract In this paper, a novel approach based on QTFDs and kernel principle component analysis (KPCA) is proposed to extract features of radar emitter signals. Then, these discriminative and low dimensional features achieved were fed to a Support Vector Machines (SVMs) based on FCM (fuzzy c-means) clustering for multi-class pattern recognition. Experimental results show that the proposed methodology...
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