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Considering the simplicity and fast training speed of Haar-like features, the high detecting precision of HOG features, a combined method is proposed on the basis of the two features. Several rectangular features which can describe local human characteristics based on original features are added. The combined method can retain the precision of HOG features and increase the speed of detection at the...
Land cover in urban areas in China is changing rapidly during the past years as a result of urbanization. Changes detected from multi-temporal remote sensing images may help significantly in understanding urban development and supporting urban planning. Indeed, differences in reflectance spectra, easily obtained by satellite sensors, are important indicators for characterizing these changes. Although...
Accurate monitoring of urban areas using remote sensing data requires reliable change detection techniques. Nevertheless, while most of the changes are optically visible and easily detectable by an expert user, automatic processes are quite difficult to develop. That is why, the interpretation of changes has remained up-to-now visual in most operational applications in remote sensing. This paper provides...
We introduce a facial expression recognition method, which incorporates a weight to the Local Binary Pattern (LBP), and generates solid expression features. Furthermore, we use Adaboost to select a small set of prominent features, which is used by the Support Vector Machine (SVM) to classify facial expressions efficiently. Experimental results demonstrate that our method outperforms the state-of-the-art...
The principle of Support Vector Machine based on spot is to choose an appropriate scale to split the image into a series of segmentation, according to certain strategy using spectral information. And this principle ensures the spectral features of the majority of patch pixel similar. This method gathers statistics of each pixel value in the spot and obtains the mean value of each band to replace the...
This paper presents an evaluation of different features for polarimetric SAR (PolSAR) image classification. Firstly, we select several of the polarimetric features to give a summary on them. Then we give an insight into their classification performance together with a texture feature using the support vector machine (SVM). Finally, we employ a feature combination and selection strategy that optimizes...
In this paper, we propose an approach for fast pedestrian detection in images. Inspired by the histogram of oriented gradient (HOG) features, a set of multi-scale orientation (MSO) features are proposed as the feature representation. The features are extracted on square image blocks of various sizes (called units), containing coarse and fine features in which coarse ones are the unit orientations...
The availability of high-resolution (HR) remote sensing multispectral imagery brings opportunities and challenges for land cover classification. The methodology of multiscale segmentation is wildly accepted for feature extraction and classification in HR image. However, the relationship among chosen scale parameters, selected features, and classification accuracy is less considered. A classification...
Automated cell phenotype image classification is an interesting bioinformatics problem. In this paper, an automated cell phase classification framework is investigated for zebra fish presomitic mesoderm (PSM) images. Low image resolution, gradual transitions between adjacent categories and irregularity of real cell images make this classification task tough but intriguing. The proposed framework first...
One of the major stakeholders of image fusion is being able to process the most complex images at the finest possible integration level and with the most reliable accuracy. The use of support vector machine (SVM) fusion for the classification of multisensors images representing a complex tropical ecosystem is investigated. First, SVM are trained individually on a set of complementary sources: multispectral,...
In this study, a novel spatial information based support vector machine for hyperspectral image classification, named spatial-contextual semi-supervised support vector machine (SC3SVM), is proposed. This approach modifies the SVM algorithm by using the spectral information and spatial-contextual information. The concept of SC3SVM is to utilize other information, obtain from the pixels of a neighborhood...
In the framework of remote-sensing image classification support vector machines (SVMs) have recently been receiving a very strong attention, thanks to their accurate results in many applications and good analytical properties. However, SVM classifiers are intrinsically noncontextual, which represents a severe limitation in image classification. In this paper, a novel method is proposed to integrate...
With the significantly improved data availability in remote sensing technology, mid-resolution images have become the primary data source for crop sown area measurement in large scale. However, it is still difficult to solve the problems of spectrum heterogeneity in one field and spectra similarity between fields. This paper developed mixed field decomposition method and tested the method in an urban...
This paper presents a case study addressing the comparison between different SAR polarimetric mode for tropical forest stratification: Full polarimetry (FP), Dual Polarimetry (DP) and Compact Polarimetry (CP). These 2 latter modes are simulated using FP data acquired by the L band PALSAR sensor over 2 study sites. Cayenne in French Guyana and the Fazenda São Nicolau in Brazil. The classification...
Hyperspectral sensors accurately sample the spectral signatures of different land covers, thus allowing an effective discrimination of cover classes or ground materials. However, addressing a supervised classification problem with hundreds of features involves critical small-sample size issues. Moreover, traditional hyperspectral-image classifiers are usually noncontextual. In this paper, a novel...
In this work, we focus on how to select the most highly informative samples for effectively training support vector machine (SVM) classifiers in remotely sensed hyperspectral data classification. This issue is investigated by comparing different unsupervised algorithms which account for the spectral purity of training samples in the process of selecting those samples for classification purposes. Sample...
A method is proposed for the classification of hyperspectral data with high spatial resolution by Support Vector Machine (SVM) with multiple kernels. The approach is an extension of previous sole-kernel classifiers by integrating spectral features with spatial or structural features for hyperspectral classification. Using Support Vector Machine (SVM) as the classifier, different multi-kernel SVM classifiers...
Based on the framework of support vector machines (SVM) using one against one (OAO) strategy, a new kernel method based on Bhattacharyya distance is proposed to raise the classification accuracy by combining the characteristics of hyperspectral data. The proposed method takes advantage of the non-uniform information distribution of hyperspectral data and makes the band with greater separability play...
We classify the visibility of TS (Transport Stream) packet losses for SDTV and HDTV MPEG-2 compressed video streams. TS packet losses can cause various temporal and spatial losses. The visual effect of a TS packet loss depends on many factors, in particular whether the loss causes a whole frame loss or partial frame loss. We develop models for predicting loss visibility for both SDTV and HDTV resolutions...
In this paper we conduct an experiment to study the effects of multiple block sizes in face images using the Discrete Cosine Transform (DCT) algorithm. Facial features are extracted from each block using the DCT algorithm. These features are then combined to form a feature vector for facial recognition. The goal of the paper is to discover if there is an underlying principle for determining the best...
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