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Scene recognition poses great challenges due to large intra-class variations. We present a novel visual descriptor to build scene gist.It is an extended version of census transform histogram.The proposed gist model is more robust and has better generalizability.It is a holistic scene-centered representation that bypasses the segmentation and the processing of individual objects or regions.We experimentally...
Remote sensing classification is the core of converting satellite image to useful geographic information. Many methods have been proposed for improving classification accuracy, however, the results are always dissatisfied. The reason is that there is serious spectral overlay phenomenon between classes which decrease the classification accuracy. This paper introduced an evidential reasoning "soft"...
ADS40 images with High spatial resolution have more spatial characteristics as well as spectral characteristics than low-resolution data. In this paper,an object-oriented classification method based on multi-scale segmentation is introduced to classify ADS40 image of Taiyuan city. Firstly,a multi-scale segmentation algorithm is applied to get objects.Then,the features of objects,such as spectral,...
This paper presents a novel algorithm based on the gradient direction to separate the multiple attaching and overlapping objects. The idea is based on the fact that the gradient directions of north,east,south,and west neighbourhood pixels are divergent when the gradient direction of the pixel located in the boundary region is used as a reference line.The proposed algorithm is composed of three steps:image...
This Paper proposes a new face recognition method based on local Gabor phase characteristics. In our proposed method, according to the good spatial position and orientation of Gabor filter, a Gabor filter with four frequencies and six orientations is firstly applied to filter face images. Based on daugman''s method and the local XOR pattern, local Gabor phase patterns are then extracted to form the...
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...
Local stereo matching could deliver accurate disparity maps by the associated method, like adaptive support-weight, but suffers from the high computational complexity, O(NL), where N is pixel count in spatial domain, and L is search range in disparity domain. This paper proposes a fast algorithm that groups similar pixels into super-pixels for spatial reduction, and predicts their search range by...
One of the most challenging problems of field robots is self-localization, which involves incremental update of position while in motion. Though wheel based odometry is cheaper to implement its accuracy degrades when wheels slip. In this paper performance of low-cost visual odometry approach is experimented as a feasibility test for field robot localization. We have used a downward-facing camera and...
Device identification is an emerging field where technologies used to create a digital image are inferred by strategic image analysis. Some of the more well understood topics in this area include techniques to identify cameras, scanners and printers. The goal of printing-imaging cycle device identification is to gather information about the printing and imaging devices used to create, then digitally...
Detection of outliers and relevant features are the most important process before classification. In this paper, a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted from the digital mammograms, and k-means clustering is applied to cluster the features, the number of clusters is equal with the number of...
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...
We present a new disparity refinement algorithm that utilize color information of the reference image to generate sharp disparity maps. While existing methods use iterative approaches or require multiple disparity maps, the proposed algorithm uses a single pass approach to reduce errors at depth discontinuities. The experimental results are evaluated on the Middlebury benchmark dataset; show the effectiveness...
In this paper an improved hill climbing algorithm based method is presented to cut character out of the license plate images. Although there are many existing commercial LPR systems, with poor illumination conditions and moving vehicle the accuracy impaired. After examination and comparison of two different types of image segmentation approaches, the hill climbing algorithm based method gave a better...
Despite the significant number of stereo vision algorithms proposed in literature in the last decade, most proposals are notably computationally demanding and/or memory hungry so that it is unfeasible to employ them in application scenarios requiring real-time or near real-time processing on platforms with limited resources such as embedded devices. In this paper, we have selected the subset of proposals...
A good benchmark suite should provide users with inputs that have multiple levels of fidelity for different use cases such as running on real machines, register level simulations, or gate-level simulations. Although input reduction has been explored in the past, there is a lack of understanding how to systematically scale input sets for a benchmark suite. This paper presents a framework that takes...
The aim of this study is to extract homogenous and edge regions from a post-earthquake Quickbird satellite image with high resolution and to combine this spatial information with spectral information in classification of earthquake damage. In order to extract the homogenous and edge regions from the image, a spatial filtering approach and Canny filter were used. A novel method called support vector...
This paper explores the use of Two-Dimensional Robust Neighborhood Discriminant Embedding (2D-RNDE) as a means to improve the performance and robustness of face recognition. 2D-RNDE is based on graph embedding framework and Fisher's criterion, it can utilize the original two-dimensional image data directly and takes into account the Individual Discriminative Factor (IDF) which is proposed to describe...
Image segmentation with the traditional Fuzzy C-means (FCM) algorithm only uses each pixel's gray value, when the image is corrupted by noises, the accuracy of segmentation will be greatly reduced. So, this paper proposed an image segmentation method which based on rough sets theory and fuzzy c-mean clustering. The test result shows that the method has a good segmentation performance.
We present an extension to the well-known local binary pattern (LBP) feature descriptor. The newly defined descriptor known as extended local ternary pattern (ELTP) exhibits better noise resistivity than the original LBP, while maintaining computational simplicity. We further investigate the presence of uniform patterns in ELTP. With a slight modification in the definition of uniformity, it is found...
In panoramic videos, the object movement between adjacent side images leads to deformation and discontinuity, which makes the traditional video tracking approaches insufficient. An effective static object tracking algorithm is proposed in this paper to resolve the tracking problems from the deformation and discontinuity in cubic panorama. The algorithm extends the relevant side images with boundary...
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