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Several techniques have recently been proposed to extract the features of an image. Feature extraction is one of the most important steps in various image processing and computer vision applications such as image retrieval, image classification, matching, object recognition. Relevant feature (global or local) contains discriminating information and is able to distinguish one object from others. Global...
Feature extraction is one of the most important steps in computer vision tasks such as object recognition, image retrieval and image classification. It describes an image by a set of descriptors where the best one gives a high quality description and a low computation. In this study, the authors propose a novel descriptor called histogram of local and global features using speeded up robust feature...
Recently, Content Based Image Retrieval (CBIR) has received a great attention by researchers. It becomes one of the most interesting topic in computer vision and image processing. CBIR image can be represent by local or global features. The entire image is described in the case of global features by using a novel descriptor called Upper-Lower of Local Binary Pattern (UL-LBP) based on Local Binary...
Human's Region of Interest is considered as one of the most challenges problems in visual perception field. Due to the huge amount of information carried out, the research in the field of human region of interest tries to avoid the data overload by choosing salient areas from the total visual scene to be processed at first. In this context, salient regions detection becomes an important task to achieve...
In this paper, we propose a new approach for extracting invariant feature from interest region. The new descriptor is inspired from the original descriptor SIFT (Scale Invariant Feature Transform) which is widely used in image matching by extracting interest points (IPs). However, this descriptor performs badly when the background is complex or corrupted with noise. Then, we adopt the local binary...
In this paper we present a new method for interest points matching to realize human body tracking in video sequences. The developed algorithm combines direct and indirect similarity measures evaluated when applying luminosity variation and motion blur noises. This new approach considers different matching constraints such as: cross-matching, uniqueness constraint and interest point's appearances and...
In this paper, three image classification methods based on Interest Points (IP) will be presented to identify different types of noise attack (Affine, Zoom, Blur and Contrast) between two images using SIFT descriptor. The first one is an affine classifier deduced from several test on a standard images database. The second is a fuzzy classifier based on rules deduced from evolution curves of membership...
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