In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. The joint sparse dictionary consists of positive and negative sub-dictionaries, which model foregroun...
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It is well known that the backgrounds or the targets always change in real scenes, which weakens the effectiveness of classical tracking algorithms because of frequent model mismatches. In this paper, an object tracki...
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Segmentation becomes a difficult task if the objects are not homogeneous and have overlapping characteristics. The Graph Cuts methods combined with Gaussian Mixture Model (GMM) for initialization label has been adopte...
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Segmentation becomes a difficult task if the objects are not homogeneous and have overlapping characteristics. The Graph Cuts methods combined with Gaussian Mixture Model (GMM) for initialization label has been adopted to detect cattle object in an image with complex background. The RGB colors and Gray Level Co-occurrence Matrix (GLCM) textures are used as the features set. This method can robustly segment the cattle beef image from its background. This segmentation method produces the average of accuracy value up to 90%.
Texture feature extraction plays an important role in texture image classification. In this paper, we have proposed a texture feature extraction method by utilizing the Short-time Fourier Transform to provide local im...
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ISBN:
(纸本)9781479906505
Texture feature extraction plays an important role in texture image classification. In this paper, we have proposed a texture feature extraction method by utilizing the Short-time Fourier Transform to provide local image information, and for the global geometric correspondence we have proposed to use Spatial Pyramid Matching in frequency domain named as Short-time Fourier Transform with Spatial Pyramid Matching (STFT-SPM). The experiments are conducted on standard benchmark datasets for texture classification like Brodatz and KTH-TIPS2-a, shows that STFT-SPM can achieve significant improvement compared to the Local Phase Quantization, Weber local Descriptor and local Binary pattern methods.
In this paper, we try to deal with the problem of shadow detection from static images and video sequences. In instead to considering individual regions separately, we use relative illumination conditions between segme...
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Weak boundary contrast, inhomogeneous background and overlapped intensity distributions of the object and background are main causes that may lead to failure of boundary detection for many traditional active contour m...
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ISBN:
(纸本)9781479923427
Weak boundary contrast, inhomogeneous background and overlapped intensity distributions of the object and background are main causes that may lead to failure of boundary detection for many traditional active contour methods. In this paper, we propose a region-based active contour model to address these problems in both local and global ways. A localized active contour framework is developed, in which two local boundary measures are introduced for the evolution of the level set function. These measures are used to select the boundary candidates for boundary preservation such that the evolution of the contour is guided in a reasonable way. The object boundary is determined by a global boundary measure which evaluates the boundary completeness during the entire evolution process. The experiments demonstrate that our method works well against weak boundary contrast, inhomogeneous background and overlapped intensity distributions.
An image compression-encryption algorithm based on 2-D compressive sensing is proposed, which can accomplish encryption and compression simultaneously. The measurements are performed in two directi-ons and the measure...
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Figs like other agricultural products may include cancerogenic aflatoxin which is caused by Aspergillus type molds. Under the UV illumination, a large portion of the aflatoxin contaminated figs expose Bright Greenish ...
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Figs like other agricultural products may include cancerogenic aflatoxin which is caused by Aspergillus type molds. Under the UV illumination, a large portion of the aflatoxin contaminated figs expose Bright Greenish Yellow Fluorescence (BGYF) in visible light spectrum. Using the fluorescence properties, the contaminated figs are visually detected and manually removed by workers. However, this procedure could not eliminate all the aflatoxin contaminated figs and the UV exposure may cause skin cancer on workers under UV illumination. Besides, the reflectance outside the visible spectrum may include significant information for aflatoxin contamination. In this study, we investigate the NIR reflectance spectroscopy for the detection of aflatoxin contaminated figs and correctly classified the figs with 90% mean accuracy.
作者:
Erdal YenialpHabil KalkanVision
Image Processing and Pattern Recognition Laboratory (VIPLAB) Bilgisayar Mühendisliği Bölümü Süleyman Demirel Üniversitesi Isparta Turkey
Segmentation algorithms are widely used in imageprocessing. These methods have different complexity values and the choice of reasonable methods decreases on large images. Especially on the medical images with large s...
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Segmentation algorithms are widely used in imageprocessing. These methods have different complexity values and the choice of reasonable methods decreases on large images. Especially on the medical images with large size, it may take days to perform segmentation in some methods. However, parallel implementation may eliminate the drawback of these algorithms to some extent. In this study, we propose to implement segmentation algorithms in parallel using Graphical processing Unit. Using the proposed implementation, the computation time of the K-centers, K-means and DBSCAN algorithms were decreases 87, 642 and 2 times, respectively.
This paper proposes a new affine registration algorithm for 2D point matching. It is a two-step iterative registration algorithm by soft weight assignment based on bidirectional distance. At each iteration, the affine...
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ISBN:
(纸本)9781479923427
This paper proposes a new affine registration algorithm for 2D point matching. It is a two-step iterative registration algorithm by soft weight assignment based on bidirectional distance. At each iteration, the affine transformation is updated by two optimization steps, in which the model data and the test data are matched from each other respectively. By the optimization of registration at separate steps during each iteration, the proposed algorithm can provide a good estimate of the accurate affine transformation in condition of poor initialization and lack of geometric assumptions on point sets. Some experiments about comparison with the current state-of-the art approaches, demonstrate the robustness and accuracy of our method.
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