Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results th...
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An algorithm for contour matching is presented in this paper. It is implemented in two steps: firstly, bottom-up, corners are matched, the matched corner points guide line segment matching, and then the matched line s...
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In recent years, iris recognition is becoming a very active topic in both research and practical applications. However, fake iris is a potential threat there are potential threats for iris-based systems. This paper pr...
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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%.
In this paper a framework for multichannel image restoration based on optimization of the structural similarity (SSIM) index is presented. The SSIM index describes the similarity of images more appropriately for the h...
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The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications...
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The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style *** transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output ***-GAN is a classic GAN model,which has a wide range of scenarios in style *** its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output ***,it is difficult for CYCLE-GAN to converge and generate high-quality *** order to solve this problem,spectral normalization is introduced into each convolutional kernel of the *** convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed ***,we use pretrained model(VGG16)to control the loss of image content in the position of l1 *** avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss *** terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative *** results show that the proposed model converges faster and achieves better FID scores than the state of the art.
This book constitutes the refereed proceedings of the 9th IAPR-TC-15 International Workshop on Graph-Based Representations in patternrecognition, GbRPR 2013, held in Vienna, Austria, in May 2013. The 24 papers presen...
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ISBN:
(数字)9783642382215
ISBN:
(纸本)9783642382208
This book constitutes the refereed proceedings of the 9th IAPR-TC-15 International Workshop on Graph-Based Representations in patternrecognition, GbRPR 2013, held in Vienna, Austria, in May 2013.
The 24 papers presented in this volume were carefully reviewed and selected from 27 submissions. They are organized in topical sections named: finding subregions in graphs; graph matching; classification; graph kernels; properties of graphs; topology; graph representations, segmentation and shape; and search in graphs.
Segmentation becomes a difficult task if the objects and background are not homogeneous and having overlapping characteristics. Cattle segmentation from its background is required in several typical applications, such...
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Segmentation becomes a difficult task if the objects and background are not homogeneous and having overlapping characteristics. Cattle segmentation from its background is required in several typical applications, such as: the automatic cattle race classification. The cattle's fur detection which is inspired from the human skin detection is investigated in this paper for cattle and background segmentation in automatic beef cattle race classification. The Gaussian mixture model that was used in skin detection has been adopted to model Bali cow and Hybrid Ongole cow in this beef cattle race classification. The RGB color space and two texture descriptors are used as the features set. The addition of texture descriptor has increased the performance of the fur detection and automatic race classification. The GMM performs well but the noise and the complexity of the background lead to misclassification.
Batik, as a cultural heritage from Indonesia, has a lot of motifs based on certain patterns. This paper discusses feature extraction methods for the recognition of batik motifs in digital images. In this study, the us...
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Batik, as a cultural heritage from Indonesia, has a lot of motifs based on certain patterns. This paper discusses feature extraction methods for the recognition of batik motifs in digital images. In this study, the use of several feature extraction methods have been compared in terms of their performance with several scenarios for testing level accuracy. The methods include Gray Level Co-occurrence Matrices (GLCM), Canny Edge Detection, and Gabor filters. The experimental results show that the use of GLCM features has performed the best with a classification accuracy reaching 80%.
In this paper descriptive visual features based on integral invariants are proposed to solve the global localization of indoor mobile robots. These descriptive features are locally extracted by applying a set of non-l...
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ISBN:
(纸本)3540302913
In this paper descriptive visual features based on integral invariants are proposed to solve the global localization of indoor mobile robots. These descriptive features are locally extracted by applying a set of non-linear kernel functions around a set ofinterest points in the image. To investigate the approach thoroughly, we use a set of images taken by re-assigning the robot position many times near a set of reference locations. Also, the presence of illumination variations is encountered many times inthe images. Compared to a well-known approach, our approach has better localization rate with moderate computational overhead.
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