We study the denoising performance of several graph wavelet filter banks andimage modeling with graph to enhance understanding of the relationship between graph signals and underlying structure. We design a new metri...
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ISBN:
(纸本)9781509062386
We study the denoising performance of several graph wavelet filter banks andimage modeling with graph to enhance understanding of the relationship between graph signals and underlying structure. We design a new metric for measuring graph structure similarity (GSSIM) to evaluate those methods. GSSIM is positively related to PSNR as an index of corrupted signal with an additive Gaussian noise in the references and it is aware of graph structure. Subgraph-Based filter banks are superior to others in graph signal denoising. We introduce the idea of group-based approaches by removing edges between different groups, a set of communities with similar average. It can improve the signal to noise ratio substantially and reduce the high frequency loss. We demonstrate GSSIM is promise through intuitive examples. Group-based analysis improves the denoising effect of all the methods studied.
Firstly, the principle of AGV vision guidance is introduced and the deviation and deflection angle are measured by image coordinate system. The visual guidance imageprocessing platform is introduced. In view of the f...
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Firstly, the principle of AGV vision guidance is introduced and the deviation and deflection angle are measured by image coordinate system. The visual guidance imageprocessing platform is introduced. In view of the fact that the AGV guidance image contains more noise, the image has already been smoothed by a statistical sorting. By using AGV sampling way to obtain image guidance, because the image has the best and different threshold segmentation points. In view of this situation, the method of two-dimensional maximum entropy image segmentation is used to solve the problem. We extract the foregroundimage in the target band by calculating the contour area method and obtain the centre line with the least square fitting algorithm. With the help of image and physical coordinates, we can obtain the guidance information.
Considering a classic image-retrieval problem, a user submits a set of index images to a system and through repeated interactions, the goals are narrowed to the image(s) that satisfies the user. To this purpose, conve...
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ISBN:
(纸本)9781467329644;9781467329637
Considering a classic image-retrieval problem, a user submits a set of index images to a system and through repeated interactions, the goals are narrowed to the image(s) that satisfies the user. To this purpose, conventional content-based image retrieval (CBIR) paradigm make uses of imageprocessing andcomputer-vision techniques, and tries to understand the visual(color, shape, texture etc) image content. We propose a novel paradigm to this problem from a totally different angle. It attempts to use the human's intuition capabilities. Instead of processingimages, the system simply accumulates records of user feedback and recycles them in the form of collaborative filtering, just like a purchase recommendation system such as Amazo web. we use paradigm by a term "content-free" image retrieval (CFIR). We discuss many issues of visual retrieval, argue for the idea of CFIR, and present results of experiment. The experiment results show that the performance of CFIR improve image performance.
In this paper, a hybrid model for detecting text regions from scene images as well as document image is presented. At first, background is suppressed to isolate foreground regions. Then, morphological operations are a...
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This paper presents an image hashing technique for content verification using Discrete Wavelet Transform (DWT) approximation features. The proposed technique converts resized RGB color images to L*a*b* color images. F...
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ISBN:
(纸本)9789811021046;9789811021039
This paper presents an image hashing technique for content verification using Discrete Wavelet Transform (DWT) approximation features. The proposed technique converts resized RGB color images to L*a*b* color images. Further, images are regularized using Gaussian low pass filter. A level 2, 2D DWT is applied on L* component of L*a*b* color image and the LL2 approximation sub-bandimage is chosen for feature extraction. The features are extracted by utilizing a sequence of circles on approximation sub-bandimage. Finally, the robust binary hash is generated from extracted features. The experimental results indicate that the hash of the presented technique is invariant to standard content preserving manipulations and malicious content altering operations. The experiment results of Receiver Operating Characteristics (ROC) plots indicate that the presented technique shows strong discriminative and robustness capabilities. Besides, the hash of the proposed technique is shorter in length and key dependent.
Feature extraction and classifier design are two main processing blocks in all pattern recognition andcomputervision systems. For visual patterns, extracting robust and discriminative features from image is the most...
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ISBN:
(纸本)9781424445189
Feature extraction and classifier design are two main processing blocks in all pattern recognition andcomputervision systems. For visual patterns, extracting robust and discriminative features from image is the most difficult yet the most critical step. Several typical and advanced approaches of feature extraction from image are explored, some of which are analyzed in depth. Various techniques of feature extraction from image are organized in four categories: human expert knowledge based methods, image local structure based approaches, image global structure based techniques and machine learning based statistical approaches. We will show examples of applying these feature extraction approaches to solve problems of the image based biometrics, including fingerprint verification/identification and face detection/recognition. These illustrative application examples unveil the ideas, principles and advancements of feature extraction techniques and demonstrate their effectiveness and limitations in solving real-world problems.
One significant method of expressing the opinion of the users of social network is expressing true feelings or emotions through chats and comments for images, status or videos that has been uploaded to social network....
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ISBN:
(纸本)9781509062386
One significant method of expressing the opinion of the users of social network is expressing true feelings or emotions through chats and comments for images, status or videos that has been uploaded to social network. This will increases the effectiveness of the communication among users since they have no face-to-face interaction. Text processing techniques are used to identify emotion which expressed through text. The research proposes an approach to get overall emotion from comment text towards an image, which uploaded to social network. The new methodology was developed as an enhanced extension of the previous works and using appropriate improvement and extension to them with Latent Semantic Analysis (LSA) because previous researches have proven that LSA is a light weight approach. And it is believed that, online emotion predicting systems must be light weight. The research achieved more efficiency than previous works due to light weight of the methodology, also introduced a prototype of GUI. Also the topic is open for future enhancement.
Aiming at the problem of Autofocus window selection in imaging system, a new algorithm based on visual saliency for Autofocus window selection is proposed, which provides a new solution. A re-designed Itti model is us...
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ISBN:
(纸本)9781509062386
Aiming at the problem of Autofocus window selection in imaging system, a new algorithm based on visual saliency for Autofocus window selection is proposed, which provides a new solution. A re-designed Itti model is used to predict the salient region in the visual scene. By choosing the local maxima in the saliency map to be the seed, the most salient region can be obtained by growing around it and a minimum enclosing rectangle can be found as the focus window. In this paper, the focus window selection based on visual saliency can efficiently capture the visual salient region and the position of general target well, highlight potential focus targets and improves the accuracy of focusing. Compared with the common focusing window selection algorithm, the method proposed in this paper can improve the focusing performance of the imaging system and has wider applicability in the general scene.
Super-resolution image reconstruction is one of the important issues in the field of computervision. Machine learning is also the powerful method to solve the problem of computervision. The method of SRCNN, which is...
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ISBN:
(纸本)9781509062386
Super-resolution image reconstruction is one of the important issues in the field of computervision. Machine learning is also the powerful method to solve the problem of computervision. The method of SRCNN, which is put forward by Tang, is used for image super-resolution and shows the state-of-the-art performance. However, the method of SRCNN still has some shortcomings. On one hand, the training network convergence is slow and one trained network can only handle one settled scale factor model. Meanwhile, the input and output of the network contains a large number of the same information. It could lead to redundant training. On the other hand, the accuracy of the super-resolution reconstruction needs to be further improved. In order to improve these weaknesses and inspired by the network of the VGG and Res Net, we propose the method to restore the high frequency information of the image based on the residual network and enlarge the range of the effective vision perception based on very deep convolutional network. Through these improvements, we speed up the convergence rate of the training and improve the super-resolution image reconstruction accuracy. Especially some areas such as image detail and depth discontinuity have good performance. These improvements can furtherly optimize the single image super-resolution method, and expand its application scope. In the end, comparing with several advanced methods, such as SRCNN though the experiment, we confirm the validity and practicability of the improved strategy.
Multimodal medical image fusion techniques are utilized to fuse two images obtained from dissimilar sensors for obtaining additional information. These methods are used to fuse computed tomography (CT) images with mag...
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