In this paper we present an adaptive method for image compression that is based on complexity lev... more In this paper we present an adaptive method for image compression that is based on complexity level of the image. The basic compressor/de-compressor structure of this method is a multilayer perceptron artificial neural network. In adaptive approach different Back-Propagation artificial neural networks are used as compressor and de-compressor and this is done by dividing the image into blocks, computing the complexity of each block and then selecting one network for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability in complexity estimation are evaluated and compared. In training and evaluation, each image block is assigned to a network based on its complexity value. Best-SNR is another alternative in selecting compressor network for image blocks in evolution phase which chooses one of the trained networks such that results best SNR i...
Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn ... more Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept...
2009 International Multimedia, Signal Processing and Communication Technologies, 2009
... Fig. 4 shows the average robustness of GA-Dugad method against the proposed attack. This figu... more ... Fig. 4 shows the average robustness of GA-Dugad method against the proposed attack. This figure shows the proposed attack can successfully destroy the hidden watermark while it keeps the quality of the attacked image so high. ... 12, p. 1673, 1997. [15] MA Suhail and MS ...
Page 1. Secure Steganography Using Gabor Filter and Neural Networks Mansour Jamzad and Zahra Zahe... more Page 1. Secure Steganography Using Gabor Filter and Neural Networks Mansour Jamzad and Zahra Zahedi Kermani ⋆ Department of Computer Engineering, Sharif University of Technology, Tehran, Iran jamzad@sharif.edu, zahedike@ce.sharif.edu Abstract. ...
Segmentation plays an important role in the machine vision field. Extraction of dominant segments... more Segmentation plays an important role in the machine vision field. Extraction of dominant segments with large number of pixels is essential for some applications such as object detection. In this paper, a new approach is proposed for color image segmentation which uses ideas behind the social science and complex networks to find dominant segments. At first, we extract the color and texture information for each pixel of input image. A network that consists of some nodes and edges is constructed based on the extracted information. The idea of community detection in social networks is used to partition a color image into disjoint segments. Community detection means partitioning vertices of a network into different non-overlapped groups (communities) such that the density of intra-group edges is much higher than the density of intergroup edges. There is a very close relation between communities in the social network and segments in an image. Our results show that community detection appr...
Motion blur is one of the most common blurs that degrades images. Restoration of such images is h... more Motion blur is one of the most common blurs that degrades images. Restoration of such images is highly dependent on estimation of motion blur parameters. Since 1976, many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms ...
In this paper we present an adaptive method for image compression that is based on complexity lev... more In this paper we present an adaptive method for image compression that is based on complexity level of the image. The basic compressor/de-compressor structure of this method is a multilayer perceptron artificial neural network. In adaptive approach different Back-Propagation artificial neural networks are used as compressor and de-compressor and this is done by dividing the image into blocks, computing the complexity of each block and then selecting one network for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability in complexity estimation are evaluated and compared. In training and evaluation, each image block is assigned to a network based on its complexity value. Best-SNR is another alternative in selecting compressor network for image blocks in evolution phase which chooses one of the trained networks such that results best SNR i...
Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn ... more Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept...
2009 International Multimedia, Signal Processing and Communication Technologies, 2009
... Fig. 4 shows the average robustness of GA-Dugad method against the proposed attack. This figu... more ... Fig. 4 shows the average robustness of GA-Dugad method against the proposed attack. This figure shows the proposed attack can successfully destroy the hidden watermark while it keeps the quality of the attacked image so high. ... 12, p. 1673, 1997. [15] MA Suhail and MS ...
Page 1. Secure Steganography Using Gabor Filter and Neural Networks Mansour Jamzad and Zahra Zahe... more Page 1. Secure Steganography Using Gabor Filter and Neural Networks Mansour Jamzad and Zahra Zahedi Kermani ⋆ Department of Computer Engineering, Sharif University of Technology, Tehran, Iran jamzad@sharif.edu, zahedike@ce.sharif.edu Abstract. ...
Segmentation plays an important role in the machine vision field. Extraction of dominant segments... more Segmentation plays an important role in the machine vision field. Extraction of dominant segments with large number of pixels is essential for some applications such as object detection. In this paper, a new approach is proposed for color image segmentation which uses ideas behind the social science and complex networks to find dominant segments. At first, we extract the color and texture information for each pixel of input image. A network that consists of some nodes and edges is constructed based on the extracted information. The idea of community detection in social networks is used to partition a color image into disjoint segments. Community detection means partitioning vertices of a network into different non-overlapped groups (communities) such that the density of intra-group edges is much higher than the density of intergroup edges. There is a very close relation between communities in the social network and segments in an image. Our results show that community detection appr...
Motion blur is one of the most common blurs that degrades images. Restoration of such images is h... more Motion blur is one of the most common blurs that degrades images. Restoration of such images is highly dependent on estimation of motion blur parameters. Since 1976, many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms ...
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