this paper proposes a segmentation method of heart cavities based on neural networks. Firstly, the ultrasound image is simplified with a homogeneity measure based on the variance. Secondly, the simplified image is cla...
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ISBN:
(纸本)9783642018107
this paper proposes a segmentation method of heart cavities based on neural networks. Firstly, the ultrasound image is simplified with a homogeneity measure based on the variance. Secondly, the simplified image is classified using a multilayer perceptron trained to produce an adequate generalization. thirdly, results from classification are improved by using simple imageprocessing techniques. the method makes it possible to detect the edges of cavities in an image sequence, selecting data for network training from a single image of the sequence. Besides, our proposal permits detection of cavity contours with techniques of a low computational cost, in a robust and accurate way, with a high degree of autonomy.
Robustness is a key attribute of spread spectrum (SS) watermarking scheme. It is significantly deteriorated if one tries to achieve high embedding rate keeping other parameters unaltered. In literatures, typically var...
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Robustness is a key attribute of spread spectrum (SS) watermarking scheme. It is significantly deteriorated if one tries to achieve high embedding rate keeping other parameters unaltered. In literatures, typically various transformations like DFT, DCT, Fourier-Mellin and wavelet are used for SS multimedia watermarking but little studies have been attempted so far to see what are the possible factors which can improve robustness. the current paper has critically analyzed few such factors namely design of code pattern, proper signal decomposition suitable for data embedding, direction of decomposition, selection of regions for data embedding, signaling scheme, choice of modulation functions and embedding strength. Based on the observation, wavelet based SS watermarking scheme is proposed and improvement in robustness performance is verified through experimental results as well by mathematical analysis. (c) 2006 Elsevier B.V. All rights reserved.
this book constitutes the refereed proceedings of the 6th International conference on Information processing, ICIP 2012, held in Bangalore, India, in August 2012. the 75 revised full papers presented were carefully re...
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ISBN:
(数字)9783642316869
ISBN:
(纸本)9783642316852
this book constitutes the refereed proceedings of the 6th International conference on Information processing, ICIP 2012, held in Bangalore, India, in August 2012. the 75 revised full papers presented were carefully reviewed and selected from 380 submissions. the papers are organized in topical sections on wireless networks; imageprocessing; pattern recognition and classification; computer architecture and distributed computing; software engineering, information technology and optimization techniques; data mining techniques; computer networks and network security.
Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance...
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ISBN:
(纸本)9781450366151
Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance, autonomous cars, etc. Also, a significant trend of latest research studies in related problem areas is the use of sophisticated Deep Learning based approaches to improve the benchmark performance on various standard datasets. A trade-off between the speed (number of video frames processed per second) and detection accuracy has often been reported in the existing literature. In this article, we present a new but simple deep learning based strategy for pedestrian detection that improves this trade-off. Since training of similar models using publicly available sample datasets failed to improve the detection performance to some significant extent, particularly for the instances of pedestrians of smaller sizes, we have developed a new sample dataset consisting of more than 80K annotated pedestrian figures in videos recorded under varying traffic conditions. Performance of the proposed model on the test samples of the new dataset and two other existing datasets, namely Caltech Pedestrian Dataset (CPD) and CityPerson Dataset (CD) have been obtained. Our proposed system shows nearly 16% improvement over the existing state-of-the-art result.
this paper focuses on shape description for machine printed Farsi/Arabic subwords retrieval. Fourier descriptor (FD) has been used frequently for shape retrieval applications. In this paper we proposed a simple and ef...
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image segmentation gained popularity recently due to numerous applications in many fields such as computervision, medical imaging. From its name, segmentation is interested in partitioning the image into separate reg...
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ISBN:
(纸本)9781479973491
image segmentation gained popularity recently due to numerous applications in many fields such as computervision, medical imaging. From its name, segmentation is interested in partitioning the image into separate regions where one of them is of special interest. Such region is called the Region of Interest (RoI) and it is very important for many medical imaging problems. Clustering is one of the segmentation approaches typically used on medical images despite its long running time. In this work, we propose to leverage the power of the graphicsprocessing Unit (GPU) to improve the performance of such approaches. Specifically, we focus on the Fuzzy C-Means (FCM) algorithm and its more recent variation, the Type-2 Fuzzy C-Means (T2FCM) algorithm. We propose a hybrid CPU-GPU implementation to speed up the execution time without affecting the algorithm's accuracy. the experiments show that such an approach reduces the execution time by up to 80% for FCM and 74% for T2FCM.
In this paper, we demonstrate that gender estimation technique can increase the accuracy of a face recognition system. If the gender of the input image can be estimated correctly before its recognition and compared on...
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From the early age of technology mankind is always fascinated about capturing the scenes around them. these photographs work as an important media to know and understand the past. Unfortunately, due to aging, repetiti...
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ISBN:
(纸本)9781450335522
From the early age of technology mankind is always fascinated about capturing the scenes around them. these photographs work as an important media to know and understand the past. Unfortunately, due to aging, repetitive usage and in presence of external reagents, the perceptual quality of these old images degrade severely. thus, restoration of vintage photographs is an important application of digital imageprocessing. In this paper we propose an algorithm which is able restore an image suffered with contrast fading and color cast. the approach can not only restore grayscale images but also is able to binarize archival documents.
Surface and sub-surface coal seam fires are detected by estimating Land Surface Temperature (LST). the LST of an area depends on several factors such as, seasonal variation, nature of soil, urban settlements, etc. Tem...
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Person re-identification has great applications in video surveillance. It can be viewed as recognizing the same person across non-overlapping cameras. Video-based person re-identification methods are gaining increased...
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ISBN:
(纸本)9781450366151
Person re-identification has great applications in video surveillance. It can be viewed as recognizing the same person across non-overlapping cameras. Video-based person re-identification methods are gaining increased attention due to the better discriminative nature of spatio-temporal feature representations. Current video-based methods make use of RNN to extract temporal information. In this paper, we propose a novel Moving Average Recurrent Neural Network (MA-RNN) model that can build a strong feature representation by taking both previous and present inputs at each time stamp. Specifically, here the recurrent layer produces a better sequential information by looking back directly in to the past values where as general RNNs has only an indirect dependence on the previous values in the form of hidden-state information. the proposed model is tested on two publicly available datasets: iLIDS-VID and PRID-2011 and it performed better in comparison withthe state-of-the-art methods with a significant margin. We also analyze the effect of the depth of previous input dependence of the MA-RNN model on the matching accuracy.
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