At present,the internet pornographic text is in varied forms and changeful, although it is prohibited ever. It severely harms people's mental and physical health development and social stability. There are IP-base...
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At present,the internet pornographic text is in varied forms and changeful, although it is prohibited ever. It severely harms people's mental and physical health development and social stability. There are IP-based,keyword-based and intelligent content analysis filtering system against it today. But they are difficult to deal with manifestations of diversity, changeful, and increasingly concealed porn. In this paper, a in-depth research has done for the appeared characteristics of such undesirable information on our statistical analysis. And based on this characteristics, a new text pre-processing algorithm PA-PTCI is proposed, and a effective model combined with content filtering technology to the current text information filtering pornography problem is designed. The experimental result shows the PA-PTCI algorithm and the model of Chinese pornography text filtering are quite effective.
Resource Space Model (RSM) is a semantic model to manage and share heterogeneous resources on the Internet. This paper focuses on the general architecture, physical implementation and application of RSM. The RSM syste...
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Face alignment or facial landmark detection plays an important role in many computer vision applications, e.g., face recognition, facial expression recognition, face animation, etc. However, the performance of face al...
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ISBN:
(纸本)9781467388528
Face alignment or facial landmark detection plays an important role in many computer vision applications, e.g., face recognition, facial expression recognition, face animation, etc. However, the performance of face alignment system degenerates severely when occlusions occur. In this work, we propose a novel face alignment method, which cascades several Deep Regression networks coupled with De-corrupt Autoencoders (denoted as DRDA) to explicitly handle partial occlusion problem. Different from the previous works that can only detect occlusions and discard the occluded parts, our proposed de-corrupt autoencoder network can automatically recover the genuine appearance for the occluded parts and the recovered parts can be leveraged together with those non-occluded parts for more accurate alignment. By coupling de-corrupt autoencoders with deep regression networks, a deep alignment model robust to partial occlusions is achieved. Besides, our method can localize occluded regions rather than merely predict whether the landmarks are occluded. Experiments on two challenging occluded face datasets demonstrate that our method significantly outperforms the state-of-the-art methods.
In this paper, we present a new method for the design of an n-bit synchronous binary up counter in quantum-dot cellular automata (QCA). This method is based on the JK flip-flop which almost always produces the simples...
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Emotion identification, which aims to determine a person’s affective state automatically, has immense potential value in many areas, such as action tendency, health care, psychological detection and human-computer (r...
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It has become a challenging work to collect valuable information from fast text streams. In this work, we propose a method which gains useful information effectively and efficiently. Firstly, we maintain an analyzer b...
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Tree-based statistical machine translation models have made significant progress in recent years, especially when replacing 1-best trees with packed forests. However, as the parsing accuracy usually goes down dramatic...
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Text Sentiment Classification, a significant task in Natural Language processing, aims to comprehend user needs and expectations by categorizing the sentiments of texts posted on platforms. Despite their utility, exis...
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In this paper, a technique for the extraction of roads in a high resolution synthetic aperture radar (SAR) image is presented. And a three-step method is developed for the extraction of road network from space borne S...
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ISBN:
(纸本)9780819469540
In this paper, a technique for the extraction of roads in a high resolution synthetic aperture radar (SAR) image is presented. And a three-step method is developed for the extraction of road network from space borne SAR image: the process of the feature points, road candidate detection and connection. Roads in a high resolution SAR image can be modeled as a homogeneous dark area bounded by two parallel boundaries. Dark areas, which represent the candidate positions for roads, are extracted from the image by a Gaussian probability iteration segmentation. Possible road candidates are further processed using the morphological operators. And the roads are accurately detected by Hough Transform, and the extraction of lines is achieved by searching the peak values in Hough Space. In this process, to detect roads more accurately, post-processing, including noisy dark regions removal and false roads removal is performed. At last, Road candidate connection is carried out hierarchically according to road established models. Finally, the main road network is established from the SAR image successfully. As an example, using the ERS-2SAR image data, automatic detection of main road network in Shanghai Pudong area is presented.
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