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Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11,   Pages 1535-1670 doi: 10.1631/FITEE.1900481

Abstract: has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for . While traditional methods focus merely on solving one specific factor, we aggregate sufficient context information into the network to tackle these problems simultaneously in this study. We build a fully convolutional network with two tasks, i.e., main density map estimation and auxiliary . The main task is to extract the multi-scale and spatial context information to learn the density map. The auxiliary task gives a comprehensive view of the background and foreground information, and the extracted information is finally incorporated into the main task by late fusion. We demonstrate that our network has better accuracy of estimation and higher robustness on three challenging datasets compared with state-of-the-art methods.

Keywords： 人群计数；卷积神经网络；密度估计；语义分割；多任务学习    

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Semantic composition of distributed representations for query subtopic mining None

Wei SONG, Ying LIU, Li-zhen LIU, Han-shi WANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1409-1419 doi: 10.1631/FITEE.1601476

Abstract:

Inferring query intent is significant in information retrieval tasks. Query subtopic mining aims to find possible subtopics for a given query to represent potential intents. Subtopic mining is challenging due to the nature of short queries. Learning distributed representations or sequences of words has been developed recently and quickly, making great impacts on many fields. It is still not clear whether distributed representations are effective in alleviating the challenges of query subtopic mining. In this paper, we exploit and compare the main semantic composition of distributed representations for query subtopic mining. Specifically, we focus on two types of distributed representations: paragraph vector which represents word sequences with an arbitrary length directly, and word vector composition. We thoroughly investigate the impacts of semantic composition strategies and the types of data for learning distributed representations. Experiments were conducted on a public dataset offered by the National Institute of Informatics Testbeds and Community for Information Access Research. The empirical results show that distributed semantic representations can achieve outstanding performance for query subtopic mining, compared with traditional semantic representations. More insights are reported as well.

Keywords： Subtopic mining     Query intent     Distributed representation     Semantic composition    

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Toward Wisdom-Evolutionary and Primitive-Concise 6G: A New Paradigm of Semantic Communication Networks Article

Ping Zhang, Wenjun Xu, Hui Gao, Kai Niu, Xiaodong Xu, Xiaoqi Qin, Caixia Yuan, Zhijin Qin, Haitao Zhao, Jibo Wei, Fangwei Zhang

Engineering 2022, Volume 8, Issue 1,   Pages 60-73 doi: 10.1016/j.eng.2021.11.003

Abstract:

The sixth generation (6G) mobile networks will reshape the world by offering instant, efficient, and intelligent hyper-connectivity, as envisioned by the previously proposed Ubiquitous-X 6G networks. Such hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks, calling for revolutionary theories and technological innovations. To this end, we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network (WePCN) vision for the Ubiquitous-X 6G network. In particular, we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework, namely semantic base, and then establishing an intelligent and efficient semantic communication (IE-SC) network architecture. In the IE-SC architecture, a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer, network protocol layer, and application-intent layer via semantic information flows. The proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in 6G. It features a lower bandwidth requirement, less redundancy, and more accurate intent identification. We also present a brief review of recent advances in semantic communications and highlight potential use cases, complemented by a range of open challenges for 6G.

Keywords： 6G     Semantic information     Semantic communication     Intelligent communication    

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Mechanized semantics and refinement of UML-Statecharts Article

Feng SHENG, Liang DOU, Zong-yuan YANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1773-1783 doi: 10.1631/FITEE.1601196

Abstract: The Unified Modeling Language (UML) is an industry standard for modeling analysis and design. However, the semantics of UML is not precisely defined and the correctness of refinement relations cannot be verified. In this study, we use the theorem proof assistant Coq to formalize and mechanize the semantics of UMLStatecharts and the refinement relations between models. Based on the mechanized semantics, the desired properties of both the semantics and the refinement relations can be described and proven as predicates and lemmas. This approach provides a promising way to obtain certified fault-free modeling and refinement.

Keywords： Unified Modeling Language (UML)-Statecharts     Coq     Refinement     Structured operational semantics    

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A saliency and Gaussian net model for retinal vessel segmentation Research Articles

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1075-1086 doi: 10.1631/FITEE.1700404

Abstract: Retinal vessel segmentation is a significant problem in the analysis of fundus images. A novel deep learning structure called the Gaussian net (GNET) model combined with a saliency model is proposed for retinal vessel segmentation. A saliency image is used as the input of the GNET model replacing the original image. The GNET model adopts a bilaterally symmetrical structure. In the left structure, the first layer is upsampling and the other layers are max-pooling. In the right structure, the final layer is max-pooling and the other layers are upsampling. The proposed approach is evaluated using the DRIVE database. Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models. The proposed algorithm performs well in extracting vessel networks, and is more accurate than other deep learning methods. Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.

Keywords： Retinal vessel segmentation     Saliency model     Gaussian net (GNET)     Feature learning    

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Vascular segmentation of neuroimages based on a prior shape and local statistics Research Articles

Yun TIAN, Zi-feng LIU, Shi-feng ZHAO

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1099-1108 doi: 10.1631/FITEE.1800129

Abstract: Fast and accurate extraction of vascular structures from medical images is fundamental for many clinical procedures. However, most of the vessel segmentation techniques ignore the existence of the isolated and redundant points in the segmentation results. In this study, we propose a vascular segmentation method based on a prior shape and local statistics. It could efficiently eliminate outliers and accurately segment thick and thin vessels. First, an improved vesselness filter is defined. This quantifies the likelihood of each voxel belonging to a bright tubular-shaped structure. A matching and connection process is then performed to obtain a blood-vessel mask. Finally, the region-growing method based on local statistics is implemented on the vessel mask to obtain the whole vascular tree without outliers. Experiments and comparisons with Frangi’s and Yang’s models on real magneticresonance-angiography images demonstrate that the proposed method can remove outliers while preserving the connectivity of vessel branches.

Keywords： Vesselness filter     Neighborhood     Blood-vessel segmentation     Outlier    

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Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1332-1348 doi: 10.1631/FITEE.2200299

Abstract: Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation. However, existing methods often fall into what we call interactive misunderstanding, the essence of which is the dilemma in trading off short- and long-term interaction information. To better use the interaction information at various timescales, we propose an interactive segmentation framework, called interactive MEdical image segmentation with self-adaptive Confidence CAlibration (MECCA), which combines action-based confidence learning and multi-agent reinforcement learning. A novel confidence network is learned by predicting the alignment level of the action with short-term interaction information. A confidence-based reward-shaping mechanism is then proposed to explicitly incorporate confidence in the policy gradient calculation, thus directly correcting the model’s interactive misunderstanding. MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance, respectively. Numerical experiments on different segmentation tasks show that MECCA can significantly improve short- and long-term interaction information utilization efficiency with remarkably fewer labeled samples. The demo video is available at https://bit.ly/mecca-demo-video.

Keywords： Medical image segmentation     Interactive segmentation     Multi-agent reinforcement learning     Confidence learning     Semi-supervised learning    

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Incorporating target language semantic roles into a string-to-tree translation model Article

Chao SU, Yu-hang GUO, He-yan HUANG, Shu-min SHI, Chong FENG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1534-1542 doi: 10.1631/FITEE.1601349

Abstract: The string-to-tree model is one of the most successful syntax-based statistical machine translation (SMT) models. It models the grammaticality of the output via target-side syntax. However, it does not use any semantic information and tends to produce translations containing semantic role confusions and error chunk sequences. In this paper, we propose two methods to use semantic roles to improve the performance of the string-to-tree translation model: (1) adding role labels in the syntax tree; (2) constructing a semantic role tree, and then incorporating the syntax information into it. We then perform string-to-tree machine translation using the newly generated trees. Our methods enable the system to train and choose better translation rules using semantic information. Our experiments showed significant improvements over the state-of-the-art string-to-tree translation system on both spoken and news corpora, and the two proposed methods surpass the phrase-based system on large-scale training data.

Keywords： Machine translation     Semantic role     Syntax tree     String-to-tree    

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Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation None

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 471-480 doi: 10.1631/FITEE.1620342

Abstract: The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging (MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. Sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of × × around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value (PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region (dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core (dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor (dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.

Keywords： Brain tumor segmentation     Kernel method     Sparse coding     Dictionary learning    

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Segmentation and focus-point location based on boundary analysis in forest canopy hemispherical photography Article

Jia-yin SONG,Wen-long SONG,Jian-ping HUANG,Liang-kuan ZHU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 8,   Pages 741-749 doi: 10.1631/FITEE.1601169

Abstract: Analysis of forest canopy hemisphere images is one of the most important methods for measuring forest canopy structure parameters. In this study, our main focus was on using circular image region segmentation, which is the basis of forest canopy hemispherical photography. The boundary of a forest canopy hemisphere image was analyzed via histogram, rectangle, and Fourier descriptors. The image boundary characteristics were defined and obtained based on the following: (1) an edge model that contains three parts, i.e., step, ramp, and roof; (2) boundary points of discontinuity; (3) an edge that has a linear distribution of scattering points. On this basis, we proposed a segmentation method for the circular region in a forest canopy hemisphere image, fitting the circular boundary and computing the center and radius by the least squares method. The method was unrelated to the parameters of the image acquisition device. Hence, this study lays a foundation for automatically adjusting the parameters of high-performance image acquisition devices used in forest canopy hemispherical photography.

Keywords： Fisheye lens     Least squares method     Image segmentation     Ecology in image processing     Hemispherical photography    

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Interactive image segmentation with a regression based ensemble learning paradigm Article

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 1002-1020 doi: 10.1631/FITEE.1601401

Abstract: To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase of manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines (MARS) and smooth thin plate spline regression (TPSR). Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression (SVR) based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com-parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in-teractive natural image segmentation.

Keywords： Interactive image segmentation     Multivariate adaptive regression splines (MARS)     Ensemble learning     Thin-plate spline regression (TPSR)     Semi-supervised learning     Support vector regression (SVR)    

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Spacecraft damage infrared detection algorithm for hypervelocity impact based on double-layer multi-target segmentation Research Article

Xiao YANG, Chun YIN, Sara DADRAS, Guangyu LEI, Xutong TAN, Gen QIU,yinchun.86416@163.com,chunyin@uestc.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 571-586 doi: 10.1631/FITEE.2000695

Abstract: To detect spacecraft damage caused by hypervelocity impact, we propose an advanced spacecraft defect extraction algorithm based on infrared imaging detection. The (GMM) is used to classify the temperature change characteristics in the sampled data of the infrared video stream and reconstruct the image to obtain the infrared reconstructed image (IRRI) reflecting the defect characteristics. The designed segmentation objective function is used to ensure the effectiveness of results for noise removal and detail preservation, while taking into account the complexity of IRRI (that is, the required trade-offs are different). A multi-objective optimization algorithm is introduced to achieve balance between detail preservation and noise removal, and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used for optimization to ensure damage segmentation accuracy. Experimental results verify the effectiveness of the proposed algorithm.

Keywords： Hypervelocity impact damage     Defect detection     Gaussian mixture model     Image segmentation    

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Survey on Extraction Methods of Transition Region

Liu Suolan,Yang Jingyu

Strategic Study of CAE 2007, Volume 9, Issue 9,   Pages 89-96

Abstract:

Image segmentation is treated as a key issue in image processing and machine vision,  and has been the bottleneck of development.  Transition region is a special region located between the object and background.  Having the aid of extraction of transition region to segment image is a kind of burgeoning technology.  Two kinds of methods have been introduced: methods based on gradient and methods based on non-gradient.  Briefly,  the extracted effects and existent problem have been analyzed.

Keywords： transition region     extraction     image segmentation     gradient method     non-gradient method    

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Analysis of A Block Cipher Based on Chaos

Jin Chenhui

Strategic Study of CAE 2001, Volume 3, Issue 6,   Pages 75-80

Abstract:

In this paper, it is pointed out that the block cipher proposed in “Design of Block Cipher substitution network on chaos” can be broken by attack with known plaintext and attack with ciphertext only, and the key of this cipher can be found by the divide-and-conquer attack with the encipher transformation. Furthermore, the mutual restriction between the successive values of the chaos sequences based on the Logistic mapping, and the property that the frontal values of the chaos sequences are not sensitive to the bits on the lower po-sitions of the initial value are also pointed out.

Keywords： chaos sequence     block cipher     transposition cipher     attack with known plaintext     attack with ciphertext only     divide-and-conquer attack    

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Semantically condensed multi-relational frequent pattern discovery based on conjunctive query containment

Yang Bingru,Zhang Wei,Qian Rong

Strategic Study of CAE 2008, Volume 10, Issue 9,   Pages 47-53

Abstract:

Multi-relational data mining is one of rapidly developing subfields of data mining. Multi-relational frequent pattern discovery approaches directly look for frequent patterns that involve multiple relations from a relational database. While the state-of-the-art of multi-relational frequent pattern discovery approaches is based on the inductive logical programming techniques, we propose an approach to semantically condensed multi-relational frequent pattern discovery based on conjunctive query containment in terms of the theory and technique of relational database. With the novelty of the groundwork, the proposed approach deals with two kinds of semantically redundant problems. In theory and experiments, it shows that our approach improve the understandability, function, efficiency and scalability of the state-of-the-art of multi-relational frequent pattern discovery approaches.

Keywords： multi-relational data mining     frequent pattern discovery     conjunctive query     condensed pattern    

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Title Author Date Type Operation

Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Journal Article

Semantic composition of distributed representations for query subtopic mining

Wei SONG, Ying LIU, Li-zhen LIU, Han-shi WANG

Journal Article

Toward Wisdom-Evolutionary and Primitive-Concise 6G: A New Paradigm of Semantic Communication Networks

Ping Zhang, Wenjun Xu, Hui Gao, Kai Niu, Xiaodong Xu, Xiaoqi Qin, Caixia Yuan, Zhijin Qin, Haitao Zhao, Jibo Wei, Fangwei Zhang

Journal Article

Mechanized semantics and refinement of UML-Statecharts

Feng SHENG, Liang DOU, Zong-yuan YANG

Journal Article

A saliency and Gaussian net model for retinal vessel segmentation

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU

Journal Article

Vascular segmentation of neuroimages based on a prior shape and local statistics

Yun TIAN, Zi-feng LIU, Shi-feng ZHAO

Journal Article

Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Journal Article

Incorporating target language semantic roles into a string-to-tree translation model

Chao SU, Yu-hang GUO, He-yan HUANG, Shu-min SHI, Chong FENG

Journal Article

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Journal Article

Segmentation and focus-point location based on boundary analysis in forest canopy hemispherical photography

Jia-yin SONG,Wen-long SONG,Jian-ping HUANG,Liang-kuan ZHU

Journal Article

Interactive image segmentation with a regression based ensemble learning paradigm

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

Journal Article

Spacecraft damage infrared detection algorithm for hypervelocity impact based on double-layer multi-target segmentation

Xiao YANG, Chun YIN, Sara DADRAS, Guangyu LEI, Xutong TAN, Gen QIU,yinchun.86416@163.com,chunyin@uestc.edu.cn

Journal Article

Survey on Extraction Methods of Transition Region

Liu Suolan,Yang Jingyu

Journal Article

Analysis of A Block Cipher Based on Chaos

Jin Chenhui

Journal Article

Semantically condensed multi-relational frequent pattern discovery based on conjunctive query containment

Yang Bingru,Zhang Wei,Qian Rong

Journal Article