公开(公告)号：US09443277B2

公开(公告)日：2016-09-13

申请号：US14753020

申请日：2015-06-29

Applicant: Beijing University of Technology

Inventor： Wei Ma ,  Shuo Liu ,  Lijuan Duan

IPC: G06K9/00 ,  G06T1/00 ,  G06K9/62 ,  G06K9/46 ,  G06T3/40

CPC classification number: G06T1/0021 ,  G06K9/46 ,  G06K9/6215 ,  G06K2009/4666 ,  G06T1/0064 ,  G06T3/40 ,  G06T2201/0051 ,  G06T2201/0052 ,  G06T2201/0061 ,  G06T2201/0081 ,  G06T2207/20021

Abstract: A method for embedding and extracting a multi-scale space based watermark, comprises: constructing a pyramid structure of an original image by dividing each carrier image layer into M square carrier image blocks of the same size; constructing a multi-scale structure of a watermark image; embedding a watermark by embedding each watermark image into a corresponding carrier image block to obtain the original image containing the watermark; locating in the pyramid structure of the original image a target image from which a watermark will be extracted; extracting the watermark by obtaining an estimated watermark by means of the target image block and the reference image block; comparing watermarks by evaluating similarity between the estimated watermark and a watermark image to which the reference image block corresponds. Due to the multi-resolution block pyramid data structure in the present invention, a large scale attack is decomposed into a multi-level small scale attack.

Abstract translation: 一种用于嵌入和提取多尺度空间的水印的方法，包括：通过将每个载体图像层划分成相同尺寸的M个正方形载体图像块来构造原始图像的金字塔结构; 构建水印图像的多尺度结构; 通过将每个水印图像嵌入到相应的载体图像块中来嵌入水印以获得包含水印的原始图像; 在原始图像的金字塔结构中定位将从其中提取水印的目标图像; 通过目标图像块和参考图像块获得估计的水印来提取水印; 通过评估估计水印与参考图像块对应的水印图像之间的相似度来比较水印。 由于本发明的多分辨率块金字塔数据结构，大规模攻击被分解为多级小规模攻击。

公开(公告)号：US20160210528A1

公开(公告)日：2016-07-21

申请号：US14601254

申请日：2015-01-21

Applicant: Beijing University of Technology

Inventor： Lijuan Duan

IPC: G06K9/46 ,  G06K9/00 ,  G06K9/52

CPC classification number: G06K9/4671 ,  G06K9/00711 ,  G06K9/00744 ,  G06K9/4652 ,  G06K9/52

Abstract: The present invention relates to a method for detecting visual saliencies of a video image based on spatial and temporal features, including: dividing an input image into image blocks and vectorizing the image blocks; decreasing dimensions of each image block through principal component analysis; calculating a dissimilarity between each image block and each of the other image blocks; calculating a visual saliency of each image block by combining a distance between image blocks, to obtain a spatial feature saliency map; imposing a central bias on the spatial feature saliency map; calculating a motion vector of each image block, extracting a temporal visual saliency of the current image by combining motion vectors of previous two frames, to obtain a temporal feature saliency map; integrating the spatial feature saliency map and the temporal feature saliency map to obtain a spatiotemporal feature saliency map, and smoothing the spatiotemporal feature saliency map to obtain a resulted image finally reflecting a saliency of each region on the current image. In the present invention, a saliency map integrating the temporal features and the spatial features, so that saliencies in different regions in a video may be predicted more accurately.

公开(公告)号：US20160180188A1

公开(公告)日：2016-06-23

申请号：US14603282

申请日：2015-01-22

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Shuo Qiu ,  Wei Ma ,  Jun Miao ,  Jia Li

IPC: G06K9/46 ,  G06T7/00 ,  G06T5/10

CPC classification number: G06K9/4671 ,  G06K9/3233 ,  G06K9/4623 ,  G06T5/10 ,  G06T7/13 ,  G06T7/174 ,  G06T7/269 ,  G06T2207/10012 ,  G06T2207/10021 ,  G06T2207/20021

Abstract: The present invention discloses a method for detecting a salient region of a stereoscopic image, comprising: step 1) calculating flow information of each pixel separately with respect to a left-eye view and a right-eye view of the stereoscopic image; step 2) matching the flow information, to obtain a parallax map; step 3) selecting one of the left-eye view and the right-eye view, dividing it into T non-overlapping square image blocks; step 4) calculating a parallax effect value for each of the image blocks of the parallax map; step 5) for each of the image blocks of the selected one of the left-eye view and the right-eye view, calculating a central bias feature value and a spatial dissimilarity value, and multiplying the three values, to obtain a saliency value of the image block; and step 6) obtaining a saliency gray scale map of the stereoscopic image from saliency values of the image blocks. The present invention provides a method for extracting stereoscopic saliency based on parallax effects and spatial dissimilarity, acquiring depth information by utilizing parallax, and combining visual central bias feature and spatial dissimilarity to realize more accurate detection of a stereoscopic salient region.

公开(公告)号：US10008004B1

公开(公告)日：2018-06-26

申请号：US15406504

申请日：2017-01-13

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Fangfang Liang ,  Yuanhua Qiao ,  Wei Ma ,  Jun Miao

IPC: G06K9/00 ,  G06T7/593 ,  G06T17/00 ,  G06K9/52 ,  G06K9/46 ,  G06T15/20 ,  G06T7/194

Abstract: A method of establishing a 3D saliency model based on 3D contrast and depth weight, includes dividing left view of 3D image pair into multiple regions by super-pixel segmentation method, synthesizing a set of features with color and disparity information to describe each region, and using color compactness as weight of disparity in region feature component, calculating feature contrast of a region to surrounding regions; obtaining background prior on depth of disparity map, and improving depth saliency through combining the background prior and the color compactness; taking Gaussian distance between the depth saliency and regions as weight of feature contrast, obtaining initial 3D saliency by adding the weight of the feature contrast; enhancing the initial 3D saliency by 2D saliency and central bias weight.

公开(公告)号：US09501715B2

公开(公告)日：2016-11-22

申请号：US14603282

申请日：2015-01-22

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Shuo Qiu ,  Wei Ma ,  Jun Miao ,  Jia Li

IPC: G06K9/46 ,  G06T5/10 ,  G06T7/00

CPC classification number: G06K9/4671 ,  G06K9/3233 ,  G06K9/4623 ,  G06T5/10 ,  G06T7/13 ,  G06T7/174 ,  G06T7/269 ,  G06T2207/10012 ,  G06T2207/10021 ,  G06T2207/20021

Abstract: The present invention discloses a method for detecting a salient region of a stereoscopic image, comprising: step 1) calculating flow information of each pixel separately with respect to a left-eye view and a right-eye view of the stereoscopic image; step 2) matching the flow information, to obtain a parallax map; step 3) selecting one of the left-eye view and the right-eye view, dividing it into T non-overlapping square image blocks; step 4) calculating a parallax effect value for each of the image blocks of the parallax map; step 5) for each of the image blocks of the selected one of the left-eye view and the right-eye view, calculating a central bias feature value and a spatial dissimilarity value, and multiplying the three values, to obtain a saliency value of the image block; and step 6) obtaining a saliency gray scale map of the stereoscopic image from saliency values of the image blocks. The present invention provides a method for extracting stereoscopic saliency based on parallax effects and spatial dissimilarity, acquiring depth information by utilizing parallax, and combining visual central bias feature and spatial dissimilarity to realize more accurate detection of a stereoscopic salient region.

Abstract translation: 本发明公开了一种用于检测立体图像的显着区域的方法，包括：步骤1）分别计算相对于立体图像的左眼视图和右眼视图的每个像素的流动信息; 步骤2）匹配流信息，获得视差图; 步骤3）选择左眼视图和右眼视图之一，将其划分为T个非重叠的平方图像块; 步骤4）计算视差图的每个图像块的视差效应值; 步骤5）对于所选择的左眼视图和右眼视图中的每一个图像块，计算中心偏置特征值和空间相似度值，并且将三个值相乘以获得显着值 图像块; 和步骤6）从图像块的显着值获得立体图像的显着灰度图。 本发明提供一种基于视差效应和空间相异性提取立体显着性的方法，通过利用视差获取深度信息，并组合视觉中心偏置特征和空间相异性，以实现对立体显着区域的更准确的检测。

公开(公告)号：US10810490B2

公开(公告)日：2020-10-20

申请号：US15018656

申请日：2016-02-08

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Bin Yuan ,  Song Cui ,  Jun Miao ,  Junfa Liu

IPC: G06N3/08

Abstract: The present invention relates to a clustering method based on iterations of neural networks, which comprises the following steps: step 1, initializing parameters of an extreme learning machine; step 2, randomly choosing samples of which number is equal to the number of clusters, each sample representing one cluster, forming an initial exemplar set and training the extreme learning machine; step 3, using current extreme learning machine to cluster samples, which generates a clustering result; step 4, choosing multiple samples from each cluster as exemplars for the cluster according to a rule; step 5, retraining the extreme learning machine by using the exemplars for each cluster obtained from step 4; and step 6, going back to step 3 to do iteration, otherwise obtaining and outputting clustering result until clustering result is steady or a maximal limit of the number of iterations is reached. The present invention resolves problems that how to realize clustering of high dimensional and nonlinear data space and that the prior art consumes a larger memory or need longer running time.

公开(公告)号：US20180182118A1

公开(公告)日：2018-06-28

申请号：US15406504

申请日：2017-01-13

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Fangfang Liang ,  Yuanhua Qiao ,  Wei Ma ,  Jun Miao

IPC: G06T7/593 ,  G06T17/00 ,  G06K9/52 ,  G06K9/46 ,  G06T15/20 ,  G06T7/194

CPC classification number: G06T7/593 ,  G06K9/00201 ,  G06K9/4652 ,  G06K9/4676 ,  G06T7/194 ,  G06T15/20 ,  G06T17/00 ,  G06T2200/04

Abstract: A method of establishing a 3D saliency model based on 3D contrast and depth weight, includes dividing left view of 3D image pair into multiple regions by super-pixel segmentation method, synthesizing a set of features with color and disparity information to describe each region, and using color compactness as weight of disparity in region feature component, calculating feature contrast of a region to surrounding regions; obtaining background prior on depth of disparity map, and improving depth saliency through combining the background prior and the color compactness; taking Gaussian distance between the depth saliency and regions as weight of feature contrast, obtaining initial 3D saliency by adding the weight of the feature contrast; enhancing the initial 3D saliency by 2D saliency and central bias weight.

公开(公告)号：US09466006B2

公开(公告)日：2016-10-11

申请号：US14601254

申请日：2015-01-21

Applicant: Beijing University of Technology

Inventor： Lijuan Duan

IPC: G06K9/48 ,  G06K9/46 ,  G06K9/52 ,  G06K9/00

CPC classification number: G06K9/4671 ,  G06K9/00711 ,  G06K9/00744 ,  G06K9/4652 ,  G06K9/52

Abstract: The present invention relates to a method for detecting visual saliencies of a video image based on spatial and temporal features, including: dividing an input image into image blocks and vectorizing the image blocks; decreasing dimensions of each image block through principal component analysis; calculating a dissimilarity between each image block and each of the other image blocks; calculating a visual saliency of each image block by combining a distance between image blocks, to obtain a spatial feature saliency map; imposing a central bias on the spatial feature saliency map; calculating a motion vector of each image block, extracting a temporal visual saliency of the current image by combining motion vectors of previous two frames, to obtain a temporal feature saliency map; integrating the spatial feature saliency map and the temporal feature saliency map to obtain a spatiotemporal feature saliency map, and smoothing the spatiotemporal feature saliency map to obtain a resulted image finally reflecting a saliency of each region on the current image. In the present invention, a saliency map integrating the temporal features and the spatial features, so that saliencies in different regions in a video may be predicted more accurately.

Abstract translation: 本发明涉及一种用于基于空间和时间特征检测视频图像的视觉效率的方法，包括：将输入图像划分为图像块并对图像块进行矢量化; 通过主成分分析减少每个图像块的尺寸; 计算每个图像块和每个其他图像块之间的不相似性; 通过组合图像块之间的距离来计算每个图像块的视觉显着性，以获得空间特征显着图; 对空间特征显着图强加中心偏见; 计算每个图像块的运动矢量，通过组合前两帧的运动矢量来提取当前图像的时间视觉显着性，以获得时间特征显着图; 整合空间特征显着图和时间特征显着图，得到时空特征显着图，并对时空特征显着图进行平滑处理，得到最终反映当前图像各区域显着性的最终图像。 在本发明中，显示了将时间特征和空间特征相结合的显着图，从而可以更准确地预测视频中的不同区域中的细节。

公开(公告)号：US20150269336A1

公开(公告)日：2015-09-24

申请号：US14583127

申请日：2014-12-25

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Hui Ge ,  Zhen Yang ,  Yuanhua Qiao ,  Wei Ma ,  Haiyan Zhou

IPC: G06F19/00 ,  G06N99/00 ,  A61B5/0484 ,  A61B5/04 ,  A61B5/00 ,  G06N5/04 ,  A61B5/0478

CPC classification number: A61B5/0484 ,  A61B5/04012 ,  A61B5/0476 ,  A61B5/0478 ,  A61B5/04842 ,  A61B5/7267 ,  G06N20/00

Abstract: The present invention relates to a method for selecting features of EEG signals based on a decision tree: firstly, acquired multi-channel EEG signals are pre-processed, and then the pre-processed EEG signals are performed with feature extraction by utilizing principal component analysis, to obtain a analysis data set matrix with decreased dimensions; superior column vectors are obtained through analyzing from the analysis data set matrix with decreased dimensions by utilizing a decision tree algorithm, and all the superior column vectors are jointed with the number of the columns increased and the number of the rows unchanged, to be reorganized into a final superior feature data matrix; finally, the reorganized superior feature data matrix is input to a support vector machine (SVM) classifier, to perform a classification on the EEG signals, to obtain a classification accuracy. In the present invention, superior features are selected by utilizing a decision tree, to avoid influence of subjective factors during the selection, so that the selection is more objective and with a higher classification accuracy. The average classification accuracy through the present invention may reach 89.1%, increased by 0.9% compared to the conventional superior electrode reorganization.

Abstract translation: 本发明涉及一种基于决策树选择EEG信号特征的方法：首先，对所获取的多通道EEG信号进行预处理，然后通过利用主成分分析进行特征提取预处理的EEG信号 ，以获得尺寸减小的分析数据集矩阵; 通过利用决策树算法从具有减小的维度的分析数据集矩阵分析中获得优越的列向量，并且所有上级列向量与增加的列的数量和行数不变而被重新组合 最后的优势特征数据矩阵; 最后，将重组的优异特征数据矩阵输入到支持向量机（SVM）分类器，对EEG信号进行分类，以获得分类精度。 在本发明中，通过利用决策树来选择优越特征，以避免在选择期间主观因素的影响，使得选择更客观并且具有更高的分类精度。 本发明的平均分类精度可达89.1％，比常规优良电极重组提高0.9％。

公开(公告)号：US20150269191A1

公开(公告)日：2015-09-24

申请号：US14603376

申请日：2015-01-23

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Wei Ma ,  Zeming Zhao ,  Xuan Zhang ,  Jun Miao

IPC: G06F17/30

CPC classification number: G06F16/5838

Abstract: The present invention discloses a method for retrieving a similar image based on visual saliencies and visual phrases, comprising: inputting an inquired image; calculating a saliency map of the inquired image; performing viewpoint shift on the saliency map by utilizing a viewpoint shift model, defining a saliency region as a circular region which taking a viewpoint as a center and R as a radius, and shifting the viewpoint for k times to obtain k saliency regions of the inquired image; extracting a visual word in each of the saliency regions of the inquired image, to constitute a visual phrase, and jointing k visual phrases to generate an image descriptor of the inquired image; obtaining an image descriptor for each image of an inquired image library; and calculating a similarity value between the inquired image and each image in the inquired image library depending on the image descriptors by utilizing a cosine similarity, to obtain an image similar to the inquired image from the inquired image library. Through the present invention, noise in expression of an image is reduced, so that the expression of the image in a computer may be more consistent with human understanding of the semantics of the image, presenting a better retrieving effect and a higher retrieving speed.

Abstract translation: 本发明公开了一种基于视觉效果和视觉短语检索相似图像的方法，包括：输入查询图像; 计算查询图像的显着图; 通过利用视点移动模型对显着性图进行视点移动，将显着区域定义为以视点为中心并以R为半径的圆形区域，并且将视点移动k次以获得查询的k个显着区域 图片; 提取查询图像的每个显着区域中的视觉词，构成视觉短语，并且连接k个视觉短语以生成所查询的图像的图像描述符; 获取查询的图像库的每个图像的图像描述符; 并且通过利用余弦相似度，根据图像描述符计算查询图像与查询图像库中的每个图像之间的相似度值，以从查询的图像库中获得类似于查询图像的图像。 通过本发明，图像的表达噪声减小，使得计算机中的图像的表达可以更好地符合人们对图像语义的理解，呈现更好的检索效果和更高的检索速度。