一种面向视觉注意区域检测的运动分割方法*

结合视觉显著区检测的特点,本文提出一种面向视觉注意区域检测的运动分割方法。该方法用一种层次聚类方法将特征点的运动轨迹进行聚类。首先用中值偏移算法扩大了不同类型运动之间特征向量的差距,同时缩小了相同运动类型的差别。继而,用一种无监督聚类算法,将不同类型的运动进行分割,同时自动获得运动分类数。最后利用运动分割结果,提出一种结合空间和颜色采样的运动显著区域生成方法。与以往方法相比,该方法能够将不同类型的运动自动进行分割,生成的视觉注意区域更为准确,而且稳定性大幅提高。实验结果证明了该方法的有效性和稳定性。     

一种结合显著性检测与词袋模型的目标识别方法

针对词袋模型易受到无关的背景视觉噪音干扰的问题,提出了一种结合显著性检测与词袋模型的目标识别方法。首先,联合基于图论的视觉显著性算法与一种全分辨率视觉显著性算法,自适应地从原始图像中获取感兴趣区域。两种视觉显著性算法的联合可以提高获取的前景目标的完整性。然后,使用尺度不变特征变换描述子从感兴趣区域中提取特征向量,并通过密度峰值聚类算法对特征向量进行聚类,生成视觉字典直方图。最后,利用支持向量机对目标进行识别。在PASCAL VOC 2007和MSRC-21数据库上的实验结果表明,该方法相比同类方法可以有效地提高目标识别性能。     

利用LapSVM的快速显著性检测方法

目的 随着互联网技术的发展,信息的数量呈几何级数增长。信息改变着人类的传统生活方式,它可以给人们的娱乐,教育,商业活动提供便利。但是另一方面,需要处理的信息数量大大超过了计算机的处理能力,因此,如何使计算机能像人眼一样可以自动在大量信息中选择重要信息进行加工就显得十分重要。图像的视觉显著性信息能够反映图像中不同区域对人视觉系统刺激的程度。可靠的显著性方法可以从大量的信息中自动预测预测和挖掘重要的视觉信息,这些信息可以为图像分割、图像检索等应用提供有价值的线索。目前,显著性检测算法的鲁棒性和实时性是研究的热点。本文提出一种基于拉普拉斯支持向量机（LapSVM）的快速显著性检测方法。方法 采用简单线性迭代聚类算法SLIC（simple linear iterative clustering）将原始图像首先分成若干个超像素块,并用它代替图像像素参与计算,可以减少算法所需的计算量。利用超像素之间相似性构建图Laplacian。分析每个图像块的边缘特性定义粗糙标识样本,并利用一种快速LapSVM进行分类。LapSVM是一种基于流形正则化的半监督分类方法。通过引入提前停止机制来加速LapSVM的训练。这样可以快速地计算出一个近似结果。计算的复杂性由原来的O（n³）降到了O（kn²）,其中n是未标识样本和标识样本的数量。k是经验评价值,它远远小于n。通过分析得到的分类结果,提取出更准确的背景和目标样本作为新的标识样本再次进行LapSVM分类。最后,利用能量函数对分类结果进行优化得到最终的显著性检测结果。结果 ASD数据库是MSRA数据库的子集,包含1 000幅图片,被广泛用于各种显著性检测算法的实验中。本文算法在ASD图像数据库上与7种流行的图像显著性检测算法进行对比实验。本文算法不仅在准确率和召回率上保持了与其他算法相当的鲁棒性,平均绝对误差达到4%左右,同时算法的平均运行时间缩短为0.03 s左右。结论 提出一种基于LapSVM的快速图像显著性检测算法,通过区域边缘特征和分类结果分析,实现图像中背景和目标样本的准确检测。实验结果表明,本文算法具有良好的鲁棒性,显著地提高了算法的实时性。因此,与其他算法相比本文算法更适用于检测跟踪等实时性要求较高的场合。该方法可以在较短的时间内,以更好地准确率水平提取显著性区域。     

基于图像显著性检测的图像分割

图像分割在许多图像处理和机器视觉问题中是一个非常重要的过程,是将一幅图分割成几个显著的区域,然而不能将其中最显著的目标直接分割出来,需要进一步处理。为此本文采用显著性检测的算法实现了对目标的分割。显著性区域检测可以应用于目标检测、图像检索、图像分割等机器视觉问题。使用杨等人提出的基于图论的流形排序算法检测显著性算法得到显著性图,再结合mean-shift分割算法,实现了对视觉显著性目标分割提取,可获得可观的图像分割结果,并将此算法应用到了森林火灾检测中,能对图像中的火焰部分进行有效的分割提取。     

基于前景分割的目标实时检测方法

针对基于单一颜色信息的目标分割算法易受光线因素影响的问题,提出一种颜色及深度信息融合进行前景分割的目标实时检测方法。采用Kinect传感器采集低成本深度(RGB-D)图像,利用改进的ViBe算法及多帧差分法分别对于RGB以及深度图像进行建模。前景分割后,利用选取基准(SC)融合策略优化目标结果,然后通过rg Chromaticity颜色模型计算前景区域直方图信息并与模板匹配完成目标标记。实验结果表明,该方法对于环境光线及噪声干扰具有一定的鲁棒性,对于ViBe算法中背景前景同色误检及“鬼影”现象,对于深度图像分割中前景背景距离过近而造成误检现象都有很好的识别效果。     

基于概率图与视觉显著性的海面目标检测方法

针对基于视觉的传统海面目标检测算法在水面无人艇的自动避碰应用中存在检测精确率、召回率低以及对复杂场景的适应性不足的问题,提出一种基于概率图与视觉显著性的海面目标检测算法。首先利用概率图模型分割出原始图像中的海界限区域与海面孤立目标;然后针对海界限区域子图像特点,设计了一种基于方向抑制的梯度特征,并结合背景先验改进频率调谐显著图,利用特征融合的方法提取海界限区域的潜在目标。实验结果表明,该算法能够有效抑制云、飞鸟、海天线和海杂波的背景干扰。与传统方法相比,提出的方法具有更高的精确率与召回率,且满足无人艇自动避碰实时性的要求。     

A robust salient object detection using edge enhanced global topographical saliency

Multimedia Tools and Applications - Complex salient object detection is the most challenging task in clutter background images. In this prevailing problem, global contrast-based methods are...     

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).     

Salient object detection via multiple saliency weights

Multimedia Tools and Applications - Salient object detection aims to emulate the extraordinary capability of human visual system, which has the ability to find the most visually attractive objects...     

Color-based object segmentation method using artificial neural network

This paper presents a color-based technique for object segmentation in colored digital images. Principally, we make use of some color spaces to segment pixels as either objects of interest or non-objects using artificial neural networks (ANN). This study clearly shows how a novel method for fusion of the existing color spaces produces better results in practice than individual color spaces. The segmented objects include lips, faces, hands, fingers and tree leaves. Using several databases to represent these problems, the ANN was trained on the color of the pixel and its surrounding 8 neighbors to be an object or non-object; in the test mode the trained set was used to segment the 9 pixels in the test image into object or non-object. The feature vector was used for training and testing results from the fusion of different types of color information that came from different color models of the targeted pixel. Several experiments were conducted on different databases and objects to evaluate the proposed method; significant results were recorded, showing the power of expressiveness of color and some texture information to deal with the object segmentation problem.     

Hierarchical salient object detection model using contrast-based saliency and color spatial distribution

Visual saliency is an important cue in human visual system to detect salient objects in natural scenes. It has attracted a lot of research focus in computer vision, and has been widely used in many applications including image retrieval, object recognition, image segmentation, and etc. However, the accuracy of salient object detection model remains a challenge. Accordingly, a hierarchical salient object detection model is presented in this paper. In order to accurately interpret object saliency in image, we propose to investigate distinctive features from a global perspective. Image contrast and color distribution are calculated to generate saliency maps respectively, which are then fused using the principal component analysis. Compared with state-of-the-art models, the proposed model can accurately detect the salient object which conform with the human visual principle. The experimental results from the MSRA database validate the effectiveness of our proposed model.     

Salient object detection via saliency bias and diffusion

Salient object detection aims to identify both spatial locations and scales of the salient object in an image. However, previous saliency detection methods generally fail in detecting the whole objects, especially when the salient objects are actually composed of heterogeneous parts. In this work, we propose a saliency bias and diffusion method to effectively detect the complete spatial support of salient objects. We first introduce a novel saliency-aware feature to bias the objectness detection for saliency detection on a given image and incorporate the saliency clues explicitly in refining the saliency map. Then, we propose a saliency diffusion method to fuse the saliency confidences of different parts from the same object for discovering the whole salient object, which uses the learned visual similarities among object regions to propagate the saliency values across them. Benefiting from such bias and diffusion strategy, the performance of salient object detection is significantly improved, as shown in the comprehensive experimental evaluations on four benchmark data sets, including MSRA-1000, SOD, SED, and THUS-10000.     

移动机器人目标分割及测距方法研究

对于移动机器人单目视觉避障导航问题,研究了室内环境中多障碍物目标图像分割与目标定位。提出一种将HSI彩色图像空间序列分割与Otsu法选取阈值相结合的图像分割方法,并采用基于亮度均值的幂次变换方法改进亮度空间的对比度,从背景环境中分割提取出多个目标所在区域的像素坐标。基于透视投影原理,应用目标定位的几何方法得到目标的空间坐标。该方法在Pioneer-2移动机器人平台上进行了实验,论证了所提出方法的实用性和有效性。     

A fast DGPSO-motion saliency map based moving object detection

Multimedia Tools and Applications - The rapid development in the field of computer vision has encouraged researchers to develop vision systems for moving object detection in embedded surveillance...     

An efficient blind detection watermarking algorithm using range conversion method

Microsystem Technologies - In recent years digital technology plays a vital role in data transmission. Most of the digital content or data exchange happens through the internet. Provision of...     

Salient object detection method using random graph

In this paper, a bottom-up salient object detection method is proposed by modeling image as a random graph. The proposed method starts with portioning input image into superpixels and extracting color and spatial features for each superpixel. Then, a complete graph is constructed by employing superpixels as nodes. A high edge weight is assigned into a pair of superpixels if they have high similarity. Next, a random walk prior on nodes is assumed to generate the probability distribution on edges. On the other hand, a complete directed graph is created that each edge weight represents the probability for transmitting random walker from current node to next node. By considering a threshold and eliminating edges with higher probability than the threshold, a random graph is created to model input image. The inbound degree vector of a random graph is computed to determine the most salient nodes (regions). Finally, a propagation technique is used to form saliency map. Experimental results on two challenging datasets: MSRA10K and SED2 demonstrate the efficiency of the proposed unsupervised RG method in comparison with the state-of-the-art unsupervised methods.     

一种结合显著性检测和超像素分割的遥感信息提取算法研究*

针对目前显著性检测算法在复杂多目标遥感图像中检测能力不足问题,提出一种结合显著性检测和超像素分割的遥感信息提取算法。该算法首先通过Graph-based Visual Saliency(GBVS)方法检测出原始影像中部分显著性较高的区域,然后利用Simple Linear Iterative Clustering(SLIC)方法分割显著区域,并修正显著区域边缘得到训练样本数据,进一步对训练样本进行统计学习,构造显著目标提取的阈值区间,最后实现对整幅超像素图像的显著目标提取。实验结果表明,该算法具有较高的准确率和召回率,能更加有效地检测出遥感图像中的显著目标,比目前主流的显著区域检测算法提取效果更好,可以很好地应用于具有明显显著区域的复杂多目标遥感图像信息提取中。     

基于运动显著性的移动镜头下的运动目标检测

针对移动镜头下的运动目标检测中的背景建模复杂、计算量大等问题,提出一种基于运动显著性的移动镜头下的运动目标检测方法,在避免复杂的背景建模的同时实现准确的运动目标检测。该方法通过模拟人类视觉系统的注意机制,分析相机平动时场景中背景和前景的运动特点,计算视频场景的显著性,实现动态场景中运动目标检测。首先,采用光流法提取目标的运动特征,用二维高斯卷积方法抑制背景的运动纹理;然后采用直方图统计衡量运动特征的全局显著性,根据得到的运动显著图提取前景与背景的颜色信息;最后,结合贝叶斯方法对运动显著图进行处理,得到显著运动目标。通用数据库视频上的实验结果表明,所提方法能够在抑制背景运动噪声的同时,突出并准确地检测出场景中的运动目标。     

帧差累积和减背景相结合的运动对象分割方法

针对低比特率的多媒体视频序列,提出了一种综合利用帧差累积和背景减法来进行运动对象分割的方法。由一种改进的帧差累积方法得到初步的运动对象区域,通过背景减法得到运动对象区域,把由两种方法得到的运动对象区域相结合取得完整准确的结果,二值化后再经过形态学处理和二次扫描填充即可得到运动对象掩模,用原图像的灰度值填充该区域。实验表明,该方法快速,准确,并有一定的应用价值。