Local graph regularized coding for salient object detection

Subspace segmentation based salient object detection has received increasing interests in recent years. To preserve the locality and similarity of regions, a grouping effect of representation is introduced to segment the salient object and background in subspace. Then a new saliency map is calculated by incorporating this local graph regularizer into coding, which explicitly explores the data self-representation model and thus locate more accurate salient regions. Moreover, a heuristic object-based dictionary from background superpixels is obtained in border set removing the image regions within the potential object regions. Experimental results on four large benchmark databases demonstrate that the proposed method performs favorably against eight recent state-of-the-art methods in terms of three evaluation criterions, with a reduction of MAE by 19.8% than GR and 29.3% than CB in the two SED datasets, respectively. Meanwhile, our method also runs faster than the comparative detection approaches.     

基于光纤声光调制的轮廓检测技术研究

为实现对工件表面微观形貌测量系统要求远距离传输、实时在线及非接触测量,同时达到高精度、高分辨力、高信噪比测量,提出基于声光调制的光纤外差法轮廓检测技术,采用外差法可有效抑制随机噪声,提高信噪比,通过原理分析建立数学模型;系统设计画出工件表面粗糙度测量框图;实验测量得出被测工件表面粗糙度,分辨力达到1 nm;讨论了不确定度来源及控制方法,测量精度为1。实验结果表明:外差法、声光调制及光纤测试技术可有效降低噪声、提高分辨力和测量精度。     

Infrared object detection using global and local cues based on LARK

Object detection has become a challenging problem in computer vision. Locally Adaptive Regression Kernel (LARK) based detection methods are able to produce visually pleasing results without any training. We in this paper present an effective object detection method by exploring global and local cues based on LARK features. First, we encode the local context similarity by exploiting region Structural LARK (SLARK) features, which measure the likeness of a pixel to its surroundings in the query image and the test image. Second, a global constraint based on SLARK features via Heat equation is learned to detect similar features in the test image. Results from matrix cosine similarity are computed to estimate similar regions between these computed features. A compactness score is provided to refine these regions. Next, we detect the location of objects in the test image using non-maxima suppression. We show in experiments that the proposed method significantly outperforms other methods on the infrared image datasets, localizing the objects in the test images effectively.     

Quantum information procession with fermions based on charge detection

This paper proposes a fermionic linear optical scheme for the teleportation and entanglement concentration via entanglement swapping based on charge detection. It also proves that this method is useful in generating entangled states such as GHZ states, W states, and cluster states by using fermionic polarizing beam splitters and single spin rotations assisted by a parity check on the fermionic qubits. This scheme is nearly deterministic (i.e., with 100\% successful probability) and does not need the joint Bell state measurement required in the previous schemes.     

Robust object detection based on deformable part model and improved scale invariant feature transform

We propose an approach to improve the detection results of a generic offline trained detector on frames from a specific video. For two consecutive frames of a video with the object, deformable part model (DPM) detection is performed to get the original detections. Then the image patches corresponding to the detected root box and part boxes were respectively obtained. Thirdly, improved scale invariant feature transform features (SIFT) from those image patches were extracted and matched with the SIFT features by KD-Tree. K-means clustering the angle and scale of matched keypoints to filter out the uncorrected matches and further remove false matches by RANSAC algorithm. Finally, the SIFT_DPM detection result from the matches between image patches of continuous frames was obtained. We focus on methods with high precision detection results since it is necessitated in real application. Extensive experiments with state-of-the-art detector demonstrate the efficacy of our approach.     

基于配置关联关系的信息系统误配置检测技术

随着软件系统变得更加复杂和可配置,由于错误配置而导致的故障正成为一个关键问题。 这种故障通常会带来较为严重的经济损失、安全隐患和功能故障。 此外,对于这种故障的诊断和修复需要跨越软件本身及其运行环境进行分析,使得其困难且昂贵。本文基于配置项之间隐含的关联关系及其运行环境,设计了基于信息系统配置关联关系的配置错误检测系统技术,利用给定的大量样本配置训练,形成配置项关联关系与检测规则,通过发掘信息系统各组件配置项之间的关联关系并利用这种关联进行配置项交叉检验,能够有效检测系统的错误配置。通过模拟测试表明,本方法的错误配置检出率达到了90%以上,在大型企业中具有广泛的应用前景。     

一种基于边缘能量图的物体检测方法

物体的变形、边缘中断等因素通常对物体检测的结果影响很大。为了减小这些因素的影响,提出一种新的基于边缘能量图的物体检测方法。边缘能量图在一定程度上反映了物体的边缘出现的概率,便于计算并具有较好的鲁棒性。计算出图像的边缘图像,在此基础上分别计算出模型和测试图像的边缘能量图。基于多分辨率的滑动窗口,在图像中找出测试图像边缘能量图与模型边缘能量图最相似的区域。基于边缘能量图快速得到目标物体的轮廓。实验结果显示,该方法使用较快的计算速度得到了相对较高的检测率。     

改进三帧差分算法在移动物体检测中的应用

移动物体监控系统利用检测算法识别监控区域的移动物体,并进行实时异常信息存储,检测算法中的传统三帧差分法中的阈值是固定的,因此重叠部分无法准确检测出来,存在空洞现象,可能发生误判。针对这些问题,对已有的三帧差分法进行改进,结合图像边缘提取和自适应的迭代阈值计算方法来提高移动物体检测的准确性,并对异常信息进行选择性存储,由实验结果可知,应用平台采用改进后的移动物体检测算法,较好的提高了移动物体检测的灵敏度,增强了检测系统的实时性和准确性,若仅存储异常信息,可节省视频存储空间,并在定位异常动态信息时节省查找时间。     

A robust level set method based on local statistical information for noisy image segmentation

The paper presents an improved local region-based active contour model for image segmentation, which is robust to noise. A data fitting energy functional is defined in terms of curves and the energy terms which are based on the differences between the local average intensity and the global intensity means. Then the energy is incorporated into a level set variational formulation, from which a curve evolution equation is derived for energy minimization. And then the level set function is regularized by Gaussian filter to keep smooth and eliminate the re-initialization. By using the local statistical information, the proposed model can handle with noisy images. The proposed model is first presented as a two-phase level set formulation and then extended to a multi-phase one. Experimental results show desirable performances of the proposed model for both noisy synthetic and real images, especially with high level noise.     

License plate detection using gradient information and cascade detectors

In this paper, we propose a license plate detection method based on the gradient information and a cascade detection framework. The proposed method consists of three main modules: image preprocessing, license plate detection and license plate confirmation. Considering the diversities of Chinese license plate, we first get the gradient images to unify plate forms through image preprocessing. The license plate will be detected roughly by using the cascade AdaBoost classifier in the next step. Finally, several heuristic judgment strategies and a voting-based method are used to verify the candidate license plates. The effectiveness of our method is validated with an experimental study in license plate detection.     

结合区域提取和改进卷积神经网络的水下小目标检测

针对水下小目标信息量有限而难以提取有效特征导致的检测性能不佳问题,提出了一种结合区域提取和融合Hu矩特征的改进卷积神经网络水下小目标检测方法。该方法包含区域提取和分类两个步骤。首先以马尔可夫随机场分割算法为基础进行区域提取,对潜在目标定位的同时降低伪目标对后续分类的干扰;然后提取潜在目标区域的Hu矩特征并融入卷积神经网络,形成一种形状特征表征能力更强的改进卷积神经网络用于分类。声呐实测数据处理结果表明,该方法可以有效提升对水下小目标的发现概率和正确报警率,与其他目标检测方法相比,该方法具有更好的检测性能和泛化性。     

基于图像融合的动态轮廓线跟踪新方法

红外与可见光传感器是目标跟踪识别系统中常用的两种传感器,对这两种传感器图像进行融合能有效提高系统跟踪检测的准确性。将动态轮廓线模型与图像融合结合,在特征搜索过程中利用特征点准确地完成了图像配准,同时使用了一种新的特征级融合方法,将两种图像中目标轮廓的B样条曲线控制点进行实时微分耦合。这种耦合将Curwen提出的微分耦合机制作了改进,利用图像配准把刚性硬模板改变为实时的变换模板并推导了融合后动态轮廓线的新的动力学方程。这种融合利用了红外图像目标轮廓信息约束可见光图像中动态轮廓线的收敛形状,有效地提高了可见光图像目标跟踪的准确性。对运动人手序列图像的对比跟踪实验表明,这种融合使得可见光图像中动态轮廓线平均跟踪误差减小了60．25％。     

Precise perimeter measurement for 3D object with a binocular stereo vision measurement system

A novel measurement scheme for a three dimensional (3D) object's surface boundary perimeter is proposed. This scheme consists of three steps. First, a binocular stereo vision measurement system with two CCD cameras is devised to obtain the two images of a detected object's 3D surface boundary. Second, two B-spline active contours are applied to converge to the object's contour edges accurately in the two CCD images to perform the stereo matching. Finally, for the reconstructed 3D active contour, its true contour length is computed as the detected object's true boundary perimeter. An experiment on a bent surface's perimeter measurement indicates that this scheme's measurement repetition error decreases to 0.6%.     

Saliency detection via two-directional 2DPCA analysis of image patches

Salient object detection is an important and challenging problem in computer vision. In this paper, we present a model of salient region detection based on the fusion of contrast and distribution, computed by two-directional 2DPCA analysis of image patches under the combination of RGB space, LAB space and YCbCr space. First, non-overlap patches of three layers from the image are obtained in the three color spaces respectively and stacked for the combination of the three sapces in a single layer. For every layer, two-directional, two-dimensional PCA are utilized to realize automatic selection of effective features, then based on the high contrast and compact character of salient object, contrast values and distribution values of image patches are fused to get the saliency map. Finally, three saliency maps for three layers are combined to detect salient object. The experimental results on a publicly available database show that the proposed algorithm performs well and are in line with the human eye observation results.     

基于序列图像的运动目标检测

针对动态环境下运动目标检测中噪声多、目标检测不完整等情况,提出了一种基于金字塔多分辨率模型的运动目标检测方法,在低分辨率下获取目标的区域,在高分辨率下获取目标的细节。对于复杂的环境,还提出了运用高低双阈值替代传统的单阈值进行图像差分运算的方案,阈值可以根据图像自动分析得到。该方法首先将当前帧和背景帧进行尺度变换,得到不同分辨率下的图像组,然后在不同尺度下得到高低阈值差分图像,最后从高层向低层进行有效融合,得到噪声少的完整目标图像。实验表明,该方法提取运动目标的精度比较高,单目标达到0.802,多目标达到0.615,尤其是在复杂的动态环境下,优势比较明显。     

基于Fisher信息理论的突变检测新方法

自然界许多复杂系统的动力学结构因其受外强迫的作用, 常常会发生与人类生活密切相关的突变. 对突变做出准确预测并采取相应对策的有效途径就是检测. Fisher信息(FI)是一种能够敏锐捕捉并刻画系统变量的概率密度分布发生微小变化的量; 而当系统的动力学结构发生突变时, 来自突变前后两种不同动力学结构的数据其概率密度分布肯定会发生不同程度的改变. 基于此,本文将它用于检测和识别系统动力学结构所发生的突变, 以一种全新的思路来解决突变检测问题. 首先通过理想数据序列展示了使用FI来表征各种不同类型理想信号突变的能力. 进而应用于中国气象局国家气象信息中心“西北地区地面气候资料日值数据集”兰州站1960–2008年日平均气温资料的实际检测中, 结果表明与已有历史记载相符合, 进一步验证了该方法的有效性和实用性. 关键词： Fisher信息 动力学结构 突变检测 概率密度分布     

基于模糊集理论的运动目标检测

针对复杂自然环境下运动目标检测中噪声多、目标检测不完整等问题,提出一种改进的基于模糊集理论的解决方法。使用金字塔多分辨率模型进行背景差分获取初步的前景掩膜,将当前帧的颜色、时间、空间、位置4种特征用模糊集表示,形成模糊向量集合簇,用模糊数学理论结合这4个向量的特征,得到每个像素点对前景的隶属度,从而检测运动目标。该方法不仅能有效地检测较完整运动目标,也可以克服自然环境下微小运动背景的影响。实验结果显示:该方法前景的识别率为0.717 4,错误率为0.011 8,能适应自然环境下动态背景的影响。     

一种改进的基于混合高斯模型的运动目标检测方法

混合高斯模型方法被广泛应用于运动目标检测,但是现有的混合高斯模型方法在应对噪声和光照突变时效果不佳,其运动目标检测的效果会受到严重影响。为了解决上述问题,提出了一种结合边缘混合高斯模型方法以及改进的基于邻域差方法的综合方法。该方法充分利用了边缘图像对于噪声和光照突变不敏感,以及邻域差方法可以去除部分噪声的特点,对图像序列进行综合处理。实验结果证明,该方法可以提升运动目标的检测率,降低误警率,可以更有效地应对噪声和光照突变的干扰,从而具有更准确的目标检测效果。     

A robust infrared dim target detection method based on template filtering and saliency extraction

Dim target detection in infrared image with complex background and low signal-clutter ratio (SCR) is a significant and difficult task in the infrared target tracking system. A robust infrared dim target detection method based on template filtering and saliency extraction is proposed in this paper. The weighted gray map is obtained from the infrared image to highlight the target which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then calculated by phase spectrum of Fourier Transform, so that the dim target detection could be converted to salient region extraction. The potential targets are finally extracted by combining the two maps. Moreover, position discrimination between targets in the two maps is used to exclude the false alarms and extract the targets. Experimental results on measured images indicate that our method is feasible, adaptable and robust in different backgrounds. The ROC (Receiver Operating Characteristic) curves obtained from the simulated images demonstrate the proposed method outperforms some existing typical methods in both detection rate and false alarm rate, for target detection with low SCR.     

基于多尺度区域对比的显著目标识别

为了对图像中的显著目标进行更精确的识别,提出一种新的基于多尺度区域对比的视觉显著性计算模型。首先基于多尺度思想将图像分别分割为不同数目的超像素,对超像素内的像素颜色值取平均以生成抽象化图像;然后根据显著特征的稀少性及显著特征的聚集性,计算单一尺度下超像素颜色特征的显著性值;最后通过取各尺度超像素显著度的平均值来融合多尺度显著图,得到最终的视觉显著图。实验表明,以MSRA图库中的1 000张随机自然图片为例,该模型较现有较好的区域对比模型,显著目标识别的精确率提高了14.8%,F-Measure值提高了9.2%。与现有的算法相比,该模型提高了算法对显著目标大小的适应性,减少了背景对显著目标识别的干扰,具有更好的一致性,能更好地识别显著目标。