A CCD/CMOS-based imager with integrated focal plane signalprocessing

Using a CCD/CMOS technology, a fully parallel 4×4 focal plane processor, which performs image acquisition, smoothing, and segmentation, has been fabricated and characterized. In this chip, image brightness is converted into signal charge using charge-coupled-device (CCD) imaging techniques. The Gaussian smoothing operation is approximated by the repeated application of a simple nearest-neighbor binomial convolution mask, realizing the first known use of a true two-dimensional charge division and transfer process. The design allows full control of the spatial extent of the smoothing operation, and incorporates segmentation circuits with global variable threshold control at each pixel location to preserve edges in the image. The processed image is read out using a standard CCD clocking scheme     

基于边缘修复的超像素平面立体匹配

视差估计的准确程度直接影响到整个系统的性能,为解决遮挡暴露引起的视差图中物体边缘出现较大面积的空洞问题,对半像素灰度差与Census变换得到初始视差图进行边缘区域修复,以此提高内点所占的比例,从而影响迭代计算的次数;然后基于RANSAC计算超像素视差平面.采用Middlebury提供的测试图,对固定窗口半径和动态规划中惩罚代价的最佳选取范围进行实验测试,结果表明经过边缘修复可以得到相对完整的物体轮廓视差图,并且适当增大Census的权重将有助于检测到细长的目标对象.     

Crowd Segmentation Using both Appearance and Stereo Information

Crowd segmentation is an important issue in video surveillance. With the decrease in their cost, stereo cameras can be used to help develop new algorithms to achieve better accuracy in crowd segmentation. This paper aims to develop a method to explore the depth cues for crowd segmentation in video surveillance. The contributions of this paper are twofold. First, a novel crowd segmentation method closely coupling appearance and stereo information has been developed. Instead of performing disparity calculation as a preprocessing step, stereo information is obtained concurrently with appearance-based crowd segmentation. Second, an object-level disparity algorithm is proposed for object segmentation in surveillance scenarios. Only one disparity value for each hypothetical object greatly reduces the computational complexity and simplifies the segmentation method. Experimental results and quantitative evaluations based on two surveillance scenarios are presented in this paper. The results consistently show the effectiveness of the algorithm in exploring depth cues for crowd segmentation.     

Simultaneous disparity estimation and object segmentation using stochastic models

Stochastic models are proposed for simultaneous disparity estimation and object segmentation in the stereo images. The disparity and segment fields are explicitly modelled as Markov random fields, and are estimated in the energy minimisation process called stochastic diffusion. Some experiments show good segmentation results of foreground objects.     

Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples

This paper provides methodology for fully automated model-based image segmentation. All information necessary to perform image segmentation is automatically derived from a training set that is presented in a form of segmentation examples. The training set is used to construct two models representing the objects--shape model and border appearance model. A two-step approach to image segmentation is reported. In the first step, an approximate location of the object of interest is determined. In the second step, accurate border segmentation is performed. The shape-variant Hough transform method was developed that provides robust object localization automatically. It finds objects of arbitrary shape, rotation, or scaling and can handle object variability. The border appearance model was developed to automatically design cost functions that can be used in the segmentation criteria of edge-based segmentation methods. Our method was tested in five different segmentation tasks that included 489 objects to be segmented. The final segmentation was compared to manually defined borders with good results [rms errors in pixels: 1.2 (cerebellum), 1.1 (corpus callosum), 1.5 (vertebrae), 1.4 (epicardial), and 1.6 (endocardial) borders]. Two major problems of the state-of-the-art edge-based image segmentation algorithms were addressed: strong dependency on a close-to-target initialization, and necessity for manual redesign of segmentation criteria whenever new segmentation problem is encountered.     

Accuracy of automatically determined borders in digital two-dimensional echocardiography using a cardiac phantom

Although two-dimensional echocardiography (2-D echo) is a useful technique for evaluation of global and regional left ventricular function, the main limitation is the inability to easily extract reliable and accurate quantitative information throughout all phases of the cardiac cycle. We sought to develop suitable automated techniques for the objective determination of endocardial and epicardial borders in two-dimensional echocardiographic images. To test algorithms for the automatic detection of myocardial borders we constructed a cardiac ultrasound phantom consisting of 16 echogenic annuli of known dimensions embedded in a material of low echogenicity which allowed imaging without partial volume effects. An algorithm based on Gaussian filtering followed by a difference gradient operator was applied to detect edges in the 2-D echo images of these annuli. The radii of the automatically determined inner borders were within 0.44 mm root meansquared error over a range of 15-25 mm true radius. This lower boundary for the error in our approach to automatic placement of myocardial borders in 2-D echocardiograms suggests the potential to extract more information concerning left ventricular function than is available with current techniques.     

应用分层MRF/GRF模型的立体图像视差估计及分割

视差估计与分割是立体图像编码及立体视觉匹配的核心问题,本文提出一种基于分层MRF/GRF模型和交叠块匹配(HMOM)视差估计算法以及结合主动轮廓模型的视差分割提取算法.该混合视差估计方法,可得到光滑准确,且具有清晰边缘的视差场;并便于用主动轮廓模型提取感兴趣对象(OOI)的视差轮廓.与通常的变尺寸块匹配(VSBM)相比,本算法得到的视差补偿图像的峰值信噪比可提高2.5dB左右.本文得到的视差场及对应的轮廓可进一步用于立体图像编码以及视频对象分割.     

基于视差和条纹调制度分析的多物体场景分割

噪声是影响图像分割的重要因素,文中提出了一种能够在含噪声的真实场景中准确提取出多个物体区域的分割方案。利用基于正弦条纹投影的双目结构光系统,得到包含目标物体的相位图和视差图。将视差图映射到U-视差图中,利用物体和噪声区域在该视差空间的不同形态特征,采用闭合区域检测算法初步得到各个物体的分割区域,并结合条纹调制度阈值分析法进一步去除阴影区域的噪声,最终得到精确的分割结果。客观评价的数据分析表明,文中提出的分割算法,不仅对噪声的鲁棒性较好,还可以有效地将物体与水平支撑面分割开,在不同场景下具有计算复杂度低,抗干扰能力强的优势,分割准确率均在90%以上,最高可达到99.2%,平均运行时间为27 ms。     

Superpixels extracted via region fusion with boundary constraint

In this paper, we present an accurate superpixel algorithm by region fusion with boundary constraint (RFBC). Superpixels with regular shape and high boundary adherence can be generated in weak boundary and complex texture regions through our algorithm. RFBC includes two steps which are initial segmentation and region fusion respectively. In initial segmentation, broken Canny edges are connected through edge closing algorithm. Subsequently, the closed Canny edges and SLIC superpixel edges are combined together to form the incipient superpixels. In region fusion, gray Gaussian distribution and adjacent relation are used as priori to compute the degree of similarity across incipient superpixels in GBP algorithm. For concreteness, the information of similarity is propagated between regions and the most similar regions are fused, which are accomplished alternatingly to preserve accurate boundaries. Extensive experiments on the Berkeley segmentation benchmark show that the proposed algorithm outperforms the most state-of-the-art algorithms.     

Moving video object segmentation using statistical hypothesistesting

Object-based coding is one of the distinct features of the MPEG-4 standard. Hence, video object segmentation is an indispensable process for MPEG-4 coding. A method of localising moving objects for video object segmentation is introduced which utilises a statistical hypothesis test without the need for true statistical parameters to be known a priori     

Image segmentation using data-modulated nonlinear diffusion

Effective object segmentation is achieved by applying a novel data-modulated nonlinear diffusion technique. The advantages of this strategy are a considerable smoothing of the detail of the scene image within the boundaries of the object while inhibiting the diffusion across the boundaries, as well as preserving and even enhancing the object borders     

Integrated method for boundary delineation of agricultural fieldsin multispectral satellite images

Most agricultural statistics are calculated per field, and it is well known that classification procedures for homogeneous objects produce better results than per-pixel classification. In this study, a multispectral segmentation method for automated delineation of agricultural field boundaries in remotely sensed images is presented. Edge information from a gradient edge detector is integrated with a segmentation algorithm. The multispectral edge detector uses all available multispectral information by adding the magnitudes and directions of edges derived from edge detection in single bands. The addition is weighted by edge direction, to remove "noise" and to enhance the major direction. The resulting edge from the edge detection algorithm is combined with a segmentation method based on a simple ISODATA algorithm, where the initial centroids are decided by the distances to the edges from the edge detection step. From this procedure, the number of regions will most likely exceed the actual number of fields in the image and merging of regions is performed. By calculating the mean and covariance matrix for pixels of neighboring regions, regions with a high generalized likelihood-ratio test quantity will be merged. In this way, information from several spectral bands (and/or different dates) can be used for delineating field borders with different characteristics. The introduction of the ISODATA classifier compared with a previously used region growing procedure improves the output. Some results are compared with manually extracted field boundaries     

Stereo matching based on multi-direction polynomial model

This paper presents a segmentation based stereo matching algorithm. For the purposes of both preserving the shape of object surfaces and being robust to under segmentations, we introduce a new scene formulation where the reference image is divided into overlapping lines. The disparity value and the index of pixels on lines are modeled by polynomial functions. Polynomial functions are propagated among lines to obtain smooth surfaces via solving energy minimizing problems. Finally, the disparity of pixels is estimated from the disparity fields provided by lines. Because lines in multiple directions implicitly segment different objects in an under segmentation region, our method is robust for under segmented regions where it is usually difficult for conventional region based methods to produce satisfactory results. Experimental results demonstrate that the proposed method has an outstanding performance compared with the current state-of-the-art methods. The scene representation method in this work is also a powerful approach to surface based scene representations.     

基于分水岭分割和尺度不变特征点的多目标全自主跟踪算法

该文针对多目标的鲁棒跟踪问题,设计了一种基于图像分水岭分割和尺度不变特征变换(SIFT)特征点的多目标全自主跟踪算法。为规避图像平坦区域,提出在原图上叠加规则坡度图的思想,并在浮点域进行一定尺度高斯模糊处理,将区域极小值点作为种子点完成分水岭分割,并将极值点作为目标特征点,通过前后帧分水岭映射生成特征点短时轨迹,自动检测运动目标。之后依据目标所处状态(是否发生遮挡)和分水岭分割图建立、更新目标SIFT特征池,结合分水岭映射、SIFT特征池匹配完成对目标的鲁棒跟踪。实验结果表明,该算法能有效完成视频中多目标的持续跟踪,并对目标遮挡有较好的鲁棒性。     

User assisted disparity remapping for stereo images

This study concentrates on user assisted disparity remapping for stereo image footage, i.e. the disparity of an object of interest is altered while leaving the remaining scene unattended. This application is useful in the sense that it provides a method for emphasizing/de-emphasizing an object on the scene by adjusting its depth with respect to the camera. The proposed technique can also be used as a post-processing step for retargeting stereoscopic footage on different display sizes and resolutions. The proposed technique involves an MRF-based energy minimization step for interactive stereo image segmentation, for which user assistance on only one of the stereo pairs is required for determining the location of stereo object pair. A key contribution of the proposed study is elimination of dense disparity estimation step from the pipeline. This step is realized through a sparse feature matching technique between the stereo pairs. Moreover, by the help of the proposed technique, novel disparity adjusted views are synthesized using the produced stereo object segments and background information for the images. Qualitative and quantitative evaluation of the generated segments and the disparity adjusted images prove the functionality and superiority of the proposed technique.     

Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions

This paper describes a dense stereo matching algorithm for epipolar rectified images. The method applies colour segmentation on the reference image. Our basic assumptions are that disparity varies smoothly inside a segment, while disparity boundaries coincide with the segment borders. The use of these assumptions makes the algorithm capable of handling large untextured regions, estimating precise depth boundaries and propagating disparity information to occluded regions, which are challenging tasks for conventional stereo methods. We model disparity inside a segment by a planar equation. Initial disparity segments are clustered to form a set of disparity layers, which are planar surfaces that are likely to occur in the scene. Assignments of segments to disparity layers are then derived by minimization of a global cost function. This cost function is based on the observation that occlusions cannot be dealt with in the domain of segments. Therefore, we propose a novel cost function that is defined on two levels, one representing the segments and the other corresponding to pixels. The basic idea is that a pixel has to be assigned to the same disparity layer as its segment, but can as well be occluded. The cost function is then effectively minimized via graph-cuts. In the experimental results, we show that our method produces good-quality results, especially in regions of low texture and close to disparity boundaries. Results obtained for the Middlebury test set indicate that the proposed method is able to compete with the best-performing state-of-the-art algorithms.     

基于自适应高斯混合模型的铁轨异物入侵检测研究

针对复杂铁路环境下异物入侵动态检测抗扰能力弱和快速性差等问题,本文提出了 一种基于自适应高斯混合模型(adaptive Gaussian mixture model,AGMM)的铁轨异物入侵检测方法。通过分析铁路场景发生复 合抖动时存在随机性的特点,首先对输入的铁路视频进行抖动检测,然后引入仿射变换与中 值滤波,对视频序列中存在抖动的图像进行处理。再采用逐帧迭代填充的方法,对去抖后图 像出现的黑边进行填充,得到无抖动和无黑边的铁路视频帧。最后,在现有高斯混合模型的 基础上,设计了自适应选择高斯分布个数和学习率,利用改进后的高斯混合模型实现复杂铁 路视频的背景建模,并由此提高前景目标的检测速度。实验结果表明,本文方法在铁路视频 存在抖动的情形下,轨道异物入侵目标检测的精度是原有的2.6倍, 检测速度是2.8倍,能提高目标检测的抗干扰性和快速性。     

Inertial snake for contour detection in ultrasonography images

Snakes, or active contour models are used extensively for image segmentation in varied fields. However, some major challenges restrict their use in many fields. The authors propose a new inertial snake model, that introduces an inertial effect of the control points into the snake framework. The proposed inertial force along with the first- and second-order continuity forces controls the spline motion through the concavities and also against weak edge forces. This smart force field, added to the inertial energy framework, posses the ability to adaptively reduce its effect near the true edges, so that the energy minimising spline converges into the edges. A greedy snake has been used for computation of the energy minimising spline. The algorithm has been tested on phantoms and ultrasound images as well. It is shown in the results that the proposed algorithm classifies the object from the background class in most of the images perfectly. Ultrasound images of a lower limb artery of an adult woman have been tested with this algorithm, and also extended for motion tracking.