Registration and interactive planar segmentation for stereo images of polyhedral scenes

We introduce a two-step iterative segmentation and registration method to find coplanar surfaces among stereo images of a polyhedral environment. The novelties of this paper are: (i) to propose a user-defined initialization easing the image matching and segmentation, (ii) to incorporate color appearance and planar projection information into a Bayesian segmentation scheme, and (iii) to add consistency to the projective transformations related to the polyhedral structure of the scenes. The method utilizes an assisted Bayesian color segmentation scheme. The initial user-assisted segmentation is used to define search regions for planar homography image registration. The two reliable methods cooperate to obtain probabilities for coplanar regions with similar color information that are used to get a new segmentation by means of quadratic Markov measure fields (QMMF). We search for the best regions by iterating both steps: registration and segmentation.     

Evaluation of multiple features for violent scenes detection

Multimedia Tools and Applications - Violent scenes detection (VSD) is a challenging problem because of the heterogeneous content, large variations in video quality, and complex semantic meanings of...     

Projecting registration error for accurate registration of overlapping range images

In this paper, we propose a novel algorithm for the automatic registration of two overlapping range images. Since it is relatively difficult to compare the registration errors of different point matches, we project them onto a virtual image plane for more accurate comparison using the classical pin-hole perspective projection camera model. While the traditional ICP algorithm is more interested in the points in the second image close to the sphere centred at the transformed point, the novel algorithm is more interested in the points in the second image as collinear as possible to the transformed point. The novel algorithm then extracts useful information from both the registration error and projected error histograms for the elimination of false matches without any feature extraction, image segmentation or the requirement of motion estimation from outliers corrupted data and, thus, has an advantage of easy implementation. A comparative study based on real images captured under typical imaging conditions has shown that the novel algorithm produces good registration results.     

应用边缘图像SIFT特征配准印鉴图像

为了准确配准印鉴图像,为高仿真印鉴的真伪识别做好准备,提出利用印鉴边缘图像SIFT（Scale Invariant Feature Transform）特征的相似性和空间关系相结合的配准方法。采用邻域搜索法提取待测印鉴与预留印鉴的二值边缘图像,在印鉴边缘图像中提取SIFT特征,并根据相似性匹配。利用印鉴边缘图像SIFT特征匹配点对的空间关系剔除错误匹配,提高配准效率。利用RANSAC方法估计两印鉴的变换模型。分别配准具有不同形状及印文内容的10组真印鉴图像和10组假印鉴图像。将所得结果与其他两种典型的配准方法作比较。以两印鉴配准后不重合边缘点之间的平均距离评价配准的准确性,以最大距离量化配准后出现的最大差异。实验结果表明,该方法可以准确配准待测印鉴与预留印鉴图像,对印鉴形状、笔画结构无任何限制,配准速度比直接利用印鉴二值图像SIFT特征的配准方法提高一倍。     

RANSAC-based DARCES: a new approach to fast automatic registrationof partially overlapping range images

In this paper, we propose a new method, the RANSAC-based DARCES method (data-aligned rigidity-constrained exhaustive search based on random sample consensus), which can solve the partially overlapping 3D registration problem without any initial estimation. For the noiseless case, the basic algorithm of our method can guarantee that the solution it finds is the true one, and its time complexity can be shown to be relatively low. An extra characteristic is that our method can be used even for the case that there are no local features in the 3D data sets     

MOF-tree: A spatial access method to manipulate multiple overlapping features

In this paper we investigate the manipulation of large sets of 2-dimensional data representing multiple overlapping features (e.g. semantically distinct overlays of a given region), and we present a new access method, the MOF-tree. We perform an analysis with respect to the storage requirements and a time analysis with respect to window query operations involving multiple features (e.g. to verify if a constraint defined on multiple overlays holds or not inside a certain region). We examine both the pointer-based as well as the pointerless MOF-tree representations, using as space complexity measure the number of bits used in main memory and the number of disk pages in secondary storage respectively. In particular, we show that the new structure is space competitive in the average case, both in the pointer version and in the linear version, with respect to multiple instances of a region quadtree and a linear quadtree respectively, where each instance represents a single feature. Concerning the time performance of the new structure, we analyze the class of window (range) queries, posed on the secondary memory implementation. We show that the I/O worst-case time complexity for processing a number of window queries in the given image space, is competitive with respect to multiple instances of a linear quadtree, as confirmed by experimental results. Finally, we show that the MOF-tree can efficiently support spatial join processing in a spatial DBMS.     

Registering multiple range images based on distance metric of surface-to-surface

1 Introduction In recent years, there has been growing interest in the range sensing techniques for building 3D computer models of real-world objects and scenes without requiring hu-mans to manually produce these models using laborious and error-prone CAD-based approaches. Using range sensors, users are able to capture 3D range images of objects from different viewpoints that may be combined to form the final model of the object or scene[1]. These models then may be used for a variety of app…     

Reconstruction of high contrast images for dynamic scenes

High Dynamic Range (HDR) imaging requires one to composite multiple, differently exposed images of a scene in the irradiance domain and perform tone mapping of the generated HDR image for displaying on Low Dynamic Range (LDR) devices. In the case of dynamic scenes, standard techniques may introduce artifacts called ghosts if the scene changes are not accounted for. In this paper, we consider the blind HDR problem for dynamic scenes. We develop a novel bottom-up segmentation algorithm through superpixel grouping which enables us to detect scene changes. We then employ a piecewise patch-based compositing methodology in the gradient domain to directly generate the ghost-free LDR image of the dynamic scene. Being a blind method, the primary advantage of our approach is that we do not assume any knowledge of camera response function and exposure settings while preserving the contrast even in the non-stationary regions of the scene. We compare the results of our approach for both static and dynamic scenes with that of the state-of-the-art techniques.     

Robust artifact-free high dynamic range imaging of dynamic scenes

The irradiance range of the real-world scene is often beyond the capability of digital cameras. Therefore, High Dynamic Range (HDR) images can be generated by fusing images with different exposure of the same scene. However, moving objects pose the most severe problem in the HDR imaging, leading to the annoying ghost artifacts in the fused image. In this paper, we present a novel HDR technique to address the moving objects problem. Since the input low dynamic range (LDR) images captured by a camera act as static linear related backgrounds with moving objects during each individual exposures, we formulate the detection of foreground moving objects as a rank minimization problem. Meanwhile, in order to eliminate the image blurring caused by background slightly change of LDR images, we further rectify the background by employing the irradiances alignment. Experiments on image sequences show that the proposed algorithm performs significant gains in synthesized HDR image quality compare to state-of-the-art methods.

     

一种改进的重叠细胞图像分割研究

在医学细胞图像处理中,经常遇到细胞图像重叠现象。传统的分水岭算法存在严重的过分割现象。使用距离变换的分水岭算法能很好地避免过分割现象。考虑到细胞具有类圆形的特点,使用固定的结构元素进行极限腐蚀容易导致细胞失真。使用交替结构元素对重叠细胞进行腐蚀,能准确地标记出单个细胞种子,同时能避免细胞因腐蚀而失真。最后使用膨胀处理找出重叠细胞的分界线。实验结果表明,算法有效地避免了过分割现象,并提高了分割处理的准确度。     

多核学习纹理特征的立体图像质量评价

为了有效地评价各种失真类型双目立体图像的质量,提出利用多核学习机学习立体图像平面纹理信息和3D映射信息的通用无参考立体图像质量评价IQA方法。该方法首先利用立体匹配模型对左右视图进行处理,获得相应的视差图DM和误差能量图DMEE;对左右视图、视差图和误差能量图进行相位一致性和结构张量变换,获得它们的平坦区和边缘区;分别提取左右视图两个区域纹理特征作为平面信息,提取视差图的纹理特征和误差能量图的统计特征作为3D信息;将所有特征作为多核学习机的输入,利用多核学习的信息融合能力预测待测失真立体图像质量。由于充分利用了立体图像的左右视图、视差图和误差能量图的失真信息,以及多核学习的信息融合能力,该方法具有很好的前景。在LIVE 3D图像质量数据库上的实验表明,该方法与主观质量有较高一致性,与现有的双目立体质量评价方法相比有很大的竞争力。     

Automatic high-dynamic range image generation for dynamic scenes.

Automatic high-dynamic range image generation from low-dynamic range images offers a solution to conventional methods, which require a static scene. The method consists of two modules: a camera-alignment module and a movement detector, which removes the ghosting effects in the HDRI created by moving objects.     

Hand-held 3D scanning based on coarse and fine registration of multiple range images

A hand-held 3D scanning technique is proposed to reconstruct 3D models of real objects. A sequence of range images captured from a hand-held stereo camera is automatically registered to a reference coordinate system. The automated scanning process consists of two states, coarse and fine registration. At the beginning, scanning process starts at the fine registration state. A fast and accurate registration refinement technique is used to align range images in a pair-wise manner. If the refinement technique fails, the process changes to the coarse registration state. A feature-based coarse registration technique is proposed to find correspondences between the last successful frame and the current frame. If the coarse registration successes, the process returns to the fine registration state again. A fast point-to-plane refinement technique is employed to do shape-based registration. After the shape-based alignment, a texture-based refinement technique matches texture features to enhance visual appearance of the reconstructed models. Through a graphic and video display, a human operator adjusts the pose of the camera to change the view of the next acquisition. Experimental results show that 3D models of real objects are reconstructed from sequences of range images.     

Edge-region-based segmentation of range images

In this correspondence, we present a new computationally efficient three-dimensional (3-D) object segmentation technique. The technique is based on the detection of edges in the image. The edges can be classified as belonging to one of the three categories: fold edges, semistep edges (defined here), and secondary edges. The 3-D image is sliced to create equidepth contours (EDCs). Three types of critical points are extracted from the EDCs. A subset of the edge pixels is extracted first using these critical points. The edges are grown from these pixels through the application of some masks proposed in this correspondence. The constraints of the masks can be adjusted depending on the noise present in the image. The total computational effort is small since the masks are applied only over a small neighborhood of critical points (edge regions). Furthermore, the algorithm can be implemented in parallel, as edge growing from different regions can be carried out independently of each other     

3D modeling of multiple-object scenes from sets of images

This paper proposes a new approach for multi-object 3D scene modeling. Scenes with multiple objects are characterized by object occlusions under several views, complex illumination conditions due to multiple reflections and shadows, as well as a variety of object shapes and surface properties. These factors raise huge challenges when attempting to model real 3D multi-object scene by using existing approaches which are designed mainly for single object modeling. The proposed method relies on the initialization provided by a rough 3D model of the scene estimated from the given set of multi-view images. The contributions described in this paper consists of two new methods for identifying and correcting errors in the reconstructed 3D scene. The first approach corrects the location of 3D patches from the scene after detecting the disparity between pairs of their projections into images. The second approach is called shape-from-contours and identifies discrepancies between projections of 3D objects and their corresponding contours, segmented from images. Both unsupervised and supervised segmentations are used to define the contours of objects.     

An efficient spatial access method for spatial images containing multiple non-overlapping features

In this paper we propose and analyze a new spatial access method, namely the S*-tree, for the efficient secondary memory encoding and manipulation of images containing multiple non-overlapping features (i.e., coloured images). The S*-tree is based on a non-straightforward and space efficient extension to coloured images of its precursor, namely the S⁺-tree, which was explicitly designed for binary images. To assess experimentally the qualities of the S*-tree, we test it against the HL-quadtree, a previous spatial access method for coloured images, which is known to be space and time efficient. Our experiments show that the S*-tree reaches up to a 75% of space saving, and performs constantly less I/O accesses than the HL-quadtree in solving classical window queries.     

Image matting for fusion of multi-focus images in dynamic scenes

In this paper, we address the problem of fusing multi-focus images in dynamic scenes. The proposed approach consists of three main steps: first, the focus information of each source image obtained by morphological filtering is used to get the rough segmentation result which is one of the inputs of image matting. Then, image matting technique is applied to obtain the accurate focused region of each source image. Finally, the focused regions are combined together to construct the fused image. Through image matting, the proposed fusion algorithm combines the focus information and the correlations between nearby pixels together, and therefore tends to obtain more accurate fusion result. Experimental results demonstrate the superiority of the proposed method over traditional multi-focus image fusion methods, especially for those images in dynamic scenes.     

Registration of partially overlapping surfaces by rejection of false point correspondences

We present a new algorithm for the registration of three-dimensional partially overlapping surfaces. It is based on an efficient scheme for the rejection of false point correspondences (correspondence outliers) and does not require initial pose estimation or feature extraction. An initial list of corresponding points is first derived using the regional properties of vertices on both surfaces. From these point correspondences, pairs of corresponding rigid triplets are formed. The normal vectors at the vertices of each corresponding triplet are used to compute the candidate rotations. By clustering the candidate rotation axes and candidate rotation angles separately, a large number of false correspondences are eliminated and an approximate rotation is decided, from which an approximate translation is also obtained. Finally, the optimal transformation parameters are determined by further refining the estimated parameters in an iterative manner. Mathematical analysis and experimental results show that the registration process is fast and accurate even when the objects are regularly shaped and contain many regionally similar surface patches.