Bottom-up image detection of water channel slope damages based on superpixel segmentation and support vector machine

The operation of water supply channels is threatened by the occasionally occurred slope damages. Timely detection of their occurrence is critical for the rapid enforcement of mitigation measures. However, current practices based on routine inspection and structural heath monitoring are inefficient, laborious and tend to be biased. As an attempt to address the limitations, this paper proposes a bottom-up image detection approach for slope damages, which includes four steps, i.e. superpixel segmentation, feature handcrafting, superpixel classification based on support vector machine (SVM), and slope damage recognition. The approach employs a bottom-up strategy to infer the upper-level slope condition from the classification results of individual superpixels in the bottom level. Experiments were conducted to demonstrate the effectiveness of the approach. The handcrafted feature “LBP + HSV” was demonstrated to be effective in characterizing the image features of slope damages. An SVM model with “LBP + HSV” as input can reliably identify the slope condition in superpixels. Based on the SVM model, the bottom-up strategy achieved high recognition performance, of which the overall accuracy can be up to 91.7%. The proposed approach has potential to facilitate the early and comprehensive awareness of slope damages along the entire route of water channel by the integration with unmanned aerial vehicles.     

Robust depth map inpainting using superpixels and non-local Gauss–Markov random field prior

Depth maps contain 3D geometrical information of a scene which can be useful in many applications. In this work we address the task of inpainting a depth map afflicted by regions with missing data. Initially, we inpaint depth values missing at random locations as well as due to overlaid text. Subsequently, we propose an approach for filling large holes in the input depth map wherein superpixel division of the corresponding RGB image is also exploited. We use a non-local extension of the classical Gauss–Markov random field model for the completed depth map, so that missing information in the degraded observation can be estimated depending upon self-similarities between non-local patches inside a superpixel based search window. Several experiments performed with disparity maps and real world depth data exhibit the efficacy of the proposed algorithm.     

砂岩显微图像分析方法及其工具实现

图像分析是研究砂岩薄片的重要手段。研究适用于砂岩薄片的图像分析的方法并进行工具实现,在岩石学研究、油气勘探等方面具有重要意义。文中设计开发了砂岩显微图像分析软件系统。首先,提出基于超像素分割和聚类的图像分割方法来分割砂岩薄片显微图像,形成只具有单一矿物成分的超像素;然后,以矿物显微图像作为训练数据,提取颜色和局部等特征参数来训练分类器分类超像素;最后,合并相邻超像素从而形成完整的矿物颗粒,并标定其类别成分。在对方法进行研究的基础上,进行软件设计实现,对砂岩薄片显微图像中的矿物组分和组构特征进行分析。对一些采自西藏的典型砂岩薄片显微图像的分析表明,该方法具有良好的实用价值,但还需要进一步完善和优化。     

一种基于超像素的点追踪方法

摘要：目的：由于当前大多数的追踪算法都是使用目标外观模型和特征进行目标的匹配,在长时间的目标追踪过程中,目标的尺度和形状均会发生变化,再加上计算机视觉误差,都会导致追踪的失误。提出一种高效的目标模型用于提高追踪的效率和成功率。方法：采用分割后提取的目标特征来进行建模表示外观结构,利用图像分割的方法,将被追踪的目标区域分割成多个超像素块,结合SIFT特征,形成词汇本,并计算每个词在词汇本中的权值,作为目标的外观模型。利用外观模型确定目标对象的关键点位置后,通过使用金字塔 Lucas-Kanade 追踪器预测关键点在下一帧图像中的位置,并移动追踪窗口位置。结果：结合点位移的加权计算有效的克服目标尺度和形状变化产生的问题。结论：实验结果表明在目标发生形变或光照变化的情况下,算法也能准确的、实时的追踪到目标。     

Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation

Image segmentation refers to the process to divide an image into meaningful non-overlapping regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. While many segmentation algorithms exist, there are only a few sparse and outdated summarizations available. Thus, in this paper, we aim to provide a comprehensive review of the recent progress in the field. Covering 190 publications, we give an overview of broad segmentation topics including not only the classic unsupervised methods, but also the recent weakly-/semi-supervised methods and the fully-supervised methods. In addition, we review the existing influential datasets and evaluation metrics. We also suggest some design choices and research directions for future research in image segmentation.     

Robust tracking based on local structural cell graph

Structure information has been increasingly incorporated into computer vision, however most trackers have ignored the inner spatial structure of the object. In this paper, we develop a simple yet robust tracking algorithm based on local structural cell graph (LSCG). This approach exploits both partial and spatial information of the target via representing the object with local structural cells (LSCs) and constructing a graph to model the spatial structure between the inner parts of the object. The tracking is formulated as matching LSCG, whose nodes are target parts and edges are the interaction between two parts. Within the Bayesian framework, we achieve object tracking by matching graphs between the reference and candidates. Eventually, the candidate with the highest similarity is the target. In addition, an updating strategy is adopted to help our tracker adapt to the fast time-varying object appearance. Experimental results demonstrate that the proposed method outperforms several state-of-the-art trackers.     

稀疏表示的超像素在线跟踪

目标表观变化的处理是视觉跟踪领域极具挑战性的问题,该文针对这一问题,在粒子滤波框架下提出一种高效的基于超像素的L1跟踪方法(SuperPixel-L1 tracker,SPL1)。首先利用具有结构性信息的中层视觉线索(超像素)构造字典来对目标的表观建模;然后求解由粒子表示的候选目标状态的L1范数最小化,把重构误差最小的候选状态作为跟踪的结果;最后进一步改进了字典的在线更新策略,不论目标发生遮挡与否,字典都被学习更新;为了降低目标发生漂移的可能,更新时保留初始帧的信息。仿真结果验证了SPL1在目标发生长时间遮挡、尺度和光照变化时依然能够稳定地跟踪目标。     

A Noise-Resistant Superpixel Segmentation Algorithm for Hyperspectral Images

The superpixel segmentation has been widely applied in many computer vision and image process applications. In recent years, amount of superpixel segmentation algorithms have been proposed. However, most of the current algorithms are designed for natural images with little noise corrupted. In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise, we propose a noise-resistant superpixel segmentation (NRSS) algorithm in this paper. In the proposed NRSS, the spectral signatures are first transformed into frequency domain to enhance the noise robustness; then the two widely spectral similarity measures-spectral angle mapper (SAM) and spectral information divergence (SID) are combined to enhance the discriminability of the spectral similarity; finally, the superpixels are generated with the proposed frequency-based spectral similarity. Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels. Moreover, the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering (SLIC), where the comparison results prove the superiority of the proposed superpixel segmentation algorithm     

No-reference stereoscopic images quality assessment method based on monocular superpixel visual features and binocular visual features

No-reference quality assessment of images has received considerable attention. However, the accuracy of such assessment remains questionable because of its weak biological basis. In this paper, we propose a novel quality assessment model based on the superpixel index and biological binocular mechanisms. The technical contributions of our model are the introduction of local monocular superpixel features and three global binocular visual features. We utilize monocular superpixel segmentation to extract two types of entropies as the local visual features for accurate quality-aware feature extraction. In addition, natural scene statistics features are extracted from the binocular visual information to complement the local monocular features and quantify the naturalness of the stereoscopic images. Finally, a regression model is learned to evaluate the quality of the stereoscopic images. Experimental results from three popular databases demonstrate that the proposed model has a more reliable performance than earlier models in terms of prediction accuracy and generalizability.