Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps.This work uses the consistency check method to find an accurate depth map for identifying occluded pixels.The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation.The improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction algorithms.The experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and runtime.We observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain cumbersome.Considering this gain,we have created our dataset with occlu-sion using the structured lighting technique.The proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing coefficients.The experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.

Generate Reservoir Depths Mapping by Using Digital Elevation Model: A Case Study of Mosul Dam Lake, Northern Iraq

Not many bathymetric maps are available for many lakes and reservoirs in developing countries. Usually the bathymetric mapping requires investment in expensive equipment and fieldwork, both of which are not accessible in these countries. This work demonstrates the ability to develop bathymetric map of Mosul Lake by using a digital elevation model (DEM). The depths model of the lake was designed through the use of three main stages;a coastline extraction, dataset interpolation and a triangular irregular network model. The normalized difference water index (NDWI) was used for automatic delineation of the lake coastline from satellite images. The ordinary kriging interpolation with a stable model was used to interpolate the water depths dataset. Finally a triangulated irregular network (TIN) model was used to visualize the resulting interpolation model. Calculated values of area and volume of a TIN model during 2011 were compared with values of supposed initial operation of the reservoir. The differences of water volume storage between these stages at 321 m water level was about 0.81 × 109 m3, where the lake lost around 10% of storage value. Also the results of depths lake model show that the change in water storage between March and July 2011 was about 3.08 × 109 m3.

The Square Mapping Graph of M2（Zp;s）

In this paper,for an arbitrary prime p,the square mapping graph of M2（Zp;s） is investigated and the formula of the decomposition of the square mapping graph of M2（Zp;s） is established.

DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.

An integrated method of selecting environmental covariates for predictive soil depth mapping

Environmental covariates are the basis of predictive soil mapping.Their selection determines the performance of soil mapping to a great extent,especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high.In this study,we proposed an integrated method to select environmental covariates for predictive soil depth mapping.First,candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge.Second,three conventional methods(Pearson correlation analysis(PsCA),generalized additive models(GAMs),and Random Forest(RF))were used to generate optimal combinations of environmental covariates.Finally,three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate.We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China.A total of 129 soil sampling sites were collected using a representative sampling strategy,and RF and support vector machine(SVM)models were used to map soil depth.The results showed that compared to the set of environmental covariates selected by the three conventional selection methods,the set of environmental covariates selected by the proposed method achieved higher mapping accuracy.The combination from the proposed method obtained a root mean square error(RMSE)of 11.88 cm,which was 2.25–7.64 cm lower than the other methods,and an R^2 value of 0.76,which was 0.08–0.26 higher than the other methods.The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties.

Enhanced Autonomous Exploration and Mapping of an Unknown Environment with the Fusion of Dual RGB-D Sensors

The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.

NON-WANDERING SET OF A CONTINUOUS GRAPH MAP

The non-wandering set Ω(f) for a graph map f is investigated. It is showed that Ω(f) is contained in the closure of the set ER(f) of eventually recurrent points of f and ω-limit set ω(Ω(f)) of Ω(f) is contained in the closure of the set R(f) of recurrent points of f.

Technique of Embedding Depth Maps into 2D Images

This paper proposes a new technique that is used to embed depth maps into corresponding 2-dimensional （2D） images. Since a 2D image and its depth map are integrated into one type of image format, they can be treated as if they were one 2D image. Thereby, it can reduce the amount of data in 3D images by half and simplify the processes for sending them through networks because the synchronization between images for the left and right eyes becomes unnecessary. We embed depth maps in the quantized discrete cosine transform （DCT） data of 2D images. The key to this technique is whether the depth maps could be embedded into 2D images without perceivably deteriorating their quality. We try to reduce their deterioration by compressing the depth map data by using the differences from the next pixel to the left. We assume that there is only one non-zero pixel at most on one horizontal line in the DCT block because the depth map values change abruptly. We conduct an experiment to evaluate the quality of the 2D images embedded with depth maps and find that satisfactory quality could be achieved.

Construct Validation by Hierarchical Bayesian Concept Maps: An Application to the Transaction Cost Economics Theory of the Firm

A concept map is a diagram depicting relationships among concepts which is used as a knowledge representation tool in many knowledge domains. In this paper, we build on the modeling framework of Hui et al. (2008) in order to develop a concept map suitable for testing the empirical evidence of theories. We identify a theory by a set of core tenets each asserting that one set of independent variables affects one dependent variable, moreover every variable can have several operational definitions. Data consist of a selected sample of scientific articles from the empirical literature on the theory under investigation. Our “tenet map” features a number of complexities more than the original version. First the links are two-layer: first-layer links connect variables which are related in the test of the theory at issue;second-layer links represent connections which are found statistically significant. Besides, either layer matrix of link-formation probabilities is block-symmetric. In addition to a form of censoring which resembles the Hui et al. pruning step, observed maps are subject to a further censoring related to second-layer links. Still, we perform a full Bayesian analysis instead of adopting the empirical Bayes approach. Lastly, we develop a three-stage model which accounts for dependence either of data or of parameters. The investigation of the empirical support and consensus degree of new economic theories of the firm motivated the proposed methodology. In this paper, the Transaction Cost Economics view is tested by a tenet map analysis. Both the two-stage and the multilevel models identify the same tenets as the most corroborated by empirical evidence though the latter provides a more comprehensive and complex insight of relationships between constructs.

3DV quality model based depth maps for view synthesis in FTV system

Depth maps are used for synthesis virtual view in free-viewpoint television （FTV） systems. When depth maps are derived using existing depth estimation methods, the depth distortions will cause undesirable artifacts in the synthesized views. To solve this problem, a 3D video quality model base depth maps （D-3DV） for virtual view synthesis and depth map coding in the FTV applications is proposed. First, the relationships between distortions in coded depth map and rendered view are derived. Then, a precisely 3DV quality model based depth characteristics is develop for the synthesized virtual views. Finally, based on D-3DV model, a multilateral filtering is applied as a pre-processed filter to reduce rendering artifacts. The experimental results evaluated by objective and subjective methods indicate that the proposed D-3DV model can reduce bit-rate of depth coding and achieve better rendering quality.

Depth Based View Synthesis Using Graph Cuts for 3DTV

In three-dimensional television (3DTV), an interactive free viewpoint selection application has received more attention so far. This paper presents a novel method that synthesizes a free-viewpoint based on multiple textures and depth maps in multi-view camera configuration. This method solves the cracks and holes problem due to sampling rate by performing an inverse warping to retrieve texture images. This step allows a simple and accurate re-sampling of synthetic pixels. To enforce the spatial consistency of color and remove the pixels wrapped incorrectly because of inaccuracy depth maps, we propose some processing steps. The warped depth and warped texture images are used to classify pixels as stable, unstable and disoccluded pixels. The stable pixels are used to create an initial new view by weighted interpolation. To refine the new view, Graph cuts are used to select the best candidates for each unstable pixel. Finally, the remaining disoccluded regions are filled by our inpainting method based on depth information and texture neighboring pixel values. Our experiment on several multi-view data sets is encouraging in both subjective and objective results. Furthermore, our proposal can flexibly use more than two views in multi-view system to create a new view with higher quality.

A General Method of Researching the N-Ordered Fixed Point on a Metric Space with a Graph

In this paper, we propose a new perspective to discuss the N-order fixed point theory of set-valued and single-valued mappings. There are two aspects in our work: we first define a product metric space with a graph for the single-valued mapping whose conversion makes the results and proofs concise and straightforward, and then we propose an SG-contraction definition for set-valued mapping which is more general than some recent contraction’s definition. The results obtained in this paper extend and unify some recent results of other authors. Our method to discuss the N-order fixed point unifies N-order fixed point theory of set-valued and single-valued mappings.

A note on equicontinuous maps of graph space

Let G be a graph and f: G→ G be a continuous map with at least one periodic point. Using the quote space method, the paper addresses that f is an equicontinuous map if and only if one of the following End(G)+2k+1 conditions holds: 1) {f jm(End(G)+2k)!}∞j=1 is uniformly convergent, in which m=1,2,…, End(G)+2k; and 2) There is a positive integer n esuring that {f jn}∞j=1 is uniformly convergent.

基于图像关键帧的Visual-Depth Map建立方法

对于室内视觉定位系统,需要在离线阶段建立Visual Map数据库用来存储图像信息,在线阶段用户通过与Visual Map数据库进行比对来完成用户位置的估计.离线阶段建立的数据库可以采用逐点采样或视频流采样的方式.但是无论何种方式,考虑到数据库中图像信息的相似性,传统方式建立的数据库中存储图像有较多冗余,导致增加了在线阶段的定位时间开销.因此,本文根据Visual Map中的相邻图像间的相似性,提出了一种基于图像关键帧的Visual-Depth Map建立方法,有效地减少了离线数据库的规模.在离线阶段,本文使用Kinect传感器同时获得图像信息和深度信息;然后,通过基于图像相似度的图像关键帧算法对原始图像序列进行筛选,得到关键帧序列,从而实现Visual-Depth Map的建立.在线阶段,用户可以直接输入查询图像与Visual-Depth Map中的图像序列进行检索匹配,找到相似度较高的匹配图像,再通过EPnP算法进行2D-3D的位姿估计,完成用户位置的计算.实验证明,本文所提方法可以在保证较高定位精度的前提下,有效减少离线数据库规模,降低在线阶段的定位时间开销.

Depth map软件在网师园空间分析中的应用

空间句法是一种通过对包括建筑、聚落、城市以及景观在内的人居空间结构的量化描述来研究空间组织的理论和方法。作为空间句法的专用分析软件,近年来Depth map在空间结构分析领域得到了越来越广泛的应用。以江南园林网师园为例,利用Depth map软件建立模型,对其空间的可视和可行2个层面分别进行连接度、整合度和深度值的空间结构分析,并得出相应结论:Depth map在定量分析园林空间特性,确定园林空间不同层面之间的联系方面起着重要作用。

基于depth-map和分布式视频编码的多视点视频传输方法

针对多视点视频传输系统数据量庞大的问题,提出一种基于深度图(depth-map)和分布式视频编码(DVC)的不等错误保护(UEP)传输方法。该方法首先基于多个视点提取深度图;然后,在传输过程中传输一个视点及其深度图;最后,经过网络传输,在解码端由一个视点图及其深度图生成其他视点。由于视点图和深度图在解码端的重要程度不同,对需要传输的视点图和深度图采用不同的分布式视频编码方法,进行不平等的错误保护。仿真实验结果表明,所提传输方法比传统的分布式多视点视频编码传输系统具有更好的抗误码性能,提高了传输可靠性,图像的峰值信噪比(PSNR)约提高1.5 dB。

一种针对大规模Read Mapping的高效DBG索引方法

为了能够回答生物信息学中关于de Bruijn graph(DBG)的两个问题——①对于任意的k-mer,回答其是否为DBG的顶点,②对于DBG的任意顶点,回答其邻接信息(入边和出边),提出了一种针对大规模read mapping的高效DBG索引方法.本文将以上两个问题转化为非重复多路径上的k-mer和(k+1)-mer的确切查找问题,并利用FM-index进行解决.首先,对给定的参考序列进行压缩,即非重复多路径的发现,从而压缩了序列中大量存在的重复(k+1)-mer.其次,基于非重复多路径FM-index对DBG进行索引.查找k-mer是否出现在DBG上,若找到,给出该k-mer的直接前驱和直接后继结点,从而提高时空效率.最后,在62种大肠杆菌菌株的基因组上进行实验.实验结果表明,所提出的方法可以高效地对多参考序列的DBG进行索引.

Interpreting Randomly Wired Graph Models for Chinese NER

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing(NLP)tasks.However,most existing approaches only focus on improving the performance of models but ignore their interpretability.In this work,we propose a Randomly Wired Graph Neural Network(RWGNN)by using graph to model the structure of Neural Network,which could solve two major problems(word-boundary ambiguity and polysemy)of ChineseNER.Besides,we develop a pipeline to explain the RWGNNby using Saliency Map and Adversarial Attacks.Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden states of RWGNN.

INPAINTING ALGORITHM FOR KINECT DEPTH MAP BASED ON FOREGROUND SEGMENTATION

The depth information of the scene indicates the distance between the object and the camera,and depth extraction is a key technology in 3D video system.The emergence of Kinect makes the high resolution depth map capturing possible.However,the depth map captured by Kinect can not be directly used due to the existing holes and noises,which needs to be repaired.We propose a texture combined inpainting algorithm in this paper.Firstly,the foreground is segmented combined with the color characteristics of the texture image to repair the foreground of the depth map.Secondly,region growing is used to determine the match region of the hole in the depth map,and to accurately position the match region according to the texture information.Then the match region is weighted to fill the hole.Finally,a Gaussian filter is used to remove the noise in the depth map.Experimental results show that the proposed method can effectively repair the holes existing in the original depth map and get an accurate and smooth depth map,which can be used to render a virtual image with good quality.

A Map Coloring Method

Different vertices are colored in a plan. Adjacent vertices are colored dif-ferently from nonadjacent vertices, which are colored the same color. One color is used for a single point, two colors are used for points without a loop, and a maximum of four colors are used for points with a loop. A maximum of four colors are used to color all points. .