Bibliometric Mapping of the Computational Intelligence Field

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation

3D Reconstruction: Novel Method for Finding of Corresponding Points using Pseudo Colors

This paper deals with the reconstruction of spatial coordinates of an arbitrary point in a scene using two images scanned by a 3D camera or two displaced cameras. Calculations are based on the perspective geom-etry. Accurate determination of corresponding points is a fundamental step in this process. The usually used methods can have a problem with points, which lie in areas without sufficient contrast. This paper describes our proposed method based on the use of the relationship between the selected points and area feature points. The proposed method finds correspondence using a set of feature points found by SURF. An algorithm is proposed and described for quick removal of false correspondences, which could ruin the correct reconstruction. The new method, which makes use of pseudo color image representation (pseudo coloring) has been proposed subsequently. By means of this method it is possible to significantly increase the color contrast of the surveyed image, and therefore add more information to find the correct correspondence. Reliability of the found correspondence can be verified by reconstruction of 3D position of selected points. Executed experiments confirm our assumption

Approximate Decentralized Bayesian Inference

This paper presents an approximate method for performing Bayesian inference in models with conditional independence over a decentralized network of learning agents. The method first employs variational inference on each individual learning agent to generate a local approximate posterior, the agents transmit their local posteriors to other agents in the network, and finally each agent combines its set of received local posteriors. The key insight in this work is that, for many Bayesian models, approximate inference schemes destroy symmetry and dependencies in the model that are crucial to the correct application of Bayes' rule when combining the local posteriors. The proposed method addresses this issue by including an additional optimization step in the combination procedure that accounts for these broken dependencies. Experiments on synthetic and real data demonstrate that the decentralized method provides advantages in computational performance and predictive test likelihood over previous batch and distributed methods.Comment: This paper was presented at UAI 2014. Please use the following BibTeX citation: @inproceedings{Campbell14_UAI, Author = {Trevor Campbell and Jonathan P. How}, Title = {Approximate Decentralized Bayesian Inference}, Booktitle = {Uncertainty in Artificial Intelligence (UAI)}, Year = {2014}

Consensus clustering in complex networks

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.Comment: 11 pages, 12 figures. Published in Scientific Report

Learning Combinatorial Embedding Networks for Deep Graph Matching

Graph matching refers to finding node correspondence between graphs, such that the corresponding node and edge's affinity can be maximized. In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises. To this end, this paper devises an end-to-end differentiable deep network pipeline to learn the affinity for graph matching. It involves a supervised permutation loss regarding with node correspondence to capture the combinatorial nature for graph matching. Meanwhile deep graph embedding models are adopted to parameterize both intra-graph and cross-graph affinity functions, instead of the traditional shallow and simple parametric forms e.g. a Gaussian kernel. The embedding can also effectively capture the higher-order structure beyond second-order edges. The permutation loss model is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network allows for varying numbers of nodes in graphs for training and inference. Moreover, our network is class-agnostic with some generalization capability across different categories. All these features are welcomed for real-world applications. Experiments show its superiority against state-of-the-art graph matching learning methods.Comment: ICCV2019 oral. Code available at https://github.com/Thinklab-SJTU/PCA-G

Structure-from-motion using convolutional neural networks

Abstract. There is an increasing interest in the research community to 3D scene reconstruction from monocular RGB cameras. Conventionally, structure from motion or special hardware such as depth sensors or LIDAR systems were used to reconstruct the point clouds of complex scenes. However, structure from motion technique usually fails to create the dense point cloud, while particular sensors are inconvenient and more expensive than RGB cameras. Recent advances in deep learning research have presented remarkable results in many computer vision tasks. Nevertheless, complete solution for large-scale dense 3D point cloud reconstruction still remains untouched. This thesis introduces a deep-learning-based structure-from-motion pipeline for the dense 3D scene reconstruction problem. Several deep neural networks models were trained to predict the single view depth maps, and relative camera poses from RGB video frames. First, the obtained depth values were sequentially scaled to the first depth map. Next, the iterative closest point algorithm was utilized to further align the estimated camera poses. From these two processed cues, the point clouds of the scene were reconstructed by simple concatenation of 3D points. Although the final point cloud results are encouraging and in certain aspects preferable to the conventional structure from motion method, the system is just tackling the 3D reconstruction problem to some extent. The prediction outputs still have errors, especially in the camera orientation estimation. This system can be seen as the initial study that opens up lots of research questions and improvements in the future. Besides, the study also signified the positive intimation for using unsupervised deep learning scheme to address the 3D scene reconstruction task