International Conference on Control, Automation, Robotics and Vision 

Abstract: Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.

Abstract: A new learning algorithm called extreme learning machine (ELM) has recently been proposed for single-hidden layer feedforward neural networks (SLFNs) to easily achieve good generalization performance at extremely fast learning speed. ELM randomly chooses the input weights and analytically determines the output weights of SLFNs. This paper shows that ELM can be extended to radial basis function (RBF) network case, which allows the centers and impact widths of RBF kernels to be randomly generated and the output weights to be simply analytically calculated instead of iteratively tuned. Interestingly, the experimental results show that the ELM algorithm for RBF networks can complete learning at extremely fast speed and produce generalization performance very close to that of SVM in many artificial and real benchmarking function approximation and classification problems. Since ELM does not require validation and human-intervened parameters for given network architectures, ELM can be easily used.

Abstract: This paper proposes a set of methods for building informative and robust feature point representations, used for accurately labeling points in a 3D point cloud, based on the type of surface the point is lying on. The feature space comprises a multi-value histogram which characterizes the local geometry around a query point, is pose and sampling density invariant, and can cope well with noisy sensor data. We characterize 3D geometric primitives of interest and describe methods for obtaining discriminating features used in a machine learning algorithm. To validate our approach, we perform an in-depth analysis using different classifiers and show results with both synthetically generated datasets and real-world scans.

Abstract: This paper presents CIRES, a new online system for a content-based retrieval in digital image libraries. Content-based image retrieval systems have traditionally used color and texture analyses. These analyses have not always achieved adequate level of performance and user satisfaction. The growing need for robust image retrieval systems has led to a need for additional retrieval methodologies. CIRES addresses this issue by using image structure in addition to color and texture. The efficacy of using structure in combination with color and texture is demonstrated.

Abstract: Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to different viewpoints on central issues, such as the formal statement of the learning goal, a large number of different methods and approaches have been introduced. In this paper we aim to present an integrated survey of the field. First, the issue of the multi-agent learning goal is discussed, after which a representative selection of algorithms is reviewed. Finally, open issues are identified and future research directions are outlined