Inverted pyramid frame forward and backward prediction for distorted video by water waves.

There has been much research on how to restore a single image from distorted video. Random water surface variation, an inability to model the surface, and multiple factors in the imaging processing leading to different geometric distortions in each frame are among the challenges. This paper proposes an inverted pyramid structure based on the cross optical flow registration approach and a multi-scale weight fusion method based on wavelet decomposition. The inverted pyramid based on the registration method is used to estimate the original pixel positions. A multi-scale image fusion method is applied to fuse the two inputs processed by optical flow and backward mapping, and two iterations are proposed to improve the accuracy and stability of the output video. The method is tested on several reference distorted videos and our videos, which were obtained through our experimental equipment. The obtained results exhibit significant improvements over other reference methods. The corrected videos obtained with our approach have a higher degree of sharpness, and the time required to restore the videos is significantly reduced.

Adaptive color correction and detail restoration for underwater image enhancement.

Underwater images have different color casts due to different attenuation conditions, such as bluish, greenish, and yellowish. In addition, due to floating particles and special illumination, underwater images have problems such as the lack of detail and unnecessary noise. To handle the above problems, this paper proposes a new, to the best of our knowledge, three-step adaptive enhancement method. For the first step, adaptive color correction, the three channels are adjusted based on the intermediate color channel, which is calculated by considering the positional relationship of the histogram distribution. For the second step, denoise and restore details, we first transform the space to hue, saturation, value (HSV), a detailed restoration method based on the edge-preserving decomposition that restores the lost detail while removing the influence of some noise. For the third step, we improve the global contrast. Still in the HSV space, a simple linear stretch strategy is applied to the saturation channel. Experiments on the standard underwater image enhancement benchmark data set have proved that our method yields more natural colors and more valuable detailed information than several state-of-the-art methods. In addition, our method also improves the visibility of underwater images captured by low-light scenes and different hardware cameras.

Motion Planning of Autonomous Mobile Robot Using Recurrent Fuzzy Neural Network Trained by Extended Kalman Filter.

This paper proposes a novel motion planning method for an autonomous ground mobile robot to address dynamic surroundings, nonlinear program, and robust optimization problems. A planner based on the recurrent fuzzy neural network (RFNN) is designed to program trajectory and motion of mobile robots to reach target. And, obstacle avoidance is achieved. In RFNN, inference capability of fuzzy logic and learning capability of neural network are combined to improve nonlinear programming performance. A recurrent frame with self-feedback loops in RFNN enhances stability and robustness of the structure. The extended Kalman filter (EKF) is designed to train weights of RFNN considering the kinematic constraint of autonomous mobile robots as well as target and obstacle constraints. EKF's characteristics of fast convergence and little limit in training data make it suitable to train the weights in real time. Convergence of the training process is also analyzed in this paper. Optimization technique and update strategy are designed to improve the robust optimization of a system in dynamic surroundings. Simulation experiment and hardware experiment are implemented to prove the effectiveness of the proposed method. Hardware experiment is carried out on a tracked mobile robot. An omnidirectional vision is used to locate the robot in the surroundings. Forecast improvement of the proposed method is then discussed at the end.

Automatic Rectification of the Hybrid Stereo Vision System.

By combining the advantages of 360-degree field of view cameras and the high resolution of conventional cameras, the hybrid stereo vision system could be widely used in surveillance. As the relative position of the two cameras is not constant over time, its automatic rectification is highly desirable when adopting a hybrid stereo vision system for practical use. In this work, we provide a method for rectifying the dynamic hybrid stereo vision system automatically. A perspective projection model is proposed to reduce the computation complexity of the hybrid stereoscopic 3D reconstruction. The rectification transformation is calculated by solving a nonlinear constrained optimization problem for a given set of corresponding point pairs. The experimental results demonstrate the accuracy and effectiveness of the proposed method.

Identifying Connectome Module Patterns via New Balanced Multi-Graph Normalized Cut.

Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks.

A novel robot visual homing method based on SIFT features.

Warping is an effective visual homing method for robot local navigation. However, the performance of the warping method can be greatly influenced by the changes of the environment in a real scene, thus resulting in lower accuracy. In order to solve the above problem and to get higher homing precision, a novel robot visual homing algorithm is proposed by combining SIFT (scale-invariant feature transform) features with the warping method. The algorithm is novel in using SIFT features as landmarks instead of the pixels in the horizon region of the panoramic image. In addition, to further improve the matching accuracy of landmarks in the homing algorithm, a novel mismatching elimination algorithm, based on the distribution characteristics of landmarks in the catadioptric panoramic image, is proposed. Experiments on image databases and on a real scene confirm the effectiveness of the proposed method.

Integrated Visualization of Human Brain Connectome Data.

Visualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases.

Feature optimization for long-range visual homing in changing environments.

This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance. This paper presents a modified feature extraction algorithm to decrease the influence of anisotropic feature distribution. In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments. To verify the feasibility of the proposal, several comprehensive evaluations are conducted. The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.