A novel camera calibration technique based on differential evolution particle swarm optimization algorithm

Opportunistic medium access control for maximizing packet delivery rate in dynamic access networks

Video frame interpolation is an important area in the computer vision research activities for video post-processing, surveillance, and video restoration tasks. It aims toward increasing the frame rate of a video sequence by calculating intermittent frames between consecutive input frames. This ensures extra smooth, clear motion in order to make animation fluid enough and reduce display motion blur. Advanced deep learning algorithms have the potential to discover knowledge from large-scale diverse video data. These algorithms gain insights about intermediate motion and provide new opportunities to further improve video interpolation technologies. This survey demonstrates a comprehensive overview of about a good number of contributions over past decade pertinent to the latest developments in this domain. The survey paper highlights common challenges in the area of video frame interpolation based on three key aspects: high visual quality, low complexity, and high efficiency of interpolated output from regular videos with the standard frame rate. We scrutinize the architectures, workflows, performance, advantages, and disadvantages and generate a broad categorization along with an overview of experimental results of various state-of-the-art methods executed on benchmark datasets. This survey discusses applications of diverse interpolation frameworks. It provides a backbone reference that inspires future researchers to optimize current techniques on academic and industrial grounds.

A comprehensive survey on video frame interpolation techniques

We have developed a smart dive glove that recognizes 13 static hand gestures used in diving communication. The smart glove employs five dielectric elastomer sensors to capture finger motion and implements a machine learning classifier in the onboard electronics to recognize gestures. Five basic classification algorithms are trained and assessed: the decision tree, support vector machine (SVM), logistic regression, Gaussian naïve Bayes, and multilayer perceptron. These basic classifiers were selected as they perform well in multiclass classification problems, can be trained using supervised learning, and are model-based algorithms that can be implemented on a microprocessor. The training dataset was collected from 24 participants providing for a range of different hand sizes. After training, the algorithms were evaluated in a dry environment using data collected from ten new participants to test how well they cope with new information. Furthermore, an underwater experiment was conducted to assess any impact of the underwater environment on each algorithm's classification. The results show all classifiers performed well in a dry environment. The accuracies and F1-scores range between 0.95 and 0.98, where the logistic regressor and SVM have the highest scores for both the accuracy and F1-score (0.98). The underwater results showed that all algorithms work underwater; however, the performance drops when divers must focus on buoyancy control, breathing, and diver trim. 

Glove-Based Hand Gesture Recognition for Diver Communication

In this work a new algorithm is developed for onboard calibration of three-axis strapdown magnetometers. The sensor errors, namely hard iron, soft iron, nonorthogonality, scale factors, biases and among others are taken into account. Particle Swarm Optimization (PSO) strategy is applied to estimate the errors above. The advantages of this method are no need for good initial values or linearization, easy realization and fast convergence. Accuracy and robustness of the proposed algorithm are validated by experiments. The post-calibration residuals are down to less than 10nT compared with approximate 1000nT before calibration. The proposed algorithm yields robust results with sufficient accuracy in tests.

Calibration of three-axis strapdown magnetometers using Particle Swarm Optimization algorithm

A novel camera calibration approach based on Particle Swarm Optimization(PSO) is put forward in this paper. Firstly, we designed 35 sample points on the calibration box; Secondly, the 3D point and their corresponding 2D image coordi- nate of these sample points were obtained; In this approach, PSO algorithm was adopted to obtain the camera intrinsic parameters. Among the 35 sample points, 30 points were used for training, and other 5 points were mainly used to evaluate the effectiveness of camera calibration. The techniques presented in this paper have been implemented and tested with both synthetic and real data. Our experimental results show that the method can obtain satisfying calibration accuracy.

Camera Calibration Based on Particle Swarm Optimization

With the development of social life and improvement of computer science and technology, human-computer interaction(HCI) technology has been paid more and more attentions and become an important aspect of research field. Gestures, as a natural ways of communication between people, contain rich information. So gesture recognition has become a branch of human-computer interaction, which has gradually become a hot research topic. This paper presents a method for gesture recognition based on data glove. Sparse decomposition is firstly conducted on the raw data, then a support vector machine(SVM) classifier is applied on the dictionary part to achieve recognition results. The experimental results reveal that our method can achieve very high recognition accuracy on the data glove gestures.

Sparse decomposition for data glove gesture recognition

Virtual 3D hand is a natural and efficient tool of human-computer interaction (HCI), which is widely used in virtual reality (VR). In order to meet the needs of 3D Hand-based HCI, a novel platform is presented in this paper. Firstly, using cascade local coordinate systems and two direction vectors of hand, a new 3D hand model with a better design of thumb is developed. The presentation of articulated object is discussed in detail. Secondly, we demonstrate a method which synthetically utilizes the data glove and position tracker. According to the analysis of the rotating hand, we established the relationship between angles of position tracker and model parameters. Finally, the 3D Hand-based HCI platform using VC++ and OpenGL is presented. Experimental results show that, our 3D hand model can express a variety of gestures and the platform design is feasible and can meet our requirements. It also laid the foundation for free hand tracking.

The design and implementation of 3d hand-based human-computer interaction platform

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective. When existing methods reconstruct the mesh surface of complex objects, the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework; the 3D topology is easily limited by predefined templates and inflexible, and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology, thus destroying the surface details; the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices, and the training time of the reconstructed network is too long. In this paper, we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network (GCN) and topology modification. We use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the topology. Meanwhile, a feature fusion method is proposed to make full use of the features of each stage of the image hierarchically. We use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training process. Extensive experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces, but also has better qualitative and quantitative results. 

Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

D hand gesture tracking is one of the key problems in human-computer interface design and has attracted increasing attention as an alternative way to traditional input devices, such as mouses and keyboards. Generally speaking, a human hand, as a typical articulated structure, has 15 joints and high dimensionality with 26DOFs, which makes it very difficult for real time tracking. A novel approach is presented in this paper. Firstly, a basic concept, sigma point, is defined, and the way to acquire the Sigma points of a state variable is presented and further proved in detail. Then, the global human hand tracking algorithm is put forward, which combine Sigma points and optimized selection based upon particle weights. In order to improve robustness to changing light conditions, a new skin model is proposed which is based on skin luminance. At last, our experimental results demonstrate that, compared with the classical particle filtering, our method has the capacity to use smaller number of particles for higher precision. Keywords-Human hand tracking; particle filtering; high dimensionality

A Novel Tracking Method Based on Optimized Selection

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