Search

Electromyography (EMG) signal based cross-subject gesture recognition methods reduce the influence of individual differences using transfer learning technology. These methods generally require calibration data collected from new subjects to adapt the pre-trained model to existing subjects. However, collecting calibration data is usually trivial and inconvenient for new subjects. This is currently a major obstacle to the daily use of hand gesture recognition based on EMG signals. To tackle the problem, we propose a novel dynamic domain generalization (DDG) method which is able to achieve accurate recognition on the hand gesture of new subjects without any calibration data. In order to extract more robust and adaptable features, a meta-adjuster is leveraged to generate a series of template coefficients to dynamically adjust dynamic network parameters. Specifically, two different kinds of templates are designed, in which the first one is different kinds of features, such as temporal features, spatial features, and spatial-temporal features, and the second one is different normalization layers. Meanwhile, a mix-style data augmentation method is introduced to make the meta-adjuster's training data more diversified. Experimental results on a public dataset verify that the proposed DDG outperforms the counterpart methods.

In neuron-biology, 3D neuron dendrite reconstruction followed by spine identification is indispensable for the study of neuronal functions and biophysical properties. In this paper, we propose an automatic dendrite reconstruction method to with a surface representation of the neuron on the basis of a novel level set approach. Our novel level set approach can effectively tackle the problem of segmentation under blurring and intensity in-homogeneity. Then spines are detected based on dendrite medial axis and a label-based thinning strategy is proposed to accurately extract the dendrite skeleton for spine identification. Experimental results reveal that our method works well.

Color demosaicing is an essential step in the camera image processing pipeline, especially on hardware platforms for real-time video applications. Though many demosaicing algorithms have been proposed in the past two decades, there remains a substantial gap between industrial needs and academic research. On one hand, industry requires a high perceptual quality, artifact-free, and low-cost demosaicing algorithm that is noniterative and small window based ready to be implemented on a hardware platform with a limited line buffer. On the other hand, academia is targeting high PSNR/SSIM, with the computation cost and line buffer receiving second priority, and often a frame buffer and iterative operation are used. The cost, large line buffer and iteration requirement make most existing demosaicing algorithms inapplicable on hardware platforms. In this paper, we introduce a novel low-cost demosaicing algorithm to narrow the gap. We keep the operation window size and computation cost as the first priority and achieve both high PSNR/SSIM and visual perceptual quality compared to previous state-of-the-art methods on standard test datasets. We fully investigate the a priori knowledge of natural scene raw images and find several key cues that are beneficial to demosaicing. Our demosaicing algorithm is a smart fusion of these useful cues. Furthermore, we solve typical demosaicing issues that occur in many traditional methods, including false color artifacts, T-section closing, and zippering artifacts. The proposed method has no learning stage, no iteration operation, a small line buffer and a limited number of parameters, so it can easily be applied to hardware platforms. [ABSTRACT FROM AUTHOR]

Through well-designed optimization paradigm and deep neural networks as feature extractor, deep reinforcement learning (DRL) algorithms learn optimal policy on discrete and continuous action space. However, such capability is restricted by the low sampling efficiency. By inspecting the importance of feature extraction in DRL, we find that state feature learning is one of the key obstacles for sampling efficiently. To this end, we propose a new state representation learning scheme with adjacent state consistency loss (ASC loss). The loss is based on the hypothesis that the distance between adjacent states is smaller than that of far apart ones since scenes in videos generally evolve smoothly. We exploit ASC loss as an assistant of RL loss in the training phase to boost the state feature learning, and make evaluation on existing DRL algorithms as well as behavioral cloning algorithm. Experiments on Atari games and MuJoCo continuous control tasks demonstrate the effectiveness of our scheme. [ABSTRACT FROM AUTHOR]

The k-nearest neighbors outlier detection is a simple yet effective widely renowned method in data mining. The actual application of this model in the big data domain is not feasible due to time and memory restrictions. Several distributed alternatives based on MapReduce have been proposed to enable this method to handle large-scale data. However, their performance can be further improved with new designs that fit with newly arising technologies. Furthermore, it gives to each attribute the same importance to outlier. There are several approaches to enhance its precision, with the entropy-based outlier detection being among the most successful ones. Entropy-based outlier detection computes attribute entropy of the data set to weighted distance formula for the outlier detection. Apart from the existing the k-nearest neighbors outlier detection to handle big datasets, there is not an entropy-based outlier detection to manage that volume of data. In this paper, we propose an entropy-based outlier detection based on Spark. It presents three separately stages. The first stage computes attribute entropy. The second stage finds the k nearest neighbors and calculates the degrees of outliers using the attribute entropy computed previously. The third stage ranks each point on the degrees of outliers and declares the top n points in this ranking to be outliers. Extensive experimental results show the advantages of the proposed method. This algorithm can improve the outlier detection precision, reduce the runtime and realize the effective large scale dataset outlier detection. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in mapping visual features into compact binary codes for applications on large-scale image collections. Encoding high-dimensional data as compact binary codes reduces the memory cost for storage. Besides, it benefits the computational efficiency since the computation of similarity can be efficiently measured by Hamming distance. In this paper, we propose a novel flexible scale invariant feature transform (SIFT) binarization (FSB) algorithm for large-scale image search. The FSB algorithm explores the magnitude patterns of SIFT descriptor. It is unsupervised and the generated binary codes are demonstrated to be dispreserving. Besides, we propose a new searching strategy to find target features based on the cross-indexing in the binary SIFT space and original SIFT space. We evaluate our approach on two publicly released data sets. The experiments on large-scale partial duplicate image retrieval system demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]