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In a vehicular optical camera communication (VOCC) system, digital information is transmitted using LED panels and received using cameras. The transmitted bits are obtained by processing the captured images to detect the ON and OFF statuses of LEDs in the array. In determining the LED status, the current LED bit detection algorithms only rely on the grayscale, which is an unreliable feature of LEDs. Consequently, they exhibit poor performance in unfavorable conditions. The contribution of this paper is the proposed multi-feature LED bit detection algorithm that employs three new features of LED: average greyscale ratio (AGR), gradient radial inwardness (GRI), and neighbor greyscale ratio (NGR). Two features, AGR and GRI, individually have substantially more discriminability of LED statuses than greyscale. More importantly, the three proposed features differentiate LED statuses under different perspectives. Consequently, the combination of the three features using Fisher linear discriminant analysis (FLDA) yields outstanding accuracy and robustness of bit detection, even in severe conditions. Highly realistic simulations of a VOCC system are conducted to verify the superiority and robustness of the proposed algorithm.

With the development of visual sensor equipment (e.g., personal smart phones, vehicle cameras, surveillance videos and camcorders), scene recognition technology has attracted much attention due to its latent applications in visual surveillance, intelligent traffic and aerial remote sensing. Although some progress has been made in the field of scene recognition in recent years, the complexity of scene images and the inadequate numbers of labeled data pose challenges in this area. Hence, to effectively fuse the multiple features of each image and employ the information of both labeled and unlabeled images for scene recognition, we proposed a semi-supervised multi-feature regression (SSMFR) model in this paper. The SSMFR model possesses three advantages. First, the model propagates the labels of labeled data to unlabeled data by utilizing graph-based semi-supervised learning techniques so that both the information regarding unlabeled data and labeled data can be exploited to gain better performance. Second, SSMFR employs multiple graphs to characterize the structures of multiple feature spaces and adaptively assigns the weight to different graphs. Therefore, SSMFR can efficiently preserve the manifold structure of samples in each feature space and adequately exploit the complementary information of multiple features. Moreover, SSMFR adopts a $l_{2,1}$ -norm constraint to learn a sparse and robust classifier for scene recognition. To solve the SSMFR model, we proposed a simple and efficient iterative update optimization scheme. Finally, we also proved the convergence of SSMFR by theoretical analysis and experiments. Experiments were conducted on several benchmark scene datasets, and the experimental results demonstrated that the proposed SSMFR model can obtain better performance for scene recognition than some other state-of-the-art algorithms.

Skeleton-based action recognition has advanced significantly in the past decade. Among deep learning-based action recognition methods, one of the most commonly used structures is a two-stream network. This type of network extracts high-level spatial and temporal features from skeleton coordinates and optical flows, respectively. However, other features, such as the structure of the skeleton or the relations of specific joint pairs, are sometimes ignored, even though using these features can also improve action recognition performance. To robustly learn more low-level skeleton features, this paper introduces an efficient fully convolutional network to process multiple input features. The network has multiple streams, each of which has the same encoder-decoder structure. A temporal convolutional network and a co-occurrence convolutional network encode the local and global features, and a convolutional classifier decodes high-level features to classify the action. Moreover, a novel fusion strategy is proposed to combine independent feature learning and dependent feature relating. Detailed ablation studies are performed to confirm the network's robustness to all feature inputs. If more features are combined and the number of streams increases, performance can be further improved. The proposed network is evaluated on three skeleton datasets: NTU-RGB + D, Kinetics, and UTKinect. The experimental results show its effectiveness and performance superiority over state-of-the-art methods.

Accurate recognition of gestures based on surface EMG signals is of very importance in the study of human prosthetic interaction. In this paper, multi-feature combination and adaptive AdaBoost algorithm are used to identify nine kinds of fine gestures. First, in order to adaptively set the threshold for extracting the active segment, the algorithm for detecting the active segment of the surface myoelectric signal is improved, and the effective active segment value is obtained by rejecting the invalid active segment. Second, a fine gesture classification method based on multi-feature combination is proposed. In this paper, the recursive feature elimination algorithm based on cyclic feature selection is used to screen out the feature set with the top classification effect, and the feature set selected by eight experimental objects is counted, and the optimal feature combination is obtained according to the experimental analysis. Finally, nine fine gesture actions are identified through a weak classifier constructed by combining the AdaBoost algorithm and the single-layer decision tree. The experimental results show that the proposed algorithm can achieve an average recognition rate of 98.56 (±1.44)% for nine fine gestures.

Exudates can be regarded as one of the most prevalent clinical signs of diabetic retinopathy, and the detection of exudates has important clinical significance in diabetic retinopathy diagnosis. In this paper, a novel approach named superpixel multi-feature classification for the automatic detection of exudates is developed. First, an entire image is segmented into a series of superpixels considered as candidates. Then, a total of 20 features, including 19 multi-channel intensity features and a novel contextual feature, are proposed for characterizing each candidate. A supervised multi-variable classification algorithm is also introduced to distinguish the true exudates from the spurious candidates. Finally, a novel optic disc detection technique is designed to further improve the performance of classification accuracy. Extensive experiments are carried out on two publicly available online databases, DiaretDB1, and e-ophtha EX. Compared with other state-of-the-art approaches, the experimental results show the advantages and effectiveness of the proposed approach.