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Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS) acquisition are very different from traditional image acquisition, the general image compression solution may not work well. In this paper, we propose an efficient lossy compression solution for CS acquisition of images by considering the distinctive features of the CSI. First, we design an adaptive compressive sensing acquisition method for images according to the sampling rate, which could achieve better CS reconstruction quality for the acquired image. Second, we develop a universal quantization for the obtained CS measurements from CS acquisition without knowing any a priori information about the captured image. Finally, we apply these two methods in the CSI system for efficient lossy compression of CS acquisition. Simulation results demonstrate that the proposed solution improves the rate-distortion performance by 0.4~2 dB comparing with current state-of-the-art, while maintaining a low computational complexity.

In this study, the authors present a method to extract moving objects in image sequences. The proposed approach is based on a graph cuts algorithm defined on a spatiotemporal superpixel neighbourhood. Presegmented superpixels are partitioned into foreground and background while preserving temporal and spatial coherence. It achieves this goal by three steps. First, instead of operating at pixel level, the superpixels are advocated as basic units of the authors segmentation scheme. Second, within the graph cuts framework, two superpixel‐based data terms and two superpixel‐based smoothness terms are proposed to solve segmentation problem. Finally, the proposed method yields the segmentation of all the superpixels within video volume by the graph cuts algorithm. To illustrate the advantages of this approach, the quantitative and qualitative results are compared with other state‐of‐the‐art methods. The experimental results show that the proposed method gives better performance of segmentation with respect to these methods.

For analyzing the traffic anomaly within dashcam videos from the perspective of ego-vehicles, the agent should spatial-temporally localize the abnormal occasion and regions and give a semantically recounting of what happened. Most existing formulations concentrate on the former spatial-temporal aspect and mainly approach this goal by training normal pattern classifiers/regressors/dictionaries with large-scale availably labeled data. However, anomalies are context-related, and it is difficult to distinguish the margin of abnormal and normal clearly. This paper proposes a progressive unsupervised driving anomaly detection and recounting (D&R) framework. The highlights are three-fold: (1) We formulate driving anomaly D&R as a temporal-spatial-semantic (TSS) model, which achieves a coarse-to-fine focusing and generates convincing driving anomaly D&R. (2) This work contributes an unsupervised D&R without any training data while performing an effective performance. (3) We novelly introduce the traffic saliency, isolation forest, visual semantic causal relations of driving scene to effectively construct the TSS model. Extensive experiments on a driving anomaly dataset with 106 video clips (temporal-spatial-semantically labeled carefully by ourselves) demonstrate superior performance over existing techniques.

Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC). Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value) color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms.

This paper describes the underwater data collection utilizing stationary sensor node and movable sensor node. We consider applying the system to investigate the conditions of marine ecosystem around the coral reef at Okinawa, Japan. The framework and fundamental design of our proposed underwater information gathering system under development are introduced. It is also reported the development of the prototypes of the sensory node and movable sensor node. Basic examinations utilizing such prototype at underwater environment are also described. This paper also describes experimental results of image gathering from via wireless local area network communication. Our future trials based on the current developmental situation are also discussed.

Localizationis of vital importance for an unmanned vehicle to drive on the road. Most of the existing algorithms are based on laser range finders, inertial equipment, artificial landmarks, distributing sensors or global positioning system(GPS) information. Currently, the problem of localization with vision information is most concerned. However, vision-based localization techniquesare still unavailable for practical applications. In this paper, we present a vision-based sequential probability localization method. This method uses the surface information of the roadside to locate the vehicle, especially in the situation where GPS information is unavailable. It is composed of two step, first, in a recording stage, we construct a ground truthmap with the appearance of the roadside environment. Then in an on-line stage, we use a sequential matching approach to localize the vehicle. In the experiment, we use two independent cameras to observe the environment, one is left-orientated and the other is right. SIFT features and Daisy features are used to represent for the visual appearance of the environment. The experiment results show that the proposed method could locate the vehicle in a complicated, large environment with high reliability.

The authors wish to make the following correction to their paper [1].[...]

Entropy has been a common index to quantify the complexity of time series in a variety of fields. Here, we introduce an increment entropy to measure the complexity of time series in which each increment is mapped onto a word of two letters, one corresponding to the sign and the other corresponding to the magnitude. Increment entropy (IncrEn) is defined as the Shannon entropy of the words. Simulations on synthetic data and tests on epileptic electroencephalogram (EEG) signals demonstrate its ability of detecting abrupt changes, regardless of the energetic (e.g., spikes or bursts) or structural changes. The computation of IncrEn does not make any assumption on time series, and it can be applicable to arbitrary real-world data.