A Biological Hierarchical Model Based Underwater Moving Object Detection

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results

Block-Based MAP Superresolution Using Feature-Driven Prior Model

In the field of image superresolution reconstruction (SRR), the prior can be employed to solve the ill-posed problem. However, the prior model is selected empirically and characterizes the entire image so that the local feature of image cannot be represented accurately. This paper proposes a feature-driven prior model relying on feature of the image and introduces a block-based maximum a posteriori (MAP) framework under which the image is split into several blocks to perform SRR. Therefore, the local feature of image can be characterized more accurately, which results in a better SRR. In process of recombining superresolution blocks, we still design a border-expansion strategy to remove a byproduct, namely, cross artifacts. Experimental results show that the proposed method is effective

Optimizing Maximum Monitoring Frequency and Guaranteeing Target Coverage and Connectivity in Energy Harvesting Wireless Sensor Networks

Improving the quality of monitoring and guaranteeing target coverage and connectivity in energy harvesting wireless sensor networks (EH-WSNs) are important issues in near-perpetual environmental monitoring. Existing solutions only focus on the utility of coverage or energy efficient coverage by considering target connectivity for battery-powered WSNs. This paper focuses on optimizing the maximum monitoring frequency with guaranteed target coverage and connectivity in EH-WSNs. We first analyzed the factors affecting monitoring quality and the energy harvesting model. Thereafter, we presented the problem formulation and proposed the algorithm for maximizing monitoring frequency and guaranteeing target coverage and connectivity (MFTCC) that is based on graph theory. Furthermore, we presented the corresponding distributed implementation approach. On the basis of the existing energy harvesting prediction model, expensive simulations show that the proposed MFTCC algorithm achieves high average maximum monitoring frequency and energy usage ratio. Moreover, it obtains a higher throughput than existing target monitoring methods

Underwater Object Segmentation Based on Optical Features

Underwater optical environments are seriously affected by various optical inputs, such as artificial light, sky light, and ambient scattered light. The latter two can block underwater object segmentation tasks, since they inhibit the emergence of objects of interest and distort image information, while artificial light can contribute to segmentation. Artificial light often focuses on the object of interest, and, therefore, we can initially identify the region of target objects if the collimation of artificial light is recognized. Based on this concept, we propose an optical feature extraction, calculation, and decision method to identify the collimated region of artificial light as a candidate object region. Then, the second phase employs a level set method to segment the objects of interest within the candidate region. This two-phase structure largely removes background noise and highlights the outline of underwater objects. We test the performance of the method with diverse underwater datasets, demonstrating that it outperforms previous methods