Zhibin Yu

Object extraction from underwater images through logical stochastic resonance.

Logical stochastic resonance (LSR), the phenomenon in which the interplay of noise and nonlinearity can raise the accurate probability of response to feeble input signals, is studied in this Lettter to extract objects from highly degraded underwater images. Images captured under water, especially in the turbid areas, always suffer from interference through heavy noise caused by the suspended particles. Inherent noise and nonlinearity cause difficulty in processing these images through conventional image processing methods. However, LSR can optimally address such issues. A heavily degraded image is first extended to a 1D form in the direction determined by the illumination condition, and then normalized to be placed in the LSR system as an input signal. Additional Gaussian noise is added to the system as the auxiliary power to help separate the object and the background. Results in the natural offshore area demonstrate the effect of LSR on image processing, and the proposed method creates an interesting direction in the processing of heavily degraded images.

Robust and automatic cell detection and segmentation from microscopic images of non-setae phytoplankton species

Saliency-based marker-controlled watershed method was proposed to detect and segment phytoplankton cells from microscopic images of non-setae species. This method first improved IG saliency detection method by combining saturation feature with colour and luminance feature to detect cells from microscopic images uniformly and then produced effective internal and external markers by removing various specific noises in microscopic images for efficient performance of watershed segmentation automatically. The authors built the first benchmark dataset for cell detection and segmentation, including 240 microscopic images across multiple phytoplankton species with pixel-wise cell regions labelled by a taxonomist, to evaluate their method. They compared their cell detection method with seven popular saliency detection methods and their cell segmentation method with six commonly used segmentation methods. The quantitative comparison validates that their method performs better on cell detection in terms of robustness and uniformity and cell segmentation in terms of accuracy and completeness. The qualitative results show that their improved saliency detection method can detect and highlight all cells, and the following marker selection scheme can remove the corner noise caused by illumination, the small noise caused by specks, and debris, as well as deal with blurred edges.

Automatic plankton image classification combining multiple view features via multiple kernel learning

BackgroundPlankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap.ResultsInspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness.ConclusionsThis study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.

Feeble object detection of underwater images through LSR with delay loop

Feeble object detection is a long-standing problem in vision based underwater exploration work. However, because of the complicated light propagation situation and high background noise, underwater images are highly degraded. Noise is not always detrimental. Logical stochastic resonance (LSR) can be a useful tool for amplifying feeble signals by utilizing the constructive interplay of noise and a nonlinear system. In the present study, an appropriate LSR structure with a delay loop is proposed to process a low-quality underwater image for enhancing the vision detection accuracy of underwater feeble objects. Ocean experiments are conducted to demonstrate the effectiveness of the proposed structure. We also give explicit numerical results to illustrate the relationship between the structure of LSR and the correct detection probability. Methods presented in this paper are quite general and can thus be potentially extended to other applications for obtaining better performance.

Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

A Hybrid Convolutional Neural Network for Plankton Classification

Plankton are fundamental and essential to marine ecosystem, and its survey is significant for sustainable development and ecosystem balance of oceans. The large amount of plankton species and complex relationship among different classes bring difficulty for us to design an automatic plankton classification system. Thus, we develop our model based on convolutional neural network and aim to overcome these shortages. We consider two different ways to extract global and local features to describe shape and texture information of plankton. Furthermore, we design a pyramid fully connected structure to merge different inner products from each sub networks. The experimental results prove our model can take advantage of multiple features and performs better than original convolutional neural network.

Unsupervised pixel-wise classification for Chaetoceros image segmentation

Abstract Chaetoceros is a dominant genus of marine planktonic diatoms with worldwide distribution. Due to the difficulty of extracting setae from Chaetoceros images, automatic segmentation of Chaetoceros is still a challenging task. In this paper, we address this difficult task by regarding the whole segmentation process as unsupervised pixel-wise classification without human participation. First, we automatically produce positive (object) and negative (background) samples for follow-up training, by combining the advantages of two image processing algorithms: Grayscale Surface Direction Angle Model (GSDAM) for extracting setae information and Canny for detecting cell edges from low-contrast and strong-noisy microscopic images. Second, we develop pixel-wise training by using the produced samples in the training process of Deep Convolutional Neural Network (DCNN). At last, the trained DCNN is used to label other pixels into object and background for final segmentation. We compare our method with eight mainstream segmentation approaches: Otsu’s thresholding, Canny, Watershed, Mean Shift, gPb-owt-ucm, Normalized Cut, Efficient Graph-based method and GSDAM. To objectively evaluate segmentation results, we apply six well-known evaluation indexes. Experimental results on a new Chaetoceros image dataset with human labelled ground truth show that our method outperforms the eight mainstream segmentation methods in terms of both quantitative and qualitative evaluation.

Depth map prediction from a single image with generative adversarial nets

A depth map is a fundamental component of 3D construction. Depth map prediction from a single image is a challenging task in computer vision. In this paper, we consider the depth prediction as an image-to-image task and propose an adversarial convolutional architecture called the Depth Generative Adversarial Network (DepthGAN) for depth prediction. To enhance the image translation ability, we take advantage of a Fully Convolutional Residual Network (FCRN) and combine it with a generative adversarial network, which has shown remarkable achievements in image-to-image tasks. We also present a new loss function including the scale-invariant (SI) error and the structural similarity (SSIM) loss function to improve our model and to output a high-quality depth map. Experiments show that the DepthGAN performs better in monocular depth prediction than the current best method on the NYU Depth v2 dataset.

Simulation of stochastic resonance in underwater laser communication

Due to the light attenuation caused by absorption and scattering of sea water, the performance of underwater optical communication system is limited. Usually, the optical signals will be decayed severely in hundreds of meters and the waveforms are submerged in noise. To improve the signal-to-noise ratio (SNR), for traditional methods, most of the noise can be suppressed by filters based on classic linear theory. However, the signal will also be relatively attenuated by the filters. In this research, the nonlinear signal processing method, named as stochastic resonance (SR), is introduced to enhance the underwater laser communication signal. A SR system is simulated to evaluate the feasibility and effectiveness of weak signal detection in underwater laser communication. The influence of the noise to the bit-error-rate(BER) and the parameter optimization are simulated and discussed. It is proved that, the SR system has advantage over the low-pass filter(LPF) and has promising prospect in the signal enhancement in underwater laser communication.