Haiyong Zheng

Application of Connected Morphological Operators to Image Smoothing and Edge Detection of Algae

Connected morphological operators have the virtue of simplifying image while preserving the edge information. Connected morphological operators are used in combination to smooth original algae images which aims to suppress noise and to simplify image. First, area opening is applied to suppress noise and keep the algal body and thin branches which are connected with body. Second, morphological reconstruction from marker and attribute thinning are performed to extract some lost fragments of body contour and acerose spinule. The smoothed algae images preserve the majority of outline information. Thus image smoothing of algae is accompanied with detection of edges simultaneously. Experimental results demonstrate that the composite method proposed in this paper is efficient in noise elimination and edges extracted correspond well with the outlines of algae visually.

ZooplanktoNet: Deep convolutional network for zooplankton classification

Zooplankton are quite significant to the ocean ecosystem for stabilizing balance of the ecosystem and keeping the earth running normally. Considering the significance of zooplantkon, research about zooplankton has caught more and more attentions. And zooplankton recognition has shown great potential for science studies and mearsuring applications. However, manual recognition on zooplankton is labour-intensive and time-consuming, and requires professional knowledge and experiences, which can not scale to large-scale studies. Deep learning approach has achieved remarkable performance in a number of object recognition benchmarks, often achieveing the current best performance on detection or classification tasks and the method demonstrates very promising and plausible results in many applications. In this paper, we explore a deep learning architecture: ZooplanktoNet to classify zoolankton automatically and effectively. The deep network is characterized by capturing more general and representative features than previous predefined feature extraction algorithms in challenging classification. Also, we incorporate some data augmentation to aim at reducing the overfitting for lacking of zooplankton images. And we decide the zooplankton class according to the highest score in the final predictions of ZooplanktoNet. Experimental results demonstrate that ZooplanktoNet can solve the problem effectively with accuracy of 93.7% in zooplankton classification.

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.

Automatic setae segmentation from Chaetoceros microscopic images

A novel image processing model Grayscale Surface Direction Angle Model (GSDAM) is presented and the algorithm based on GSDAM is developed to segment setae from Chaetoceros microscopic images. The proposed model combines the setae characteristics of the microscopic images with the spatial analysis of image grayscale surface to detect and segment the direction thin and long setae from the low contrast background as well as noise which may make the commonly used segmentation methods invalid. The experimental results show that our algorithm based on GSDAM outperforms the boundary‐based and region‐based segmentation methods Canny edge detector, iterative threshold selection, Otsus thresholding, minimum error thresholding, K‐means clustering, and marker‐controlled watershed on the setae segmentation more accurately and completely. Microsc. Res. Tech. 77:684–690, 2014.

Depth measurement of underwater target based on laser frequency-difference scanning

Underwater 3-D visual technology is one of key technologies in deep sea detection and operation. To acquire underwater depth information of target object is a key point of underwater 3-D visual technology. In this paper, based on active triangular method, we propose a method through laser frequency-difference scanning technology to obtain depth information from 2-D image. We verify the accuracy of the depth information through experiments, which are carried out in air and underwater environments. The results of experiments indicate that the laser difference-frequency scanning technology can get 3-D depth information from 2-D image in high accuracy.

Automatic object detection and segmentation from underwater images via saliency-based region merging

Underwater object detection and segmentation has been attracting a lot of interest, and recently various systems have been designed. In this paper, we introduce a novel technique to automatically detect and segment objects from underwater images via saliency-based region merging. The method is composed of three main steps. Firstly, a salient object detection model is used to detect the position of salient objects in underwater image. Secondly, background prior is applied to determine the approximate background location. Thirdly, the region merging based interactive image segmentation method is improved by adding the determined object and background location information as the user inputs so that the algorithm becomes automatic. The experimental results show that its efficient to segment objects from the underwater image by the proposed method.

An approach of image decomposition for underwater target detection by inhomogeneous illumination based on G-Space and PDE

This paper presents a new approach of image decomposition for underwater target detection by inhomogeneous illumination based on G-Space and Partial Differential Equation( PDE). Underwater target images with high contrast visibility (less back-scattering) can be obtained within the inhomogeneous illumination field which power density is allocated inversely propotional to the rule of the light attenuation in water medium. Then the image f is decomposed into a sum of two functions u + v, where u component is modeled by a function of bounded variation (a cartoon or sketchy approximation of f), while v component representing the texture or noise is modeled by an oscillatory function. In this paper, the Vese-Osher(VO) model and Mumford-Shah-G(MS-G) model based on G-Space and PDE are introduced for texture image extraction. And the experimental results show that its effective to obtain the cartoon and texture components as well as edge component of the underwater targets by MS-G model, which can be applied to further procedures such as image reconstruction and object recognition for underwater target detection.

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.

Underwater Image Restoration via Maximum Attenuation Identification

Underwater images are difficult to process because of low contrast and color distortion. The in-water light propagation model was proposed several years ago but is relatively complicated to be used in reality. In this paper, the full underwater light propagation model is simplified to be used as the transmission model. On the basis of this model, we propose a new method, called maximum attenuation identification, to derive the depth map from degraded underwater images. At the same time, regional background estimation is implemented to ensure optimal performance. Experiments on three groups of images, namely, natural underwater scene, calibration board, and colormap board, are conducted. We report the quantitative and qualitative comparisons of our approach with existing state-of-the-art approaches. The performance evaluation on contrast enhancement and color restoration validates that our approach outperforms existing state-of-the-art approaches.

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.