Lei Feng

Bio: Lei Feng is an academic researcher from Yanshan University. The author has contributed to research in topics: Algorithm & Artificial intelligence. The author has an hindex of 2, co-authored 3 publications receiving 18 citations.

A Color Image Segmentation Method Based on Region Salient Color and Fuzzy C-Means Algorithm

Abstract: This paper proposes a color image segmentation method based on region salient color and the fuzzy C-means (FCM) algorithm. The method first uses the convex hull theory based on Harris corner detection to detect the object of the image. Thus, the object and the background can be separated. Then, the quantized color histogram can be studied in the HSV color space. By calculating the number of the peak values of both the object and the background histograms, the quantity of the regional salient colors can be obtained. The quantity is the number of the clustering centroids of FCM algorithm. Finally, the FCM algorithm and the noise correction algorithm can be used in the object and the background, respectively. The obtained segmented image consists of the object and the background segmentation. It proves that the method in this paper is an effective segmentation method based on the experiments made by use of Berkeley segmentation dataset. According to the experimental results, it can be concluded that the proposed algorithm has the highest segmentation accuracy and the shortest computing time among the algorithms mentioned in this paper. The algorithm can achieve high-quality, stable and accurate color image segmentation results.

Underwater image enhancement and restoration based on local fusion

Abstract: Underwater imaging and image processing play important roles in oceanic scientific research. However, because the light is absorbed and scattered, the obtained underwater images are seriously degraded. Color distortion, low contrast, and detail (edge information) loss are the major problems of underwater images. We propose a method to solve these problems. First, a local adaptive proportion fusion algorithm is proposed to produce a color-balanced image, which is the first input image. Second, an edge-enhanced image is produced as the second input image. Third, a proportion fusion image is produced as the third input image. Finally, the image formation model-based local triple fusion method is used to merge these three input images and get the final result. Experimental results show that the proposed method could effectively balance color distortion and enhance edges of the degraded images. Subjective and objective evaluations show that our method is superior to many state-of-the-art methods.

Application Analysis of Artificial Intelligence Algorithms in Image Processing

Abstract: Due to the rapid development of information, the image recognition method based on the artificial intelligence algorithm has been applied to many application fields. As the product of modern information technology, the image recognition method based on the artificial intelligence algorithm is also a significant and pioneering research topic. With the development of digital image processing technology and the updating of computer electronic products, digital image processing technology has been applied to various fields and has made great contributions to the progress of science and technology and the development of productivity. Image processing methods cover a wide range of application fields, including image conversion, restoration, separation, enhancement and matching, and classification. In order to explore the application of the AI algorithm in image processing, on the basis of the latest BAS algorithm discovered recently, we have established a brand-new hybrid intelligent algorithm BAS based on C as computing, which is applied to multiple image processing fields and achieves good optimization results. Studying the problems and future development directions in the application is conducive to promoting such development of image recognition technology and is conducive to applying image recognition technology to more fields.

An Improved Saliency Detection Algorithm Based on Edge Boxes and Bayesian Model

Abstract: Visual saliency detection aims to extract salient objects from the original image, making it less complicated to process the image. This paper combines an edge box algorithm with Bayesian theory to detect salient objects. The proposed saliency detection algorithm transforms the process of traditional detection method, and prioritizes the positioning of significant objects. Firstly, the Harris corners of the original image were calculated, and clustered by the improved clustering algorithm, yielding the number of salient objects in the image. Then, all possible positions of salient objects in the image were framed by the edge box algorithm, and the boxes were sorted in descending order of the score. According to the number N of clusters of the image corners, the N top-ranking boxes were selected to determine the salient regions. In this way, the position and number of salient objects were clarified. Based on the selected salient regions, the final saliency map was calculated by improved geodesic distance and Bayesian model. Experimental results show that our approach performed better than 11 existing algorithms in both simple and relatively complex scenes. In terms of objective performance, the accuracy and recall of our algorithm on MSRA10k, ECSSD, DUT-OMRON and SED2 datasets were higher than that of the other algorithms.

Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset

Abstract: Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We also contribute a dataset of 57 images taken in different locations. To obtain ground truth, we placed multiple color charts in the scenes and calculated its 3D structure using stereo imaging. This dataset enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time.

Study on Spectrum Characteristics and Clustering of Acoustic Emission Signals from Rock Fracture

Abstract: Acoustic emission signals are relevant to the process of rock failure. In this paper, acoustic emission waveform signals during rock loading are acquired through uniaxial compression test of granite in laboratory. Short-time Fourier transform is used to analyze the acoustic emission signals during rock fracture to obtain the peak frequency. Based on the peak frequency of acoustic emission, four types of acoustic emission signals are classified by using fuzzy C-means method. The parameters of different types of acoustic emission signals are analyzed, which include ring count, duration, amplitude and energy. Meanwhile, the progressive propagation of surface cracks in rock specimens is quantitatively analyzed by digital image correlation (DIC) technology. The result shows that different types of acoustic emission signals correspond to different strength of rock fracture. Before rock fracture, high-count, long-duration and high-energy precursory characteristic signals appear intensively. The event density $$ {\text{Den}}_{t} = 1 $$ is taken as the early warning threshold of rock fracture through the quantitative analysis of acoustic emission signal. The present results of the research show the usefulness of the DIC and acoustic emission techniques in experiment of that type.

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

Abstract: Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly be used in this scenario. Moreover, wooden boards are long, and their images have a high resolution, which may lead to the growth of computational complexity. To overcome these challenges, in this paper, we propose a new mechanism for color classification of wooden boards based on machine vision. The image of the wooden board is preprocessed to subtract irrelevant colors, and the feature vector is extracted based on 3D color histogram to reduce the computational complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through the K-means algorithm. Then, the clustering result can be used in the online classification to classify the new wood image. Furthermore, to process the abnormal images of wooden boards, we propose an improved algorithm with centroid improvement and image filtering. The experimental results verify the effectiveness of the proposed mechanism.