Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn.

Ghost Detection and Removal Based on Two-Layer Background Model and Histogram Similarity

Detecting and removing ghosts is an important challenge for moving object detection because ghosts will remain forever once formed, leading to the overall detection performance degradation. To deal with this issue, we first classified the ghosts into two categories according to the way they were formed. Then, the sample-based two-layer background model and histogram similarity of ghost areas were proposed to detect and remove the two types of ghosts, respectively. Furthermore, three important parameters in the two-layer model, i.e., the distance threshold, similarity threshold of local binary similarity pattern (LBSP), and time sub-sampling factor, were automatically determined by the spatial-temporal information of each pixel for adapting to the scene change rapidly. The experimental results on the CDnet 2014 dataset demonstrated that our proposed algorithm not only effectively eliminated ghost areas, but was also superior to the state-of-the-art approaches in terms of the overall performance.

A hybrid image similarity measure based on a new combination of different similarity techniques

Image similarity is the degree of how two images are similar or dissimilar. It computes the similarity degree between the intensity patterns in images. A new image similarity measure named (HFEMM) is proposed in this paper. The HFEMM is composed of two phases. Phase 1, a modified histogram similarity measure (HSSIM) is merged with feature similarity measure (FSIM) to get a new measure called (HFM). In phase 2, the resulted (HFM) is merged with error measure (EMM) in order to get a new similarity measure, which is named (HFEMM). Different kindes of noises for example Gaussian, Uniform, and salt & ppepper noiser are used with the proposed methods. One of the human face databases (AT&T) is used in the experiments and random images are used as well. For the evaluation, the similarity percentage under peakk signal to noise ratio (PSNR) is usedd. To show the effectiveness of the proposed measure, a comparision anong different similar technique such as SSIM, HFM, EMM and HFEMM are considered. The proposed HFEMM achieved higher similarity result when PSNR was low compared to the other methods.

Full-Reference Facial Image Quality Assessment and Identification by Two Proposed Measures

In this paper, the goal was to identify a person’s face in the acquired image by the proposed measures. We discuss the appearance of two types of noise together in an image. The acquired facial image quality was also assessed by two proposed measures, the histogram similarity measure and the histogram error mean measure. The histogram structural similarity measure is a previously described modified version of the information-theoretic structural similarity measure. It was merged with the structural similarity measure and the error mean measure, derived from the mean squared error, to get the proposed measures. The first proposed histogram similarity measure consists of merging histogram structural similarity with structural similarity measure, and the second proposed histogram error mean measure consists of merging histogram structural similarity with error mean measure. Finally, many algorithms for identification have recently been proposed to measure the similarity between two images. The results showed that the two proposed measures were better than existing methods. Different noises types (such as white Gaussian, speckle, and salt-and-pepper) are used with the proposed methods. Two facial image datasets were used in this paper. The AT&T database included color images of 92 x 112 pixels (px), and the Faculty of Industrial Engineering database included color images of 480 x 640 px. To evaluate performance and quantify the error, the structural similarity measure, histogram structural similarity, and error mean measure were considered. Noise ratios that depended on a peak signal-to-noise ratio were used in this experiment.

Dimensionality reduction and hierarchical clustering in framework for hyperspectral image segmentation

The hyperspectral data contains hundreds of narrows bands representing the same scene on earth, with each pixel has a continuous reflectance spectrum. The first attempts to analysehyperspectral images were based on techniques that were developed for multispectral images by randomly selecting few spectral channels, usually less than seven. This random selection of bands degrades the performance of segmentation algorithm on hyperspectraldatain terms of accuracies. In this paper, a new framework is designed for the analysis of hyperspectral image by taking the information from all the data channels with dimensionality reduction method using subset selection and hierarchical clustering. A methodology based on subset construction is used for selecting k informative bands from d bands dataset. In this selection, similarity metrics such as Average Pixel Intensity [API], Histogram Similarity [HS], Mutual Information [MI] and Correlation Similarity [CS] are used to create k distinct subsets and from each subset, a single band is selected. The informative bands which are selected are merged into a single image using hierarchical fusion technique. After getting fused image, Hierarchical clustering algorithm is used for segmentation of image. The qualitative and quantitative analysis shows that CS similarity metric in dimensionality reduction algorithm gets high quality segmented image.

Detection of QR-Codes in Digital Images Based on Histogram Similarity

This paper considers the problem of QR codes detection in digital images. In particular, the approach for detection of QR codes is proposed. The approach is based on histogram correlation between the reference image of QR code and the input image. In particular the input image is firstly divided into blocks. These are next used to build binary map of regions similar and dissimilar in terms of histogram to the image of QR code. On the binary map the morphological operations are next applied in order to remove outliers and identify the QR code. The results of applying the introduced approach to various images are presented and discussed. Different lighting conditions, image resolutions and orientations of QR codes are considered.