Multi-scale Gabor Filters Research Articles

M2GF: Multi-Scale and Multi-Directional Gabor Filters for Image Edge Detection

An image edge detection algorithm using multi-directional and multi-scale Gabor filters is proposed in this paper. The main merit of this method is that high edge detection accuracy can be obtained while maintaining noise robustness. The approach proposed in this paper consists of three procedures: firstly, the transformation to the CIE L*a*b* color space, which has a wide shading area and uniform distribution; secondly, under different scales, the edge feature information of the image is extracted from several different directions by Gabor filters, and a new edge strength map is obtained by feature fusion; thirdly, the new fused edge strength map is enhanced with local features, and a noise-resistant image edge detector is obtained under a novel hysteresis threshold calculation. The experiments illustrate that, compared to the methods involved, the designed edge detector outperforms by about 2% to 4%, and also shows competitive performance regarding the ability to handle noise.

A Method of Infrared Small Target Detection in Strong Wind Wave Backlight Conditions

How to accurately detect small targets from the complex maritime environment has been a bottleneck problem. The strong wind-wave backlight conditions (SWWBC) is the most common situation in the process of distress target detection. In order to solve this problem, the main contribution of this paper is to propose a small target detection method suitable for SWWBC. First of all, for the purpose of suppressing the gray value of the background, it is analyzed that some minimum points with the lowest gray value tend to gather in the interior of the small target. As the distance from the extreme point increases, the gray value of the pixel in all directions also increases by the same extent. Therefore, an inverse Gaussian difference (IGD) preprocessing method similar to the distribution of the target pixel value is proposed to suppress the uniform sea wave and intensity of the sky background. So as to achieve the purpose of background suppression. Secondly, according to the feature that the small target tends to “ellipse shape” in both horizontal and vertical directions, a multi-scale and multi-directional Gabor filter is applied to filter out interference without “ellipse shape”. Combined with the inter-scale difference (IsD) operation and iterative normalization operator to process the results of the same direction under different scales, it can further suppress the noise interference, highlight the significance of the target, and fuse the processing results to enrich the target information. Then, according to different texture feature distributions of the target and noise in the multi-scale feature fusion results, a cross-correlation (CC) algorithm is proposed to eliminate noise. Finally, according to the dispersion of the number of extreme points and the significance of the intensity of the small target compared with the sea wave and sky noise, a new peak significance remeasurement method is proposed to highlight the intensity of the target and combined with a binary method to achieve accurate target segmentation. In order to better evaluate the performance index of the proposed method, compared with current state-of-art maritime target detection technologies. The experimental results of multiple image sequence sets confirm that the proposed method has higher accuracy, lower false alarm rate, lower complexity, and higher stability.

An advanced gradient texture feature descriptor based on phase information for infrared and visible image matching

Infrared and visible image matching has many applications in remote sensing, computer vision, military fields, etc. The differences in the many characteristics of infrared images and visible images make a robust feature description vital but difficult. Texture orientation information retains the general properties in refrared and visible images, and multi-scale, and multi-oriented Gabor filters can accurately reveal the texture orientation information. This paper presents a feature descriptor by capturing the phase information between neighboring pixels with Log-Gabor filters. Firstly, the original matching image is enhanced via histogram equalization to emphasize the regions of interest, and the gradient magnitude for each pixel is computed to extract the image profile, which advances the performance of the algorithm significantly. Secondly, multi-scale and multi-oriented Log–Gabor filters are utilized to obtain the angle information for different scales and phases in the neighboring region of each pixel, and the angle information is indexed by computing the maximum of energy, including the magnitude, real part, and imaginary part to generate the marked image in which the histograms of the subregion of the detected keypoints are employed to generate the feature descriptors. Finally, we advocate five evaluation measures for testing the performance of the algorithm. The proposed approach is evaluated with four data sets composed of images obtained in visible light and infrared spectra, and its performance is compared with the performance of the state-of-the-art algorithms: Scale-invariant feature transform(SIFT), Speeded up robust features(SURF), Oriented fast and rotated BRIEF(ORB), the edge-oriented histogram descriptor (EHD), the phase congruency edge-oriented histogram discriptor (PCEHD), and the Log–Gabor histogram descriptor (LGHD). The experimental results indicate that the performance of the proposed approach is higher than that of other state-of-the-art algorithms.

Road Centerline Extraction From VHR Images Using SVM and Multi-Scale Maximum Response Filter

In this work, an integrated framework comprising of pixel-based classification, road network filtering, and multi-scale Gabor filter is proposed to address the various prevailing issues in road centerline extraction from VHR images. The proposed framework is composed of three steps; generation of the initial road map, road network filtering and road centerline extraction. In the first step, pixel-based classification using support vector machine (SVM) is performed on VHR imagery to classify it into the road and non-road classes. In the road network filtering step, to retain the road features in classified imagery, an edge-preserving guided filter is applied and to improve the veracity of road extraction, undesirable components are eliminated by shape feature analysis. Finally, the complete and accurate road centerline are obtained by combining multi-scale Gabor filter and fast parallel thinning algorithm. The maximum response from a multi-scale Gabor filter is not only used for extraction but also used for reconstructing the broken road network, which is due to occlusions and artifacts. The proposed framework is implemented on publically available VHR images dataset. The simulation outcomes reflect the dominance of the proposed framework on different quantitative evaluation parameters as compared to up-to-date methods.

Controlled differential evolution based detection of neovascularization on optic disc using support vector machine.

The severe stage of Diabetic Retinopathy (DR) is characterized by the growth of new blood vessels which is called Neovascularization (NV). The abnormally grown blood vessels on the disc are breakable in nature thus the patient is at high risk of sudden blindness. Therefore, the significance of early and accurate detection of Neovascularization on Disc (NVD) should not be neglected. This paper presents an automatic detection of the optic disc using a Controlled Differential Evolution (CDE) algorithm. Further, the Region of Interest (ROI) is created automatically by extending the extreme boundaries of the optic disc by 100 pixels to ensure the presence of NV around the optic disc also. From the ROI so created, blood vessels are segmented using multi-scale Gabor filtering and subsequently, both the morphological and textural features are extracted. Simultaneously, statistical features are directly extracted from the earlier created ROI. Finally, the fundus image is classified by a Support Vector Machine (SVM) using the extracted features from all three feature sets. From each individual image, 16 features are extracted and the feature dimension is reduced to 13 using a sequential backward feature (SBF) selection algorithm. The optimal features are obtained from a total of 205 fundus images, which consists of 99 NVD positive and 106 NVD negative images. This paper attains an average accuracy of 98.75%, the specificity of 100%, the sensitivity of 97.8%, and area under the curve (AUC) as 100% when tested over image selected randomly.

An improved fast segmentation algorithm for text and graphics data

The application of video and image segmentation is carried out from the aspects of improving the accuracy of segmentation and reducing the calculation time, but the segmentation result is affected by the initial curve position, so this paper proposes a new method. As an important part of the Internet, pictures are usually used to help visitors understand. The image contains a lot of deep-level video information, which is an important basis for video content retrieval and data analysis. In this paper, combining the texture and edge features of the image in the process of text location, a multi-scale Gabor filter bank is proposed to transform the original image, and a priori knowledge of the text region is used to process the non-text object in the transform result. In the part of extracting text from pictures, and improved TF-IDF algorithm, BC-TF-IDF algorithm, is proposed to extract text from pictures. To ensure the integrity of the extracted image, the Sobel algorithm is used to process the image in the edge extraction step. Finally, the above method is applied to the Weibo network, and a system of collecting and recognizing the character content of the Weibo image is set up, which completes the function of collecting and gradually recognizing the Weibo image, and verifies the proposed localization method.

DNN Based Moth Search Optimization for Video Forgery Detection

Recently, video tampering process becomes easier due to the rapid advancements in user-friendly editing software and multimedia technology (e.g., Mokey by Imagineer Systems, and Photoshop and Premiere by Adobe). This technologies may highly tamper the original videos, so that the audience gets mislead. Nowadays, MPEG-4 codec is included in a large proportions of video cameras and surveillance systems. Therefore the double compression detection process included as an initial step in the video forensic is receiving a high significance. In this paper, the double compression artifacts is detected by adopting the Markov based features, which identifies the interpolated original videos. The double compressed frames are then segmented by introducing an SLIC super pixel segmentation technique. Here, the feature extraction is performed by applying the scale information that is obtained from the multi-scale Gabor filters. The features of this Gabor scale accurately extract the structural features and also reduce too much of redundancy. This extracted features are then provided to DNN (deep neural network) for forgery detection. In this video forensic process, DNN classifier is included for forgery detection. The CNN classifier is included in various existing forgery detection techniques. But, in our work we include DNN because it contains number of hidden layers which provide accurate results for this forgery detection process. To improve the DNN performance, Moth Search Optimization (MSO) algorithm is introduced in this forgery detection technique. Every nook and corner of this world we can able to find the surveillance cameras for security purpose. But, some fraudsters perform forgeries in this recorded videos for their own benefits. To identify this, a lot of forgery detection techniques are coming into existence. So in this work, we introduce the DNN based MSO to perform the forgery detection in videos.This implementation is processed in python simulation platform. The parametric evaluations are taken in terms of F1-Score, average accuracy, Precision, Recall and. Experimental results will provide improved performance in video forgery detection.

Interest point detection method based on multi‐scale Gabor filters

In this study, a novel interest point detection algorithm which combines image intensity variation and edge contour information is proposed. Firstly, the Canny edge contour detector is used to extract the edge map. Secondly, the imaginary parts of multi-scale Gabor filters are applied to smooth input image and then the normalised information entropies at various scales can be acquired. Finally, multiplication of different normalised information entropies will be served as a new measure for interest point that can also be used for interest point detection. This method has two advantages: on the one hand, detection accuracy is greatly improved because combination information is adopted to extract interest points including contour shape information, grey variation of edge pixels and their neighbours; on the other hand, non-interest points can be well inhabited due to multi-scale product in detector is served as an interest point measure. Also, desirable noise robustness and time efficiency are validated through experiments. Compared with four other state-of-art methods, the proposed method shows excellent performance in terms of geometric transformations and localisation accuracy of repeated interest points.

Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment

Biologically inspired model (BIM) for image recognition is a robust computational architecture, which has attracted widespread attention. BIM can be described as a four-layer structure based on the mechanisms of the visual cortex. Although the performance of BIM for image recognition is robust, it takes the randomly selected ways for the patch selection, which is sightless, and results in heavy computing burden. To address this issue, we propose a novel patch selection method with oriented Gaussian–Hermite moment (PSGHM), and we enhanced the BIM based on the proposed PSGHM, named as PBIM. In contrast to the conventional BIM which adopts the random method to select patches within the feature representation layers processed by multi-scale Gabor filter banks, the proposed PBIM takes the PSGHM way to extract a small number of representation features while offering promising distinctiveness. To show the effectiveness of the proposed PBIM, experimental studies on object categorization are conducted on the CalTech05, TU Darmstadt (TUD) and GRAZ01 databases. Experimental results demonstrate that the performance of PBIM is a significant improvement on that of the conventional BIM.

A machine learning attack against variable-length Chinese character CAPTCHAs

CAPTCHA (Completely Automated Public Turing test to tell Computer and Human Apart) is widely used as a standard security mechanism to protect resources on websites. Among various kinds of CAPTCHAs, the text-based CAPTCHA is the most popular scheme, which consists of English letters, Arabic digits and other character sets, such as Chinese characters. Due to the large quantity of Chinese characters and complicated character structure, it is difficult for bots to crack Chinese character CAPTCHAs. Thus, Chinese character CAPTCHAs have been widely applied in China. Nevertheless, effective offensive approaches are necessary to help CAPTCHA designers find security vulnerabilities to improve defense mechanisms. To deal with variable-length Chinese character CAPTCHAs with noises, an automatic attacking approach is proposed, which includes preprocessing, character segmentation and character recognition. For character recognition, two methods are proposed: MGLCR (Multi-scale Gabor and Logistic regression based CAPTCHA Recognition) and CCR (Convolutional neural network based CAPTCHA Recognition). MGLCR extracts features by multi-scale Gabor filters and classifies characters with logistic regression. CCR extracts features and recognize characters automatically with CNN (Convolutional Neural Network). Experimental results show that the proposed approaches are efficient in attacking variable-length Chinese character CAPTCHAs with noises. The pros and cons of proposed MGLCR and CCR methods are discussed, which outperform state-of-the-art methods. Besides, the proposed methods could achieve satisfactory results in breaking the mixed character CAPTCHAs which consist of English letters, Arabic digits, Chinese characters and mathematical operators.

Multiscale road centerlines extraction from high-resolution aerial imagery

Accurate road extraction from high-resolution aerial imagery has many applications such as urban planning and vehicle navigation system. The common road extraction methods are based on classification algorithm, which needs to design robust handcrafted features for road. However, designing such features is difficult. For the road centerlines extraction problem, the existing algorithms have some limitations, such as spurs, time consuming. To address the above issues to some extent, we introduce the feature learning based on deep learning to extract robust features automatically, and present a method to extract road centerlines based on multiscale Gabor filters and multiple directional non-maximum suppression. The proposed algorithm consists of the following four steps. Firstly, the aerial imagery is classified by a pixel-wise classifier based on convolutional neural network (CNN). Specifically, CNN is used to learn features from raw data automatically, especially the structural features. Then, edge-preserving filtering is conducted on the resulting classification map, with the original imagery serving as the guidance image. It is exploited to preserve the edges and the details of the road. After that, we do some post-processing based on shape features to extract more reliable roads. Finally, multiscale Gabor filters and multiple directional non-maximum suppression are integrated to get a complete and accurate road network. Experimental results show that the proposed method can achieve comparable or higher quantitative results, as well as more satisfactory visual performance.

Improving performance of coronary artery segmentation using calculated vessel location from the angiogram

Vessel segmentation in X-Ray Coronary Angiography (XCA) images, which is used for the diagnosis and treatment of anomalies in coronary vessels, has been an intriguing topic in recent years. Due to dynamic background and ambiguous brightness distribution, success rate in segmentation is low and realization has been a challenging process. This paper presents a new approach to increase the success rate of segmentation in XCA images. For this purpose, the vessel structure position is first determined with foreground detection algorithm in the XCA image sequences. Next, state of the art methods of vessel identification and image thresholding used for vessel structure segmentation are being experimented in full and foreground images. The contribution of the suggested method to the success of the segmentation is revealed with the experiments carried out. The scales of sensitivity, specificity and accuracy are given in the paper to evaluate the success of the segmentation. The results were compared with the angiography images segmented manually by a specialist physician. Without proposed detection of candidate vessel location, highest accuracy results 92.4 is achieved with Multiscale Gabor filter and PTile thresholding. Frangi Filter and Otsu thresholding algorithm reached the highest success rate of 93.81 by using the proposed method. All the experiments have shown that accuracy of vessel segmentation is increase by the proposed approach.

Retinal Blood Vessel Segmentation Using Extreme Learning Machine

Extreme learning machine (ELM) is an effective machine learning technique that widely used in image processing. In this paper, a new supervised method for segmenting blood vessels in retinal images is proposed based on the ELM classifier. The proposed algorithm first constructs a 7-D feature vector using multi-scale Gabor filter, Hessian matrix and bottom-hat transformation. Then, an ELM classifier is trained on gold standard examples of vessel segmentation images to classify previous unseen images. The algorithm was tested on the publicly available DRIVE database – a digital image database for vessel extraction. Experimental results on both real-captured images and public database images demonstrate that our method shows comparative performance against other methods, which make the proposed algorithm a suitable tool for automated retinal image analysis.

Self-Adaptive Correction of Heading Direction in Stair Climbing for Tracked Mobile Robots Using Visual Servoing Approach

In this paper, we describe a heading direction correction algorithm for a tracked mobile robot. To save hardware resources as far as possible, the mobile robot’s wrist camera is used as the only sensor, which is rotated to face stairs. An ensemble heading deviation detector is proposed to help the mobile robot correct its heading direction. To improve the generalization ability, a multi-scale Gabor filter is used to process the input image previously. Final deviation result is acquired by applying the majority vote strategy on all the classifiers’ results. The experimental results show that our detector is able to enable the mobile robot to correct its heading direction adaptively while it is climbing the stairs.

Automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization

Abstract This paper presents a new method consisting of two stages for automatic detection and segmentation of coronary arteries in X-ray angiograms. In the first stage, multiscale Gabor filters are used to detect vessel structures in the angiograms. The results of multiscale Gabor filtering are compared with those obtained by applying multiscale methods based on the top-hat operator, Hessian matrix, and Gaussian matched filters. The performance of the vessel-detection methods is evaluated through the area ( A z ) under the receiver operating characteristic (ROC) curve. In the second stage, coronary arteries are segmented by binarizing the magnitude response of Gabor filters using a new thresholding method based on multiobjective optimization, which is compared with seven thresholding methods. Measures of sensitivity, specificity, accuracy, and positive predictive value are used to analyze the segmentation methods, by comparing the results to the ground-truth markings of the vessels drawn by a specialist. Finally, the proposed method is compared with seven state-of-the-art vessel segmentation methods. The result of vessel detection using multiscale Gabor filters demonstrated high accuracy with A z = 0.961 with a training set of 40 angiograms and A z = 0.952 with an independent test set of 40 angiograms. The results of vessel segmentation with the multiobjective thresholding method provided an average accuracy of 0.881 with the test set of angiograms.

Iris Recognition Method Based on Feature Discrimination and Category Correlation

With the increasing number of internet user, the authentication technology is more and more important. Iris recognition as an important method for identification, which has been attention by researchers. In order to improve the predictive accuracy of iris recognition algorithm, the iris recognition method is proposed based feature discrimination and category correlation. The feature discrimination and category correlation are calculated by laplacian score and mutual information. The formula about feature discrimination and category correlation are built. Aiming at texture characteristic of iris image, the multi-scale circular Gabor filter is used to feature extraction. The computational efficiency of algorithm is improved. In order to verify the validity of the algorithm, the CASIA iris database of Chinese Academy of Sciences is used to do the experiment. The experimental results show that our method has high predictive accuracy.

Semi-Automatic Extraction Method for Low Contrast Road Based on Gabor Filter and Simulated Annealing

High-resolution satellite remote sensing image are mostly used for accurate updating of GIS data. As the primary GIS data, urban roads on the image show the rich geometric features and radiation characteristics, that edge detection and grouping becoming an important way to solve the road extraction. However, edge elements obtained from images are always discontinuous for interference of noise and weak contrast between road and background. What more, vehicles, plant, buildings and shadow blocking results in weak grouping relation of elements. In processing, insignificant candidate road may be weeded out as noise and lead to failure road extraction. This paper presents a semi-automated extraction method for low contrast road basing on statistical grouping of orientation texture feature. Multi-direction and multi-scale Gabor filters are employed to detect directions of road texture. Then same direction pixels are grouped under constraining of rectangle template and generate road base elements. Finally, simulated annealing algorithm is used to optimize elements connection. Experiment results show that proposed method was effective in accurate extraction of low contrast road.

Multi-Layer and Adaptive Edge Detection Method Based on Multi-Scale Gabor Wavelets

Recent years, the common adaptive edge detection algorithms use merely the global character of images, and the local character is ignored. The lack of image information always leads these algorithms are difficult to detect the low contrast edges and sensitive to asymmetric illumination. A novel adaptive edge detection algorithm is proposed to improve the edge detection performance. First, the original image is filtered to obtain the edge response image by introducing multi-scale Gabor filters. Second, non-maxima suppression technique is used on the edge response image to get the coarse edge points. Then, the edge response image is delaminated to adaptively select different thresholds towards different pixels of different layers according to pixels local mean and variance values. The fine edge points are got after filtering the edge response image with these thresholds. Finally, combine the coarse edge points and fine edge points together and get the final edge. Experimental results show that the proposed algorithm achieves lower contrast edge detection and robustness to asymmetric illumination, has better edge detection performance.

Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking

BackgroundSegmentation of the coronary angiogram is important in computer-assisted artery motion analysis or reconstruction of 3D vascular structures from a single-plan or biplane angiographic system. Developing fully automated and accurate vessel segmentation algorithms is highly challenging, especially when extracting vascular structures with large variations in image intensities and noise, as well as with variable cross-sections or vascular lesions.MethodsThis paper presents a novel tracking method for automatic segmentation of the coronary artery tree in X-ray angiographic images, based on probabilistic vessel tracking and fuzzy structure pattern inferring. The method is composed of two main steps: preprocessing and tracking. In preprocessing, multiscale Gabor filtering and Hessian matrix analysis were used to enhance and extract vessel features from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In tracking, a seed point was first automatically detected by analyzing the vessel feature map. Subsequently, two operators [e.g., a probabilistic tracking operator (PTO) and a vessel structure pattern detector (SPD)] worked together based on the detected seed point to extract vessel segments or branches one at a time. The local structure pattern was inferred by a multi-feature based fuzzy inferring function employed in the SPD. The identified structure pattern, such as crossing or bifurcation, was used to control the tracking process, for example, to keep tracking the current segment or start tracking a new one, depending on the detected pattern.ResultsBy appropriate integration of these advanced preprocessing and tracking steps, our tracking algorithm is able to extract both vessel axis lines and edge points, as well as measure the arterial diameters in various complicated cases. For example, it can walk across gaps along the longitudinal vessel direction, manage varying vessel curvatures, and adapt to varying vessel widths in situations with arterial stenoses and aneurysms.ConclusionsOur algorithm performs well in terms of robustness, automation, adaptability, and applicability. In particular, the successful development of two novel operators, namely, PTO and SPD, ensures the performance of our algorithm in vessel tracking.

Detection of blood vessels in the retina with multiscale Gabor filters

We propose image processing techniques for the detection of blood vessels in fundus images of the retina. The methods include the design of a bank of directionally sensitive Gabor filters with tunable scale and elongation parameters. Forty images of the retina from the Digital Retinal Images for Vessel Extraction database were used to evaluate the performance of the methods. The results of blood vessel detection using inverted green-channel images were compared with the corresponding manually segmented blood vessels. High efficiency in the detection of blood vessels with the area under the receiver operating characteristics curve of up to 0.96 was achieved with a combination of Gabor filters at three scales.