Multiple Gabor Filters Research Articles

Automated Fabric Defect Detection Based on Multiple Gabor Filters and KPCA

Automated Fabric Defect Detection Based on Multiple Gabor Filters and KPCA

KLT 특징점에 기반한 비접촉 장문인식

비접촉 장문을 인식하기 위해서는 영상의 크기 및 회전 변형을 효과적으로 해결해야 한다. 본 연구에서는 손의 크기와 방향에 따라 관심영역(ROI)을 추출한 후 정규화하여 일차적으로 이러한 변형을 최소화하였다. 본 논문에서는 KLT(Kanade-Lukas-Tomasi) 특징점에 기반한 비접촉 장문인식 방법을 제안한다. 대응되는 KLT 특징점 주위의 국소영역에 대한 텍스처를 비교하여 대응되는 특징점을 검출한 후, 특징점 쌍의 변위 크기와 방향을 나타내는 변위벡터들 간의 유사도를 비교하여 장문을 인식한다. CASIA 공개 데이터베이스를 이용한 실험결과 제안된 방법이 비접촉 장문인식에 효과적임을 확인할 수 있었다. 특히 다중 가버 필터를 이용하였을 때 99%를 상회하는 정인식률을 얻을 수 있었다. An effective solution to the variation on scale and rotation is required to recognize contactless palmprint. In this study, we firstly minimize the variation by extracting a region of interest(ROI) according to the size and orientation of hand and normalizing the ROI. This paper proposes a contactless palmprint recognition method based on KLT(Kanade-Lukas-Tomasi) feature points. To detect corresponding feature points, texture in local regions around KLT feature points are compared. Then, we recognize palmprint by measuring the similarity among displacement vectors which represent the size and direction of displacement of each pair of corresponding feature points. An experimental results using CASIA public database show that the proposed method is effective in contactless palmprint recognition. Especially, we can get the performance of exceeding 99% correct identification rate using multiple Gabor filters.

New color GPHOG descriptors for object and scene image classification

This paper presents a novel set of image descriptors that encodes information from color, shape, spatial and local features of an image to improve upon the popular Pyramid of Histograms of Oriented Gradients (PHOG) descriptor for object and scene image classification. In particular, a new Gabor-PHOG (GPHOG) image descriptor created by enhancing the local features of an image using multiple Gabor filters is first introduced for feature extraction. Second, a comparative assessment of the classification performance of the GPHOG descriptor is made in grayscale and six different color spaces to further propose two novel color GPHOG descriptors that perform well on different object and scene image categories. Finally, an innovative Fused Color GPHOG (FC---GPHOG) descriptor is presented by integrating the Principal Component Analysis (PCA) features of the GPHOG descriptors in the six color spaces to combine color, shape and local feature information. Feature extraction for the proposed descriptors employs PCA and Enhanced Fisher Model (EFM), and the nearest neighbor rule is used for final classification. Experimental results using the MIT Scene dataset and the Caltech 256 object categories dataset show that the proposed new FC---GPHOG descriptor achieves a classification performance better than or comparable to other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT) based Pyramid Histograms of visual Words descriptor, Color SIFT four Concentric Circles, Spatial Envelope, and Local Binary Patterns.

Palm vein recognition using adaptive Gabor filter

Vein pattern recognition is one of the newest biometric techniques researched today. One of the reliable and robust personal identification authentication approaches using palm vein patterns is presented in this paper. In our work, we consider the palm vein as a piece of texture and apply texture-based feature extraction techniques to palm vein authentication. A Gabor filter provides the optimized resolution in both the spatial and frequency domains, thus it is a basis for extracting local features in the palm vein recognition. However, Gabor filter has many potential parameter combinations to use, and it is a common practice now to use multiple Gabor filters or to determine desired single combination by experience. The overall aim of this work is to discuss the optimization algorithm that determines the best parameter values of a single Gabor filter for palm vein recognition. In order to obtain effective pattern of palm vascular, we proposed an innovative and robust adaptive Gabor filter method to encode the palm vein features in bit string representation. The bit string representation, called VeinCode, offers speedy template matching and enables more effective template storage and retrieval. The similarity of two VeinCodes is measured by normalized Hamming distance. A total of 4140 palm vein images were collected form 207 persons to verify the validity of the proposed palm vein recognition approach. High accuracy has been obtained by the proposed method and the speed of this method is rapid enough for real-time palm vein recognition. Experimental results demonstrate that our proposed approach is feasible and effective in palm vein recognition.

Detection of Architectural Distortion and Analysis of Mammary Gland Structure in Mammograms Using Multiple Gabor Filters

Detection of Architectural Distortion and Analysis of Mammary Gland Structure in Mammograms Using Multiple Gabor Filters

Designing multiple Gabor filters for multitexture image segmentation

We consider the problem of segmenting multitextured images using multiple Gabor lters. In particular, we present a mathematical framework for a multichannel texture-segmentation system consisting of a parallel bank of lter channels, a vector classier stage, and a postprocessing stage. The framework establishes mathematical relationships between the predicted texture-segmentation error, the frequency spectra of constituent textures, and the parameters of the lter channels. The framework also permits the systematic formulation of lter-design procedures and provides predicted vector output statistics that are useful for classier design. This paper focuses on the mathematical framework and provides experimental results that conrm the utility of the framework in the design of a complete image-segmentation system. The results demonstrate eective segmentation using a straightforward classier and fewer than half the number of lters needed in previously proposed approaches. Subject terms: Gabor prelter, Gabor lter, Gabor function, texture segmentation, statistical image analysis, texture analysis, computer vision, image segmentation.

Gabor filter design for multiple texture segmentation

A method is presented for the design of a single Gabor filter for the segmentation of multitextured images. Earlier methods were limited to filters designed for one or two textures or to filters selected from a predetermined filter bank. Our proposed method yields new insight into the design of Gabor filters for segmenting multitextured images and lays an essential foundation for the design of multiple Gabor filters. In the method, Rician statistics of filtered textures at two different Gabor-filter envelope scales are used to efficiently generate probability density estimates for each filtered texture over an extensive set of candidate filter parameters. Variable degrees of postfiltering and the accompanying effect on postfilter output statistics are also included in the design procedure. The result is a unified framework that analytically relates the texture power spectra, Gabor-filter parameters, postfiltering effects, and image-segmentation error. Finally, the resulting filter design is based on all constituent textures and is not constrained to a limited set of candidate filters.

Localized measurement of image fractal dimension using gabor filters

We present a method for making accurate, optimally localized measurements of the fractal dimension of images modeled as locally fractal Brownian surfaces. Fractal Brownian surfaces are good models for the multiscale and irregular image textures arising from natural scenes and for elevation maps of terrain. To obtain accurate local values of the fractal dimension, spatio-spectrally localized measurements are necessary. Our method employs Gabor filters, which optimize the conflicting goals of spatial and spectral localization as constrained by the functional uncertainty principle. The outputs from multiple Gabor filters are fitted to a fractal power-law curve whose parameters determine the fractal dimension. The algorithm produces a local value of the fractal dimension at every point in the image. We also introduce a variational technique for enforcing a smoothness constraint on the computed fractal dimension function. This technique is implemented using an iterative relaxation algorithm. We demonstrate the method on synthetic images, real images of natural textures, and U.S. Geo-Data digital elevation maps of real terrain. We discuss the ways in which real images depart from the fractal Brownian surface model.