Steerable Filters Research Articles

Finger Vein Recognition Using Generalized Local Line Binary Pattern

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

Robust multilane detection and tracking in urban scenarios based on LIDAR and mono‐vision

Lane detection and tracking is the basic component of many intelligent vehicle systems. In this study, a robust multilane detection and tracking method is proposed. Using the measurements provided by an in-vehicle mono-camera and a forward-looking LIDAR, this algorithm can address challenging scenarios in real urban driving situations. The proposed approach makes use of steerable filters for lane feature detection, LIDAR-based image drivable space segmentation for lane marking points validations and the RANdom SAmple Consensus technique for robust lane model fitting. To improve the robustness of the fitting further, the parallel lanes hypothesis is introduced. The detected lanes initialise particle filters for tracking, without knowing the ego-motion information. The image processing procedures are carried out in inverse perspective mapping image, because of its convenience for multilane detection. Experimental results indicate that the algorithm in this study has robustness against various driving situations.

Feature integration of EODH and Color-SIFT: Application to image retrieval based on codebook

This paper proposes a new feature descriptor, edge orientation difference histogram (EODH) descriptor, which is a rotation-invariant and scale-invariant feature representation. The main orientation of each edge pixel is obtained through steerable filter and vector sum. Based on the main orientation, we construct the EODH descriptor for each edge pixel. Finally, we integrate the EODH and Color-SIFT descriptor, and build an effective image retrieval system based on weighted codeword distribution using the integrated feature descriptor. Experiments show that the codebook-based image retrieval method achieves the best performance on the given benchmark problems comparing to the state-of-the-art methods.

Image Denoising Using Total Variation Model Guided by Steerable Filter

We propose an adaptive total variation (TV) model by introducing the steerable filter into the TV-based diffusion process for image filtering. The local energy measured by the steerable filter can effectively characterize the object edges and ramp regions and guide the TV-based diffusion process so that the new model behaves like the TV model at edges and leads to linear diffusion in flat and ramp regions. This way, the proposed model can provide a better image processing tool which enables noise removal, edge-preserving, and staircase suppression.

Automated analysis of morphological and synaptic characteristics in neurons

Event Abstract Back to Event Automated analysis of morphological and synaptic characteristics in neurons Johannes Hjorth1* and Sabine Schmitz1 1 Vrije Universiteit Amsterdam, UK Differences in morphology and distribution of synapses and proteins have implications for the function of neurons. Manual quantification of protein expression in immuno-fluorescence images is both time consuming and prone to observer bias. Here we present SynD, a MATLAB based program, developed to automatically analyze dendrite and synapse characteristics in immuno-fluorentscence images. The program uses a combination of steerable filters and deconvolution to detect dendrites and synapses. It reports dendrite morphology, synapse size, number and distribution, as well as protein expression at the soma, at synapses and as a function of distance from the soma. The dendritic tree is characterized using Sholl analysis. The automatic measures have been compared to manually traced neurons and synapses showing good accuracy. Results are exported as csv files readable by excel and to user-friendly summary figures. Reference: Automated analysis of neuronal morphology, synapse number and synaptic recruitment. Schmitz SK, Hjorth JJJ, et al 2011, J Neurosci Methods 195(2):185-93 doi: http://dx.doi.org/10.1016/j.jneumeth.2010.12.011 Keywords: Neuroimaging, immuno-fluorescence images, Neuroscience, SynD, morphology Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Demo Topic: Neuroinformatics Citation: Hjorth J and Schmitz S (2014). Automated analysis of morphological and synaptic characteristics in neurons. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00048 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Johannes Hjorth, Vrije Universiteit Amsterdam, Cambridge, UK, hjorth@kth.se Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Johannes Hjorth Sabine Schmitz Google Johannes Hjorth Sabine Schmitz Google Scholar Johannes Hjorth Sabine Schmitz PubMed Johannes Hjorth Sabine Schmitz Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Classifying Similarity and Defect Fabric Textures based on GLCM and Binary Pattern Schemes

Textures are one of the basic features in visual searching,computational vision and also a general property of any surface having ambiguity. This paper presents a texture classification system which has high tolerance against illumination variation. A Gray Level Co-occurrence Matrix (GLCM) and binary pattern based automated similarity identification and defect detection model is presented. Different features are calculated from both GLCM and binary patterns (LBP, LLBP, and SLBP). Then a new rotation-invariant, scale invariant steerable decomposition filter is applied to filter the four orientation sub bands of the image. The experimental results are evaluated and a comparative analysis has been performed for the four different feature types. Finally the texture is classified by different classifiers (PNN, K-NN and SVM) and the classification performance of each classifier is compared. The experimental results have shown that the proposed method produces more accuracy and better classification accuracy over other methods.

Consistency analysis on orientation features for fast and accurate palmprint identification

A typical palmprint identification system identifies a query palmprint by matching it with the templates in the database and comparing the similarity score with a pre-defined threshold. However, continual database expansion would make conventional brute force search method inefficient for large-scale palmprint identification. In this paper, we propose a consistent orientation pattern (COP) hashing method to enforce fast search. Using the orientation and response features extracted by steerable filter, we first give an analysis on the consistency of orientation features, and then introduce a method to construct COP using the consistent features. Because the COP is very stable across the samples of the same subject, the COP hashing method can find the target template quickly and thus can lead to early termination of the searching process. Experiments have been carried out on several real and synthetic databases, and the results show that the COP hashing method can accelerate the identification process by more than an order of magnitude, and can achieve comparable identification accuracy while compared with the state-of-the-art approaches.

Real-time lane departure warning system based on a single FPGA

Abstract Abstract This paper presents a camera-based lane departure warning system implemented on a field programmable gate array (FPGA) device. The system is used as a driver assistance system, which effectively prevents accidents given that it is endowed with the advantages of FPGA technology, including high performance for digital image processing applications, compactness, and low cost. The main contributions of this work are threefold. (1) An improved vanishing point-based steerable filter is introduced and implemented on an FPGA device. Using the vanishing point to guide the orientation at each pixel, this algorithm works well in complex environments. (2) An improved vanishing point-based parallel Hough transform is proposed. Unlike the traditional Hough transform, our improved version moves the coordinate origin to the estimated vanishing point to reduce storage requirements and enhance detection capability. (3) A prototype based on the FPGA is developed. With improvements in the vanishing point-based steerable filter and vanishing point-based parallel Hough transform, the prototype can be used in complex weather and lighting conditions. Experiments conducted on an evaluation platform and on actual roads illustrate the effective performance of the proposed system.

Counteracting geometrical attacks on robust image watermarking by constructing a deformable pyramid transform

Counteracting geometrical attacks remains one of the most challenging problems in robust watermarking. In this paper, we resist rotation, scaling, and translation (RST) by constructing a kind of deformable pyramid transform (DPT) that is shift-invariant, steerable, and scalable. The DPT is extended from a closed-form polar-separable steerable pyramid transform (SPT). The radial component of the SPT's basis filters is taken as the kernel of the scalable basis filters, and the angular component is used for the steerable basis filters. The shift-invariance is inherited from the SPT by retaining undecimated high-pass and band-pass subbands. Based on the designed DPT, we theoretically derive interpolation functions for steerability and scalability and synchronization mechanisms for translation, rotation, and scaling. By exploiting the preferable characteristics of DPT, we develop a new template-based robust image watermarking scheme that is resilient to RST. Translation invariance is achieved by taking the Fourier magnitude of the cover image as the DPT's input. The resilience to rotation and scaling is obtained using the synchronization mechanisms for rotation and scaling, for which an efficient template-matching algorithm has been devised. Extensive simulations show that the proposed scheme is highly robust to geometrical attacks, such as RST, cropping, and row/column line removal, as well as common signal processing attacks such as JPEG compression, additive white Gaussian noise, and median filtering.

Feature Based Image Mosaic Using Steerable Filters and Harris Corner Detector

Image mosaic is to be combine several views of a scene in to single wide angle view. This paper proposes the feature based image mosaic approach. The mosaic image system includes feature point detection, feature point descriptor extraction and matching. A RANSAC algorithm is applied to eliminate number of mismatches and obtain transformation matrix between the images. The input image is transformed with the correct mapping model for image stitching and same is estimated. In this paper, feature points are detected using steerable filters and Harris, and compared with traditional Harris, KLT, and FAST corner detectors.

Recognition of Amazigh Characters using Visual Rotation Invariant Features

paper, we present a new method for Amazigh Character Recognition. It is based on the extraction of salient points from the Tifinagh characters using a visual detector and descriptor. The novelty of this work is, first, the development of a new detector descriptor having a high repeatability which outperforms other transforms like (SIFT, SURF, Harris), second, its application for the recognition of the Amazigh characters. Our method is called Rotation Invariant Detector (RID). We use steerable filters for representing the image in many scales and orientations, then, the Harris detector applied to each of the obtained images, because of its high speed in the detection operation. In order to recognize a character, we match our image test to the database of learnt characters. We present in this work two applications based on RID: , The visual recognition of handwritten Tifinagh Characters using the paradigm (learning, classification) The conversion of a handwritten character to a typed character with a computer 1. INTRODUCTION computer vision is the science of recognizing objects in the images and interpreting them. During the last decade, this discipline has been widely investigated because of its relation to other fields such as charactere treatment and image retrieval. In order to interpret an image, the analysis has to move from image processing tasks to object recognition and categorization. Consequently, the algorithms which are used should be faster and more complicated. The description of the images can be obtained with predifined kernels to obtain edges. Or by using more recent differential methods to extract

Structural Texture Similarity Metrics for Image Analysis and Retrieval

We develop new metrics for texture similarity that accounts for human visual perception and the stochastic nature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviations between textures that according to human judgment are essentially identical. The proposed metrics extend the ideas of structural similarity and are guided by research in texture analysis-synthesis. They are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in sliding windows. We conduct systematic tests to investigate metric performance in the context of "known-item search," the retrieval of textures that are "identical" to the query texture. This eliminates the need for cumbersome subjective tests, thus enabling comparisons with human performance on a large database. Our experimental results indicate that the proposed metrics outperform peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM) and its variations, as well as state-of-the-art texture classification metrics, using standard statistical measures.

FAST POWER LINE DETECTION AND LOCALIZATION USING STEERABLE FILTER FOR ACTIVE UAV GUIDANCE

Abstract. In this paper we present a fast power line detection and localisation algorithm as well as propose a high-level guidance architecture for active vision-based Unmanned Aerial Vehicle (UAV) guidance. The detection stage is based on steerable filters for edge ridge detection, followed by a line fitting algorithm to refine candidate power lines in images. The guidance architecture assumes an UAV with an onboard Gimbal camera. We first control the position of the Gimbal such that the power line is in the field of view of the camera. Then its pose is used to generate the appropriate control commands such that the aircraft moves and flies above the lines. We present initial experimental results for the detection stage which shows that the proposed algorithm outperforms two state-of-the-art line detection algorithms for power line detection from aerial imagery.

New Families of Fourier Eigenfunctions for Steerable Filtering

A new diadic family of eigenfunctions of the 2-D Fourier transform has been discovered. Specifically, new wavelets are derived by steering the elongated Hermite-Gauss filters with respect to rotations, thus obtaining a natural generalization of the Laguerre-Gauss harmonics. Interestingly, these functions are also proportional to their 2-D Fourier transform. Their analytical expression is provided in a compact and treatable form, by means of a new ad hoc matrix notation in which the cases of even and odd orders of the Hermite polynomials are unified. Moreover, these functions can be efficiently implemented by means of a recursive formula that is derived in this paper. The proposed filters are applied to the problem of gradient estimation to improve the theoretical Canny tradeoff of position accuracy versus noise rejection that occurs in edge detection. Experimental results show considerable improvements in using the new wavelets over both isotropic Gaussian derivatives and other elongated steerable filters more recently introduced. Finally, being the proposed wavelets a set of Fourier eigenfunctions, they can be of interest in other fields of science, such as optics and quantum mechanics.

HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments

A new method (HoGG) for human detection based on Gabor filters and Histograms of Oriented Gradients is presented in this paper. The effect of Gabor preprocessing is analyzed in detail, in particular the improvement experienced by the image's information and the influence exerted over the extracted feature. To compare the performance of the proposed method, several alternative algorithms for human detection have been considered. In order to evaluate these techniques in non-controlled environments, a collection of standard databases, well known in the surveillance research community, has been used: PETS 2006, PETS 2007, PETS 2009 and CAVIAR. An exhaustive test design has been built based on two complementary evaluations: an evaluation oriented to counting people and a novel evaluation oriented to identification. Moreover, with the purpose of studying the performance of the Gabor-based preprocessing, a test adding Gabor filters to other local feature extraction methods, such as Steerable filters and the SIFT method, has been implemented. The HoGG method has achieved a good performance regardless of the difficulty of the images (occlusions, overlapping, carrying baggage, etc.). The proposed method has surpassed the alternative techniques in most of the analyzed situations. When the Gabor preprocessing is introduced into other local feature extraction methods, they achieve a better detection of the relevant information by enhancing the human shape. The results show that using Gabor preprocessing in techniques based on features like orientation or magnitude of gradient improve their performance. Given the excellent results obtained by HoGG at the identification-oriented evaluation, the method presented in this paper should be taken into account in the future design of intelligent surveillance systems.

A pixel-based color image segmentation using support vector machine and fuzzy -means

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a pixel-based color image segmentation using Support Vector Machine (SVM) and Fuzzy C-Means (FCM). Firstly, the pixel-level color feature and texture feature of the image, which is used as input of the SVM model (classifier), are extracted via the local spatial similarity measure model and Steerable filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation can not only take full advantage of the local information of the color image but also the ability of the SVM classifier. Experimental evidence shows that the proposed method has a very effective computational behavior and effectiveness, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.

Multi-scale Steerable Phase-Symmetry Filters for ITK

We have implemented filters for computing phase symmetry (PS) in ITK using steerable filters computed and applied in the Fourier domain. In some recent publications, phase symmetry has been shown to be a useful pre-processing measure for performing image registration and segmentation. Matlab implementations of the code are currently available, but may be slower than C++ versions and most importantly, do not work with volumetric (3D) data. In this paper we briefly provide background information and list some applications of PS filters from the literature. We then provide instructions and examples of how to use the classes and executables.

Design and Implementation of Multisteerable Matched Filters

Image analysis problems such as feature tracking, edge detection, image enhancement, or texture analysis require thedetection of multi-oriented patterns which can appear at arbitrary orientations. Direct rotated matched filtering for feature detection is computationally expensive, but can be sped up with steerable filters. So far, steerable filter approaches were limited to only one direction. Many important low-level image features are, however, characterized by more than a single orientation. We therefore present here a framework for efficiently detecting specific multi-oriented patterns with arbitrary orientations in grayscale images. The core idea is to construct multisteerable filters by appropriate combinations of single-steerable filters. We exploit that steerable filters are closed under addition and multiplication. This allows to derive a design guide for multisteerable filters by means of multivariate polynomials. Furthermore, we describe an efficient implementation scheme and discuss the use of weighting functions to reduce angular oscillations. Applications in camera calibration, junction analysis of images from plant roots, and the discrimination of L, T, and X-junctions demonstrate the potential of this approach.

An Efficient Algorithm for Focus Measure Computation in Constant Time

This letter presents an efficient algorithm for focus measure computation, in constant time, to estimate depth map using image sequences acquired at varying focus. Two major factors that complicate focus measure computation include neighborhood support and gradient detection for oriented intensity variations. We present a distinct focus measure based on steerable filters that is invariant to neighborhood size and accomplishes fast depth map estimation at a considerably faster speed compared to other well-documented methods. Steerable filters represent architecture to synthesize filters of arbitrary orientation using a linear combination of basis filters. Such synthesis is helpful to analytically determine the filter output as a function of orientation. Steerable filters remove inherent limitations of traditional gradient detection techniques which perform inadequately for oriented intensity variations and low textured regions.

Texture Segmentation Using Active Contours Driven by Local Steerable Features

Oriented filters are useful in many computer vision and image processing tasks. It is a kind of oriented filters, rotated very efficiently by taking a suitable linear combination of basis filters. Local steerable feature (LSF), which captures local structure corresponding to any orientation, is distinctive enough to provide useful identity orientation information. This paper investigates novel LSF-guided active contour approach for texture segmentation. The steerable filters are first employed to produce a new energy image, then, the active contour proposed by Chan-Vese (ACWE) is applied to the energy image. These active contours combine the advantages of both the LSF texture representation and the ACWE model, and yield high quality texture segmentation. The performance of the algorithm on some test images is presented and analyzed.