Edge Texture Research Articles

A Robust Biometric Image Texture Descripting Approach

The huge demand in online transactions calls for a secure, safe and accurate authentication system .The biometric system such as face, iris, fingerprint, gait has already replaced the existing manual inspection process and surveillance systems in many disciplines. Amongst all these biometrics, face is more attractive as it provides information such as identity, expression, gender, ethnicity and age of an individual.Specially for surveillance purposes,the data acquisition for face is much more simpler and can be obtained without the subjects knowledge and cooperation (simply by installing camera in public areas) when compared to fingerprints and iris data accumulation.In this paper an edge texture feature using different weight mask coding is utilized for face recognition.Five different sign and difference operators named LSH LC, LSH GC,LDH LC, LDH GC and LSH LGV are developed and used to code the image texture feature.Each image is decomposed into four subset images which are used to generate the texture features. A decision fusion technique is then used for feature classification. The main advantage of this approach lies in the fact that they are computationally inexpensive when compared to most texture descriptors.The feature descriptor is applied for face biometric recognition to demonstrate the effectiveness of each approach in extracting textural features.It can also be tested with medical images or other pattern recognition applications.The dataset used for training and testing have considerable variances in lighting,viewpoint and other factors so that the potential of the feature extractor, when subjected to any kind of variations, can be judged.

Local Stereo Matching Using Adaptive Local Segmentation

We propose a new dense local stereo matching framework for gray-level images based on an adaptive local segmentation using a dynamic threshold. We define a new validity domain of the frontoparallel assumption based on the local intensity variations in the 4 neighborhoods of the matching pixel. The preprocessing step smoothes low-textured areas and sharpens texture edges, whereas the postprocessing step detects and recovers occluded and unreliable disparities. The algorithm achieves high stereo reconstruction quality in regions with uniform intensities as well as in textured regions. The algorithm is robust against local radiometrical differences and successfully recovers disparities around the objects edges, disparities of thin objects, and the disparities of the occluded region. Moreover, our algorithm intrinsically prevents errors caused by occlusion to propagate into nonoccluded regions. It has only a small number of parameters. The performance of our algorithm is evaluated on the Middlebury test bed stereo images. It ranks highly on the evaluation list outperforming many local and global stereo algorithms using color images. Among the local algorithms relying on the frontoparallel assumption, our algorithm is the best-ranked algorithm. We also demonstrate that our algorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face.

Frequency domain filtering of gradient image for contour detection

A novel computational step, frequency-domain filtering of gradient image, is proposed to improve detection of object contours and region boundaries in natural scenes for gradient-based edge detectors. The step is inspired by analyzing the spectrum distribution of object contours and texture edges in the frequency domain of gradient image. We illustrate the principle and effect of the proposed step by adding it to the Canny edge detector. The resulting operator can selectively retain object contours, and meanwhile can dramatically reduce non-meaningful elements resulting from a texture background. Experimental results demonstrate the effectiveness of the proposed additional step.

Denoising and Enhancement of Magnetic Resonance Image Based on the Dyadic Wavelet Analysis

This paper is applied non-linear operators for image enhancing. The efficient algorithm of MRI enhancement is provided by decomposition of localized information at different spatial frequencies. So the MRI edge textures was enhanced while suppresses the noise maximally. Experiment was show more obvious unseen or barely seen features, and the visual effect is improved after the method, and help for diagnosing the state of an illness.

Unsupervised parameter estimation based on structure detection and clustering for polarimetric SAR speckle filtering

A novel parameter estimation method for polarimetric synthetic aperture radar (PolSAR) speckle removal was introduced, which is a combination of adaptive structure detection and K-means clustering. First, an effective geometrical ratio detection approach was used to define training vectors. Based on the textural characteristic, the vectors were then classified into four types, i.e., homogeneous or slightly textured area, edge, line, and strong scatterer by clustering. These clustering results were then applied to estimate filter parameters. The effectiveness of the proposed method was demonstrated using NASA SIR-C/X-SAR, L-band, four-look PolSAR data of Tian Mountain, China. Finally, the multilook polarimetric whitening filtering algorithm was also used for comparing this method with three baseline ones, i.e., the Boxcar filter, the Prewitt edge detector, and the geometrical ratio detection method. Experimental results showed the superior performance of our proposed method on filter parameter estimation.

An improved model for surround suppression by steerable filters and multilevel inhibition with application to contour detection

Psychophysical and neurophysiological evidence about the human visual system shows the existence of a mechanism, called surround suppression, which inhibits the response of an edge in the presence of other similar edges in the surroundings. A simple computational model of this phenomenon has been previously proposed by us, by introducing an inhibition term that is supposed to be high on texture and low on isolated edges. While such an approach leads to better discrimination between object contours and texture edges w.r.t. methods based on the sole gradient magnitude, it has two drawbacks: first, a phenomenon called self-inhibition occurs, so that the inhibition term is quite high on isolated contours too; previous attempts to overcome self-inhibition result in slow and inelegant algorithms. Second, an input parameter called ''inhibition level'' needs to be introduced, whose value is left to heuristics. The contribution of this paper is two-fold: on one hand, we propose a new model for the inhibition term, based on the theory of steerable filters, to reduce self-inhibition. On the other hand, we introduce a simple method to combine the binary edge maps obtained by different inhibition levels, so that the inhibition level is no longer specified by the user. The proposed approach is validated by a broad range of experimental results.

Contrasting the processes of texture segmentation and discrimination with static and phase-reversing stimuli

Regions of visual texture can be automatically segregated from one another when they abut but also discriminated from one another if they are separated in space or time. A difference in mean orientation between two textures serves to facilitate their segmentation, whereas a difference in orientation variance does not. The present study further supports this notion, by replicating the findings of Wolfson and Landy (1998) in showing that judgments (odd-one-out) made for textures that differ in mean orientation were more accurate (and more rapid) when the textures were abutting than when separated, whereas judgments of variance were made no more accurately for abutting relative to separated textures. Interestingly, however, responses were overall faster for textures differing in variance when they were separated compared to when they were abutting. This is perhaps due to the clear separation boundary, which serves to delineate the regions on which to perform some regional estimation of orientation variance. A second experiment highlights the phase-insensitivity of texture segmentation, in that locating a texture edge (defined by a difference in mean orientation) in high frequency orientation-reversing stimuli can be performed at much higher frequencies than the discrimination of the same regions but with the texture contour masked. Textures that differed in variance did not exhibit this effect. A final experiment demonstrates that the phase-insensitive perception of texture borders improves with eccentric viewing relative to the fovea, whereas perception of the texture regions does not. Together, these experiments show dissociations between edge- and region-based texture analysis mechanisms and suggest a fast, sign-invariant contour extraction system mediating texture segmentation, which may be closely linked to the magnocellular subdivision of visual processing.

TEXTURE ANALYSIS USING LOCAL TEXTURE PATTERNS: A FUZZY LOGIC APPROACH

Texture analysis plays a vital role in image processing. The prospect of texture based image analysis depends on the texture features and the texture model. This paper presents a new texture feature extraction method 'Fuzzy Local Texture Patterns (FLTP)' and 'Fuzzy Pattern Spectrum (FPS)', suitable for texture analysis. The local image texture is described by FLTP and the global image texture is described by FPS. The proposed method is tested with texture classification, texture segmentation and texture edge detection. The results show that the proposed method provides a very good and robust performance for texture analysis.

Image Retrieval with Shape Features Extracted using Morphological Operators with BTC

ABSTRACT The paper discusses novel image retrieval methods based on edge texture of images extracted using morphological operators. The existing CBIR techniques are based on the feature vectors extracted from morphological edge extraction techniques such as simple morphological edge extraction technique, Top-Hat transform and Bottom-Hat transform. The proposed CBIR techniques are using the morphological edge extraction techniques with block truncation coding (BTC). The proposed techniques are tested on generic image database with 1000 images spread across 11 categories. In all 55 queries (5 from each category) are fired on the image database. The average precision and recall of all queries are computed and considered for performance analysis. The experimental results show that use of BTC over morphological shape images for feature extraction improves the performance of image retrieval with reduced computational complexity for query execution. In all BTC with simple morphological edge extraction based CBIR method (SMBTC) gives best performance.

Hybrid Rugosity Mesostructures (HRMs) for fast and accurate rendering of fine haptic detail


 
 
 Thehapticrenderingofsurfacemesostructure(finerelieffeatures)indensetrianglemeshesrequires special structures, equipment, and high sampling rates for detailed perception of rugged models. Low cost approaches render haptic texture at the expense of fidelity of perception. We propose a faster method for sur- face haptic rendering using image-based Hybrid Rugosity Mesostructures (HRMs), paired maps with per-face heightfield displacements and normal maps, which are layered on top of a much decimated mesh, effectively adding greater surface detail than actually present in the geometry. The haptic probe’s force response algorithm is modulated using the blended HRM coat to render dense surface features at much lower costs. The proposed method solves typical problems at edge crossings, concave foldings and texture transitions. To prove the well- ness of the approach, a usability testbed framework was built to measure and compare experimental results of haptic rendering approaches in a common set of specially devised meshes, HRMs, and performance tests. Trial results of user testing evaluations show the goodness of the proposed HRM technique, rendering accurate 3D surface detail at high sampling rates, deriving useful modeling and perception thresholds for this technique.
 
 

Edge Texture Based CBIR using Row Mean of Transformed Column Gradient Image

The paper discusses novel image retrieval methods based on edge texture of images extracted using gradient operators and slope magnitude technique with image transforms. Twenty eight variations of proposed image retrieval techniques using seven image transforms and four gradient operators like Roberts, Sobel, Prewitt and Canny are considered here. The proposed image retrieval techniques are tested on generic image database with 1000 images spread across 11 categories. In all 55 queries (5 from each category) are fired on the image database. The average precision and recall of all queries are computed and considered for performance analysis. The various „Mask-Shape-Transform‟ CBIR techniques [1] are compared with each other and each of the proposed „MaskShape-Transform‟ CBIR methods. The „Mask-ShapeTransform‟ is found to be better than the „Mask-Shape‟ Technique. In all Roberts-Kekre-Transform based CBIR gives best performance followed by Sobel-Slant-Transform and Sobel-Hartley-Transform.

A Combined Color, Texture and Edge Features Based Approach for Identification and Classification of Indian Medicinal Plants

This paper presents a method for identification and classification of images of medicinal plants such as herbs, shrubs and trees based on color and texture feature using SVM and neural network classifier. The tribal people in India classify plants according to their medicinal values. In the system of medicine called Ayurveda, identification of medicinal plants is considered an important activity in the preparation of herbal medicines. Ayurveda medicines have become alternate for allopathic medicine. Hence, leveraging technology in automatic identification and classification of medicinal plants has become essential. Plant species belonging to different classes such as Papaya, Neem, Tulasi, Aloe and Garlic are considered in this work. This paper presents edge and color descriptors that have low-dimension, effective and simple. In addition, the rotation invariant texture descriptors namely, directional difference and the gradient histogram are used. These features are obtained from 900 images of medicinal plants and used to train and test the image samples of three classes with SVM and radial basis exact fit neural network (RBENN). The classification accuracies for color, edge texture features are 74% and 80% respectively. The accuracy is improved to 90% with combined color and texture features. The results are encouraging for tree image plants than herbs and shrubs due to distinguishing feature of stem.

Context-based mapping of damaged buildings from high-resolution optical satellite images

In the early stages of post-disaster response, a quick and reliable damage assessment map is essential. As time is a critical factor, automated damage mapping from remotely sensed images is the expected solution to drastically reduce data acquisition and computation time. Recently, high-resolution satellite images, such as QuickBird data, have been in high demand by damage assessment analysts and disaster management practitioners. However, the existing automated mapping approaches hardly accommodate such high-resolution data. This research aims at developing a new context-based automated approach for earthquake damage mapping from high-resolution satellite images. Relevant contextual information (including structure, shape, size, edge texture, spatial relations) describing the damage situation is formulated and up-scaled on a morphological scale-space. Speed optimization is achieved by parallel processing implementation. The developed approach was tested with two QuickBird images acquired on 26 June 2005 and 3 June 2008 over YingXiu town, Sichuan, China, which suffered the devastating 12 May 2008 earthquake. In comparison to the reference, the developed mapping approach could achieve over 80% accuracy for computation of the damage ratio. Future research is planned to test the approach on various disaster cases for both optical and radar images using a grid-computing platform towards a cost-effective damage mapping solution.

Real‐time fur simulation and rendering

AbstractReal‐time simulation of fur is a key element in many virtual reality applications. However, it is still difficult to simulate effects of fur animation in real‐time. In this paper, we propose an interactive fur simulation method, to calculate effects of external forces, such as wind and gravity, and direct manipulation for real‐time interactive animation. The proposed method consists of two layered textures for rendering. The first texture represents volumes of fur, and the second texture covers and laps the edge of the first texture. Our approach unifies these two structures using a shared vertex array to enhance rendering performance. We modify the shared vertex array with external force simulations and render it into the graphics pipeline. The proposed method is applied to virtual fashion design and 3D story telling as an example. Copyright © 2010 John Wiley & Sons, Ltd.

Improved Gabor Filter for Extracting Texture Edge Features in Ultrasound Kidney Images

The diagnosis of urinary tract calculi begins with a focused history of calculi, evolution of its symptoms and the duration. Computer assisted approaches for analyzing the images have increased since the manual interpretation of the image is a time consuming process and susceptible to human errors. The proposed system develops a multi-scale wavelet based Bayesian speckle suppression method for ultrasound kidney images. The logarithmic transform of the original image is analyzed into the multi-scale wavelet domain. The subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions. Bayesian estimators are designed to exploits these statistics. Ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted Kidney segmentation and disease diagnosis applications. Automatic Kidney segmentation from US images, however, remains a challenge due to speckle noise and various other artifacts inherent to US. This paper, design intensity invariant local image phase features, obtained using improved Gabor filter banks, for extracting edge texture features that occur at core and intermediate layer interfaces. The proposed model does the extension of phase symmetry features to modified gabor mode and their use in automatic extraction of kidney edge texture features from US normal and diseased patient images. The system functionality is proved qualitatively and quantitatively through experimentation for synthetic and real data sets. The localization feature value threshold is evaluated with the training samples of US images. The speckle noise error ratio with respect to the standard US image are compared and experimented.

Texture regions are more easily detected than texture edges

Texture regions are more easily detected than texture edges

Video Text Localization and Extraction Based on Variance Projection and Morphology

The text of the video can provide important information for video description and retrieval. First,the paper adopts Maximum Gradient algorithm which is based on the Sobel Gradient operator to detect the edge of the text. Second, with regards to the rich information of the edge of text, variance projection is employed to detect text zones and to filtrate false text zones by calculating the Regional Density. Finally, Image Morphology is employed to treat the edge of the text. It is to get position and color information of pixels of original image and extract text from complex background. A large number of experiments to prove that the algorithm is very effective.

Text Extraction in Complex Color Document Images for Enhanced Readability

Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text with colors of background. Automatic segmentation of foreground text in such document images is very much essential for smooth reading of the document contents either by human or by machine. In this paper we propose a novel approach to extract the foreground text in color document images having complex background. The proposed approach is a hybrid approach which combines connected component and texture feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all possible text edge pixels. Connected component analysis is performed on these edge pixels to identify candidate text regions. Because of background complexity it is also possible that a non-text region may be identified as a text region. This problem is overcome by analyzing the texture features of potential text region corresponding to each connected component. An unsupervised local thresholding is devised to perform foreground segmentation in detected text regions. Finally the text regions which are noisy are identified and reprocessed to further enhance the quality of retrieved foreground. The proposed approach can handle document images with varying background of multiple colors and texture; and foreground text in any color, font, size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of text regions in the source document. Readability of the extracted foreground text is illustrated through Optical character recognition (OCR) in case the text is in English. The proposed approach is compared with some existing methods of foreground separation in document images. Experimental results show that our approach performs better.

New PDE-based methods for image enhancement using SOM and Bayesian inference in various discretization schemes

A novel approach is presented in this paper for improving anisotropic diffusion PDE models, based on the Perona–Malik equation. A solution is proposed from an engineering perspective to adaptively estimate the parameters of the regularizing function in this equation. The goal of such a new adaptive diffusion scheme is to better preserve edges when the anisotropic diffusion PDE models are applied to image enhancement tasks. The proposed adaptive parameter estimation in the anisotropic diffusion PDE model involves self-organizing maps and Bayesian inference to define edge probabilities accurately. The proposed modifications attempt to capture not only simple edges but also difficult textural edges and incorporate their probability in the anisotropic diffusion model. In the context of the application of PDE models to image processing such adaptive schemes are closely related to the discrete image representation problem and the investigation of more suitable discretization algorithms using constraints derived from image processing theory. The proposed adaptive anisotropic diffusion model illustrates these concepts when it is numerically approximated by various discretization schemes in a database of magnetic resonance images (MRI), where it is shown to be efficient in image filtering and restoration applications.

Shape-Direction-Adaptive Lifting-Based Discrete Wavelet Transform for Arbitrarily Shaped Segments in Image Compression

In this paper, a new lifting-based shape-direction-adaptive discrete wavelet transform (SDA-DWT) which can be used for arbitrarily shaped segments is proposed. The SDA-DWT contains three major techniques: the lifting-based DWT, the adaptive directional technique, and the concept of object-based compression in MPEG-4. With SDA-DWT, the number of transformed coefficients is equal to the number of pixels in the arbitrarily shaped segment image, and the spatial correlation across subbands is well preserved. SDA-DWT also can locally adapt its filtering directions according to the texture orientations to improve energy compaction for images containing non-horizontal or non-vertical edge textures. SDA-DWT can be applied to any application that is wavelet based and the lifting technique provides much flexibility for hardware implementation. Experimental results show that, for still object images with rich orientation textures, SDA-DWT outperforms SA-DWT up to 5.88dB in PSNR under 2.15-bpp (bit/object pixel) condition, and reduces the bit-budget up to 28.5% for lossless compression. SDA-DWT also outperforms DA-DWT up to 5.44dB in PSNR under 3.28-bpp condition, and reduces the bit-budget up to 14.0%.