Image Texture Extraction Research Articles

An Automatic Text Region Positioning Method for the Low-Contrast Image

Text extraction is the key step in the character recognition; its accuracy highly relies on the location of the text region. In this paper, we propose a new method which can find the text location automatically to solve some regional problems such as incomplete, false position or orientation deviation occurred in the low-contrast image text extraction. Firstly, we make some pre-processing for the original image, including color space transform, contrast-limited adaptive histogram equalization, Sobel edge detector, morphological method and eight neighborhood processing method (ENPM) etc., to provide some results to compare the different methods. Secondly, we use the connected component analysis (CCA) method to get several connected parts and non-connected parts, then use the morphology method and CCA again for the non-connected part to erode some noises, obtain another connected and non-connected parts. Thirdly, we compute the edge feature for all connected areas, combine Support Vector Machine (SVM) to classify the real text region, obtain the text location coordinates. Finally, we use the text region coordinate to extract the block including the text, then binarize, cluster and recognize all text information. At last, we calculate the precision rate and recall rate to evaluate the method for more than 200 images. The experiments show that the method we proposed is robust for low-contrast text images with the variations in font size and font color, different language, gloomy environment, etc.

Image Retrival Pada Obyek Lingga Yoni Di Situs Peninggalan Sejarah Trowulan Mojokerto

This study contains about Image Retrieval system image on Lingga Yoni at historical sites Trowulan. In the area Trowulan is a legacy of work Majapahit era where the majority of people it is a Hindu, so many relics found in the form of Linga Yoni which serves as the worship of Lord Shiva. Data retrieval image Yoni Linga Linga Yoni as many as 50 images using a digital camera, and the image size Lingga Yoni 200 x 300 pixels in BMP file format. Stages Image Retrieval system on the study include segmentation stages: (1) Smoothing using the method Pas Low Filter to soften the image of the noise; (2) the extraction step texture by using Region Growing by altering the RGB color image is converted into to facilitate the HSL color groups; (3) Region Region Merging Growing did for the incorporation of color image corresponding to the object Linga Yoni; (4) to get to extraction stage form was originally looking for edge detection using Canny edge; (5) the image is converted into binary form to the morphology using opening and closing. At Stages Image Retrieval 50 Linga Yoni image texture extraction step performed using the 4 corners of each feature value GLCM with Different Inverse Moment IDM to revise the results of Image Retrieval using methods Precision and Recall.

A Novel Texture Extraction Method for Digital Radiography

As known, there always exist severely degradation problems in digital radiography. How we can extract necessary textures from degraded radiographic images is the post-processing key. Local binary pattern (LBP) is a well-known method, which is widely used in fast image texture extraction. However, for noisy images, LBP can’t work well due to its sensitivity to details. On the other hand, as one of the important shock filters developed in recent years, complex shock filter possesses excellent capabilities in textural image processing. Here, by combining complex shock filter with LBP, a novel fast and efficient method, C-LBP is presented for texture extraction of degraded radiographic images. Experimental results show that comparing with traditional LBP, C-LBP not only distinguishes between noise and details in radiographic images, but also extracts image textures efficiently and rapidly, which plays an important role in developing nondestructive detection technique by low-dose ray radiography.

Design and FPGA Implementation of DWT, Image Text Extraction Technique

Abstract In digital image processing, detection and extraction of text from a documentary image is found a challenging task, especially for inclined, vertical and circular text. In the extraction method, the image is segmental in text regions from a compound image. There are many techniques to segment the image but HAAR DWT is one of the popular and fastest methods, because its coefficients are either 1 or -1. HAAR DWT detects 3 kinds of edges for colour images and removes non texted regions. The paper focuses on the design, modeling and simulation of the proposed method with the help of VHDL programming language. The design will be compared with the existing methods, and the speed and existing hardware is optimized in terms of memory and time parameters. The design is developed in Xilinx 14.2 ISE software and is synthesized on Virtex -5 FPGA.

Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture

Urban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover types. In this paper, a new method that combines spectral information and multivariate texture is proposed. The multivariate textures are separately extracted from multispectral data using a multivariate variogram with different distance measures, i.e., Euclidean, Mahalanobis and spectral angle distances. The multivariate textures and the spectral bands are then combined for urban built-up area extraction. Because the urban built-up area is the only target class, a one-class classifier, one-class support vector machine, is used. For comparison, the classical gray-level co-occurrence matrix (GLCM) is also used to extract image texture. The proposed method was evaluated using bi-temporal Landsat TM/ETM+ data of two megacity areas in China. Results demonstrated that the proposed method outperformed the use of spectral information alone and the joint use of the spectral information and the GLCM texture. In particular, the inclusion of multivariate variogram textures with spectral angle distance achieved the best results. The proposed method provides an effective way of extracting urban built-up areas from Landsat series images and could be applicable to other applications.

Vision-Based Vehicle Detection in Real Traffic Environment Using Fast Wavelet Transform and Kalman Filter

A new vision-based approach for robust vehicle detection is addressed in this study. First, fast wavelet transform (FWT) is proposed to extract image texture, while grey level co-occurrence matrix (GLCM) is employed to measure and analyze the extracted texture. Then, vehicles can be extracted because the vehicle sections and the shadow sections have different textures in the foreground image. Moreover, we put forward the state and observation matrixes of Kalman filter which can be used to track vehicles under complicated traffic scenes. Experimental results in real traffic scenes show that the proposed methods are effective and efficient.

Implementation of Coarse-to-Fine Strategy of Vehicle Registration Plate Identification

Edges contains some of the most useful information in an image. In an image, the edges can be used to measure the size of objects; to isolate particular objects from their background; to recognize or classify objects. The registration plate is one of the key steps in vehicle recognition system, the positioning accuracy is direct impact on the effect of registration plate recognition. In this paper, for the plate image that is under different backgrounds and lighting conditions,a Vehicle Registration Plate using coarse-to-fine strategy based on Edge detection technique was proposed. Text that appears in images contains important and useful information. Detection and extraction of text in images have been used in many applications. In this paper, we propose a coarse-to-fine edge-based text extraction system, which can detect and extract text in complex images. The experiments results show that the algorithm with good accuracy and have good practical value.

Research on born-digital image text extraction based on conditional random field

With the number of digital videos and digital images increasing tremendously in e-mails and web pages, text extraction from images becomes important more than ever. Born-digital images are generated directly with the computer and the text in the images is important to help the semantic understanding of the images. Although there are many methods proposed over the past years for text extraction from natural scene images, the text detection and extraction from born-digital images remains a challenge. This paper proposes a novel method to segment the text connected components CCs from a born-digital image. Firstly, binarisation is conducted on the given image to get all candidate text CCs based on wavelet theory. Secondly, classification is conducted on the extracted CCs to label text CCs based on conditional random field CRF - a probabilistic graph model that has been widely used in natural language processing. Experimental results show that the proposed method can effectively extract text from the born-digital images.

Global–local optimizations by hierarchical cuts and climbing energies

Hierarchical segmentation is a multi-scale analysis of an image and provides a series of simplifying nested partitions. Such a hierarchy is rarely an end by itself and requires external criteria or heuristics to solve problems of image segmentation, texture extraction and semantic image labelling. In this theoretical paper we introduce a novel framework: hierarchical cuts, to formulate optimization problems on hierarchies of segmentations. Second we provide the three important notions of h-increasing, singular, and scale increasing energies, necessary to solve the global combinatorial optimization problem of partition selection and which results in linear time dynamic programs. Common families of such energies are summarized, and also a method to generate new ones is described. Finally we demonstrate the application of this framework on problems of image segmentation and texture enhancement.

Color co-occurrence matrix based froth image texture extraction for mineral flotation

It is well accepted that the surface texture appearance of the flotation froth involves crucial information about its separation process, which can be used as an effective criterion for the qualitative assessment of the flotation performance. To obtain the distinctive characteristic of the froth surface appearance under various production conditions, a texture feature extraction method based on color co-occurrence matrix (CCM) is presented compared to the commonly used gray level co-occurrence matrix (GLCM). First, the HIS (Hue, Saturation and Intensity) color space is employed to exhibit and quantify the froth image, which yields a more intuitive description of the color properties in comparison with the RGB (Red, Green and Blue) color space. Then, the CCM is computed and the corresponding feature statistics of the froth surface texture are extracted based on the proposed matrix. Next, a new feature parameter is defined and extracted to describe the froth texture complexity based on the aforementioned texture feature statistics. After adequate offline froth images have been obtained from a bauxite flotation plant located in China under various production statuses with the corresponding concentrate grade in the froth assayed manually, the qualitative relationship between the texture complexity and the corresponding concentrate grade is investigated. Consequently, the optimal texture complexity range to achieve satisfactory production index is obtained for the further research of the optimal control of the flotation process. Experimental results have verified the effectiveness of the method and demonstrated its superiority over the previous texture feature extraction methods based on GLCM.

Contourlet-1.3 Texture Retrieval Algorithm by Sub-Bands Energy and Consistency

Contourlet-1.3 transform has fewer artifacts than original contourlet transform proposed by Do in 2002; it can extract image texture information more efficiently and has been studied for image de-noising, enhancement, and retrieval situations. Focus on improving the retrieval rate of contourlet-1.3 transform retrieval system, a new contourlet-1.3 texture retrieval algorithm was proposed in this paper. The feature vector of this system was a combination of sub-band energy and consistency and the similarity measure function used here was Canberra distance. Experimental results on 109 texture images coming from Brodatz album show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and system structure.

Material Texture Contourlet Retrieval by Energy and Variance Distribution Features

contourlet transform can extract image texture information more efficiently than wavelet transform and has been studied for image de-noising, enhancement, and retrieval situations, its low retrieval rate are still not satisfied due to feature extraction and other reasons. Focus on improving the retrieval rate of contourlet transform retrieval system, a new feature named variance distribution was proposed and a contourlet retrieval system was constructed in this paper. The feature vectors were constructed by cascading the energy and variance distribution of each sub-band coefficients and the similarity measure used here was Canberra distance. Experimental results show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and hardware complexity.

Variance Distribution and Energy Featured Non-Subsampled Contourlet Texture Retrieval

Non-Subsampled contourlet transform can extract image texture information more efficiently than basic contourlet transform and has been studied for image de-noising, enhancement, and retrieval situations, its low retrieval rate are still not satisfied due to feature extraction and other reasons. Focus on improving the retrieval rate of non-subsampled contourlet transform retrieval system, a new feature named variance distribution was proposed and then a non-subsampled contourlet retrieval system was constructed in this paper. The feature vectors were constructed by cascading the energy and variance distribution of each sub-band coefficients and the similarity measure used here was Canberra distance. Experimental results show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and hardware complexity.

An Edge-Based Algorithm for Text Extraction in Images and Video Frame

In this paper, a proposal of a new and unusual framework to detect and extract the text from the images and video frames have been presented. In the past various methods have been presented for detection and localization of text in images and video frames. In this paper, a comparison has been made between several text detection methods and proposed method for text detection in images and video frames. The proposed method is carried out by edge detection, and the projection profile method is used to localize the text region better. Various experiments have been carried out to evaluate and compare the performance of the proposed algorithm. Experimental results tested from a large dataset have demonstrated that the proposed method is effective and practical. Various parameters like average time, precision and recall rates and analyzed for both existing and proposed method to determine the success and limitation of our method.

Incorporating Spatial Variability Measures in Land-cover Classification using Random Forest

The spatial variability of remotely sensed image values provides important information about the arrangement of objects and their spatial relationships within the image. The characterisation of spatial variability in such images, for example, to measure of texture, is of great utility for the discrimination of land cover classes. To this end, the variogram, a function commonly applied in geostatistics, has been used widely to extract image texture for remotely sensed data classification.The aim of this study was to assess the increase in accuracy that can be achieved by incorporating univariate and multivariate textural measures of Landsat TM imagery in classification models applied to large heterogeneous landscapes. Such landscapes which difficult to classify due to the large number of land cover categories and low inter-class separability. Madogram, rodogram and direct variogram for the univariate case, and cross- and pseudocross variograms for the multivariate one, together with multi-seasonal spectral information were used in a Random Forest classifier to map land cover types.The addition of spatial variability into multi-seasonal Random Forest models leads to an increase in the overall accuracy of 8%, and to an increase in the Kappa index of 9%, respectively. The increase in per categories Kappa for the textural Random Forest model reached 30% for certain categories. This study demonstrates that the use of information on spatial variability produces a fundamental increase in per class classification accuracy of complex land-cover categories.

A projected gradient algorithm based on the augmented Lagrangian strategy for image restoration and texture extraction

Based on the augmented Lagrangian strategy, we construct a projected gradient algorithm for image restoration and texture extraction. The proposed algorithm is established on the basis of a mixed model which combines the Rudin–Osher–Fatemi (ROF) model with the Lysaker–Lundevold–Tai (LLT) model to reduce the staircase effect and blur phenomenons. The proof of the convergence of the proposed algorithm is provided. Moreover, we show that the dual methods based on convex analysis which have been proposed in some papers can be actually deduced from the augmented Lagrangian strategy. Some numerical examples are supplied to illustrate the efficiency of the proposed algorithm.

New frontiers in breast ultrasound technology

Cancer remains a leading cause of death in Taiwan, and the prevalence of breast cancer has increased in recent years. The early detection and diagnosis of breast cancer is the key to ensuring prompt treatment and a reduced death rate. Mammography and ultrasound (US) are the main imaging techniques used in the detection of breast cancer. The heterogeneity of breast cancers leads to an overlap in benign and malignant ultrasonography images, and US examinations are also operator dependent. Recently, computer-aided diagnosis (CAD) has become a major research topic in medical imaging and diagnosis. Technical advances such as tissue harmonic imaging, compound imaging, split screen imaging and extended field-of-view imaging, Doppler US, the use of intravenous contrast agents, elastography, and CAD systems have expanded the clinical application of breast US. Breast US CAD can be an efficient computerized model to provide a second opinion and avoid interobserver variation. Various breast US CAD systems have been developed using techniques which combine image texture extraction and a decision-making algorithm. However, the textural analysis is system dependent and can only be performed well using one specific US system. Recently, several researchers have demonstrated the use of such CAD systems with various US machines mainly for preprocessing techniques designed to homogenize textural features between systems. Morphology-based CAD systems used for the diagnosis of solid breast tumors have the advantage of being nearly independent of either the settings of US systems or different US machines. Future research on CAD systems should include pathologically specific tissue-related and hormonerelated conjecture, which could be applied to picture archiving and communication systems or teleradiology.

Gaussian mixture modeling and learning of neighboring characters for multilingual text extraction in images

This paper proposes an approach based on the statistical modeling and learning of neighboring characters to extract multilingual texts in images. The case of three neighboring characters is represented as the Gaussian mixture model and discriminated from other cases by the corresponding ‘pseudo-probability’ defined under Bayes framework. Based on this modeling, text extraction is completed through labeling each connected component in the binary image as character or non-character according to its neighbors, where a mathematical morphology based method is introduced to detect and connect the separated parts of each character, and a Voronoi partition based method is advised to establish the neighborhoods of connected components. We further present a discriminative training algorithm based on the maximum–minimum similarity (MMS) criterion to estimate the parameters in the proposed text extraction approach. Experimental results in Chinese and English text extraction demonstrate the effectiveness of our approach trained with the MMS algorithm, which achieved the precision rate of 93.56% and the recall rate of 98.55% for the test data set. In the experiments, we also show that the MMS provides significant improvement of overall performance, compared with influential training criterions of the maximum likelihood (ML) and the maximum classification error (MCE).

Gaussian Mixture Modeling of Neighbor Characters for Multilingual Text Extraction in Images

Gaussian Mixture Modeling of Neighbor Characters for Multilingual Text Extraction in Images

Multidimensional pulse image processing of chemical structure data

Recent advances in the understanding of the mammalian visual cortex have led to new approaches for image processing techniques. As a result of this, computer simulations using the proposed visual cortex model have become very useful in the field of image processing. Models of this kind have the ability to efficiently extract image segments, edges and texture. They operate by generating a set of pulse images (images with binary pixels) for a static input. These pulse images display synchronized activity of neighboring neurons, and it is these images in which the information about segments, edges and texture are displayed. Pulse image generation is dependent on autowaves that travel throughout the image. In order to extend pulse image processing to multidimensional data (i.e., data cubes), the autowaves are designed to expand in all of the cube's dimensions. In this fashion, pulse cubes can be created and the same analysis techniques that have been applied to two-dimensional pulse images can be applied to pulse image cubes. This paper examines and discusses multidimensional pulse image analysis applied to three-dimensional (3D) chemical structural data of 17β-estradiol.