Local Binarization Method Research Articles

Binary Ghost Imaging Based on the Fuzzy Integral Method

The reconstruction quality of binary ghost imaging depends on the speckle binarization process. In order to obtain better binarization speckle and improve the reconstruction quality of binary ghost imaging, a local adaptive binarization method based on the fuzzy integral is proposed in this study. There are three steps in the proposed binarization process. The first step is to calculate the integral image of the speckle with the summed-area table algorithm. Secondly, the fuzzy integral image is calculated through the discrete Choquet integral. Finally, the binarization threshold of each pixel of the speckle is selected based on the calculated fuzzy integral result. The experiment results verify the feasibility of the proposed method. Compared with other methods qualitatively and quantitatively, the proposed method has high performance in reconstructing image of target.

Inspection of textile fabrics based on the optimal Gabor filter

Defect detection and quality control play an important role in the textile industry. In this paper, an automatic algorithm based on the optimal Gabor filter is proposed for real-time inspection of textile fabrics. The Cuckoo optimization algorithm is adopted to optimize the parameters of the Gabor filter. Also, an adaptive local binarization method is proposed, which enhances the performance of our algorithm. In order to locate the defects, the filtered image is divided into non-overlapping blocks. Then, the candidate defective blocks are binarized using adaptive thresholds, which are determined by blocks statistics. The performance of the proposed algorithm is evaluated through different types of fabrics in the TILDA database and an online Fabric Stain Dataset. The experimental results demonstrate the efficiency of the proposed method in detecting defects on the plain, regular and irregular patterned fabrics. Furthermore, the comparative results are provided to show the robustness of the proposed method.

Scaling properties of binary and greyscale images in the context of X-ray soil tomography

Characterization of the complex soil structure is one the cornerstones of soil science and pore space detection is a crucial step in this process. Synthetic soil image construction has been proved to be an efficient resource for validating different binarization methods given that, unlike in real world, ground truth information is known. In this work, we introduce an improved Truncated Multifractal Method (TMM), to better simulate synthetic computed tomography (CT) soil images and then we generate 150 synthetic images with three different porosities (7%, 12% and 17%), both in greyscale and in binary scale (pore spaces). Synthetic images are then compared with two sets of 260 slides of real CT soil images, in order to validate the goodness of the method. All images are subjected to multifractal analysis where we show a detailed comparative analysis of parameters such as lacunarity, characteristic length and multifractal spectrum, that are calculated both for the whole set of synthetic (greyscale and binary) and for the sets of real CT soil images. With respect to lacunarity, a not previously reported inverse relationship between binary and grey lacunarity is found for this range of porosities. Moreover, we have also reported a new relationship between lacunarity and characteristic length. Similar multifractal results, that we obtain for real CT and synthetic soil images, prove that TMM is a reasonable solution to create simulated CT soil images. Finally, a segmentation test was carried out, using TMM synthetic greyscale soil images and its binary counterpart as ground-truth information, evaluating global (Otsu) and local (Combining Singularity-CA) binarization methods, where we report better performance for the last.

Adaptive uneven illumination correction method of document images

Under condition of uneven illumination, directly recognizing the document image by OCR would lead to a bad effect, which is a difficult point in document recognition. To solve this problem above, a Retinex-based illumination correction method was proposed. Firstly, the images were supposed to be di vided into blocks, which were processed respectively by Otsu binarization. The light conditions can be got through the feedback results of processing, which helps to have uneven illumination image blocks achieved. Then images can be enhanced by the improved Retinex method, which extends dynamic range of image block gray value and enhances details of image block. Finally, images can be corrected by sauvola binarization processing. Experimental results show that the algorithm can eliminate the impact of uneven illumination. Compared with the common local binarization and enhancement methods, the OCR recognition rates are significantly increased. At the same time, the combination of global and local methods promotes the applicable scope of being in condition of samples and different illumination.

Niblack’s binarization method and its modifications to real-time applications: a review

Local binarization methods deal with the separation of foreground objects (textual content) and background noise (non-text) specifically at the pixel level. This is a much-explored field in the domain of documents image-processing that tends to separate the textual content from a degraded document. Since three decades, many local binarization methods have been developed to binarize documents images suffering from severe deteriorations. This paper presents a review of local binarization methods that are developed based on Niblack’s binarization method (NBM, developed in 1986) only. Further, this paper is a review of local binarization methods having more or less modifications to the original Niblack’s method, depending on the requirements of their model and the processed output. The modifications to NBM can be seen in various applications, such as deteriorated documents image binarization, manuscripts restoration, finding texts in video frames, revealing engraved wooden stamps, vehicle license plate number recognition, stained cytology nuclei detection and product barcodes reading. However, there could be a possibility of other applications using NBM with modifications based on the input images and the applications’ requirements.

An Improved Adaptive Binarization Algorithm Based on Fuzzy Logic

Image binarization is divided into global algorithm and local algorithm. Global binarization algorithms have a problem to describe objects that have similar brightness with a single threshold. Local binarization algorithms make boundary lines because these algorithms split the image into a specific size of blocks. Therefore, in this paper, we propose a binarization method to complement these problems. The proposed method uses triangular fuzzy membership function to classify the image into obvious regions and ambiguous regions. Obvious regions are binarized by using global binarization algorithm. Whereas ambiguous regions are binarized by using improved local algorithm. Experimental results show the proposed method binarizes the image with less information loss. Moreover, binarized image describes the object in more detail than global binarization methods and more natural than local binarization method.

Gradation Image Processing for Text Recognition in Road Signs Using Image Division and Merging

This paper proposes a gradation image processing method for the development of a Road Sign Recognition Platform (RReP), which aims to facilitate the rapid and accurate management and surveying of approximately 160,000 road signs installed along the highways, national roadways, and local roads in the cities, districts (gun), and provinces (do) of Korea. RReP is based on GPS(Global Positioning System), IMU(Inertial Measurement Unit), INS(Inertial Navigation System), DMI(Distance Measurement Instrument), and lasers, and uses an imagery information collection/classification module to allow the automatic recognition of signs, the collection of shapes, pole locations, and sign-type data, and the creation of road sign registers, by extracting basic data related to the shape and sign content, and automated database design. Image division and merging, which were applied in this study, produce superior results compared with local binarization method in terms of speed. At the results, larger texts area were found in images, the accuracy of text recognition was improved when images had been gradated. Multi-threshold values of natural scene images are used to improve the extraction rate of texts and figures based on pattern recognition.

Defect inspection system for steel wire rods produced by hot rolling process

A vision-based inspection system has been investigated in order to improve the quality of products and processes found in various industries. In this paper, we propose a new defect detection algorithm for steel wire rods produced by the hot rolling process. Because the steel wire rods are long cylinder rods with a circular cross section, the brightness at the sides and center is inconsistent. Moreover, the various types of steel wire rods and the presence of scales affect the reflection properties of the rod surface. In order to resolve the abovementioned difficulties, the use of dynamic programming and a discrete wavelet transform are proposed. An adaptive local binarization method is used to further reduce the effects of scale. The effectiveness of the proposed method is shown by means of experiments conducted on images of steel wire rods that were obtained from an actual steel production line.

SEGMENTING THE EYE FUNDUS IMAGES FOR IDENTIFICATION OF BLOOD VESSELS

Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of vessel information and segmentation of vessel pixels. Algorithms of the first group start on known vessel point and trace the vasculature structure in the image. Algorithms of the second group perform a binary classification (vessel or non-vessel, i.e. background) in accordance of some threshold. We focus here on the binarization [4] methods that adapt the threshold value on each pixel to the global/local image characteristics. Global binarization methods [5] try to find a single threshold value for the whole image. Local binarization methods [3] compute thresholds individually for each pixel using information from the local neighborhood of the pixel. In this paper, we modify and improve the Sauvola local binarization method [3] by extending its abilities to be applied for eye fundus pictures analysis. This method has been adopted for automatic detection of blood vessels in retinal images. We suggest automatic parameter selection for Sauvola method. Our modification allows determine/extract the blood vessels almost independently of the brightness of the picture.

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.

Adaptive Binarization of Unconstrained Hand-Held Camera-Captured Document Images

This paper presents a new adaptive binarization technique for degraded hand-held camera-captured document images. The state-of-the-art locally adaptive bi- narization methods are sensitive to the values of free parameter. This problem is more critical when binarizing degraded camera-captured document images because of distor- tions like non-uniform illumination, bad shading, blurring, smearing and low resolution. We demonstrate in this paper that local binarization methods are not only sensitive to the selection of free parameters values (either found manually or automatically), but also sensitive to the constant free parameters values for all pixels of a document image. Some range of values of free parameters are better for foreground regions and some other range of values are better for background regions. For overcoming this prob- lem, we present an adaptation of a state-of-the-art local binarization method such that two different set of free parameters values are used for foreground and background regions respectively. We present the use of ridges detection for rough estimation of foreground regions in a document image. This information is then used to calculate ap- propriate threshold using different set of free parameters values for the foreground and background regions respectively. The evaluation of the method using an OCR-based measure and a pixel-based measure show that our method achieves better performance as compared to state-of-the-art global and local binarization methods.