Adaptive Binarization Research Articles

Multi Core Processors for Camera based OMR

Today, most of desktops, laptops, tablets, and even smart phones are shipped with multi-core processors. The efficient utilization of multi-core processors computation power can't be achieved by developing traditional applications with sequential algorithms. Parallel algorithms utilize the capabilities of these processors, but need a special design to optimize the application to fit the hardware system. Image processing programs are heavy computational algorithms with very large amount of data. They are very well suited for parallel processing. This work presents a low cost and fast solution for optical mark recognition system working in multi-core processor system. The answer sheet is captured using a digital camera and the image is processed. Initially the borders of the sheet are located then the bubbles are detected. Fast techniques are used to detect the bubbles without a rotation correction. An adaptive binarization has been used to overcome the lighting effects of the camera based images. A classifier is trained to decide if the bubble is marked or not. A dataset of images under different rotations, illuminations, is used to train and test the system. An accuracy of 99.8% is obtained. The algorithms are analyzed and optimized for parallel computation on a multicore processor. The processing time is reduced to about 40% of the sequential computation time.

Local-Global Image Binarization for Reconstructing the Cellular Structure of Polymer Foam Materials

AbstractThis paper presents a new hybrid method for the adaptive binarization of cellular structures in the computer aided modeling of polymer foam materials. The proposed method incorporates two established binarization methods, one global approach and one local. The binarization of cellular structures presents particular challenges as images often include distinctive edges that exist at different depths. This issue is addressed along with the common problems of noise and gradients that can be present in images. By incorporating global information with the local information, the current method was able to maintain the details achieved by the local method while reducing the noise as a global method does. Experimental results indicate the efficacy of our approach in comparison with existing methods. The proposed method can be applied in bio-inspired material design, bio-medical materials, and polymer materials science in general.

A combined approach for the binarization of handwritten document images

There are many challenges addressed in handwritten document image binarization, such as faint characters, bleed-through and large background ink stains. Usually, binarization methods cannot deal with all the degradation types effectively. Motivated by the low detection rate of faint characters in binarization of handwritten document images, a combination of a global and a local adaptive binarization method at connected component level is proposed that aims in an improved overall performance. Initially, background estimation is applied along with image normalization based on background compensation. Afterwards, global binarization is performed on the normalized image. In the binarized image very small components are discarded and representative characteristics of a document image such as the stroke width and the contrast are computed. Furthermore, local adaptive binarization is performed on the normalized image taking into account the aforementioned characteristics. Finally, the two binarization outputs are combined at connected component level. Our method achieves top performance after extensive testing on the DIBCO (Document Image Binarization Contest) series datasets which include a variety of degraded handwritten document images.

저화질 문서영상들을 위한 적응적 이진화 알고리즘

본 논문은 인쇄체 한글과 한자를 포함하는 저화질 문서영상에 효과적인 이진화 알고리즘을 제안하다. 가는 가로획과 굵은 세로획으로 구성된 문자 속성 때문에 기존 알고리즘은 저화질 문서영상에서 세로획보다 약한 성분을 갖는 가로획을 쉽게 검출할 수 없다. 제안된 알고리즘은 전방향 참조 적응적 알고리즘에 수직 방향 참조 알고리즘을 추가하여 기존 알고리즘이 갖는 문제를 해결한다. 실험 결과는 제안된 알고리즘이 다양한 저화질 문서영상들에서 문자를 잘 추출하는 것을 보여주고 있다. This paper proposes an adaptive binarization algorithm which is highly effective for a degraded document image including printed Hangul and Chinese characters. Because of the attribute of character composed of thin horizontal strokes and thick vertical strokes, the conventional algorithms can't easily extract horizontal strokes which have weaker components than vertical ones in the degraded document image. The proposed algorithm solves the conventional algorithm's problem by adding a vertical-directional reference adaptive binarization algorithm to an omni-directional reference one. The simulation results show the proposed algorithm extracts well characters from various degraded document images.

Improving the quality of pigskin leather texture images using enhanced image processing

In this paper, we propose a method to improve the quality of pigskin leather texture images using digital image processing approach. The proposed method intends to minimize the imbalance illumination problem that occurs in the images captured from consumer leather products. This method is compared with the other methods in the literature such as contrast adaptive binarization method, and the results showed that the proposed method yielded better results in terms of highlighting the special characteristics of pigskin in consumer leather products. Key words: Digital image processing, pig skin images, binarization, local global analysis, histogram.

Improving the quality of pigskin leather texture images using enhanced image processing

In this paper, we propose a method to improve the quality of pigskin leather texture images using digital image processing approach. The proposed method intends to minimize the imbalance illumination problem that occurs in the images captured from consumer leather products. This method is compared with the other methods in the literature such as contrast adaptive binarization method, and the results showed that the proposed method yielded better results in terms of highlighting the special characteristics of pigskin in consumer leather products. Key words: Digital image processing, pig skin images, binarization, local global analysis, histogram.

Parameter-free based two-stage method for binarizing degraded document images

Binarization plays an important role in document image processing, especially in degraded documents. For degraded document images, adaptive binarization methods often incorporate local information to determine the binarization threshold for each individual pixel in the document image. We propose a two-stage parameter-free window-based method to binarize the degraded document images. In the first stage, an incremental scheme is used to determine a proper window size beyond which no substantial increase in the local variation of pixel intensities is observed. In the second stage, based on the determined window size, a noise-suppressing scheme delivers the final binarized image by contrasting two binarized images which are produced by two adaptive thresholding schemes which incorporate the local mean gray and gradient values. Empirical results demonstrate that the proposed method is competitive when compared to the existing adaptive binarization methods and achieves better performance in precision, accuracy, and F-measure.

AdOtsu: An adaptive and parameterless generalization of Otsu's method for document image binarization

Adaptive binarization methods play a central role in document image processing. In this work, an adaptive and parameterless generalization of Otsu's method is presented. The adaptiveness is obtained by combining grid-based modeling and the estimated background map. The parameterless behavior is achieved by automatically estimating the document parameters, such as the average stroke width and the average line height. The proposed method is extended using a multiscale framework, and has been applied on various datasets, including the DIBCO'09 dataset, with promising results.

Design of Ball-Beam Control System Based on Machine Vision

The nonlinear Ball-beam system combine with a CCD camera is studied in this paper. The images which include Ball-beam system and a ruler are collected by CCD sensor. The image is segmented using the adaptive image binarization threshold algorithm, and then the ruler, the ball position and the pointer position are extracted from the image. The ruler is scaled and the pointer position is also calculated. Finally, the value of pointer position is input into Ball-beam system as an expected ball balance position. A Fuzzy self-tuning PID controller and a BP neural network PID controller are designed for ball balance stable control. After experimental, the Ball-beam balance control in any position can be fulfilled using both of algorithms.

Vehicle License Plate Recognition Based on Text-line Construction and Multilevel RBF Neural Network

License plate localization and character segmentation and recognition are the research hotspots of vehicle license plate recognition (VLPR) technology. A new method to VLPR is presented in this paper. In license plate localization section, Otsu binarization is operated to get the plate-candidates regions, and a text-line is constructed from the candidate regions. According to the text-line construction result and the characteristics of the license plate character arrangement, the license plate location will be determined. And then the locally optimal adaptive binarization is utilized to make more accurate license plate localization. After the license plate localization, the segment method of vertical projection information with prior knowledge is used to slit characters and the statistical features are extracted. Then the multilevel classification RBF neural network is used to recognize characters using the feature vector as input. The results show that this method can recognize characters precisely and improve the ability of license plate character recognition effectively.

A QoE Assessment System in Distance Education

It is a challenging task to improve the real-time property and objectivity of the effect assessment for the distance education. This paper presents a QoE (Quality of Experience) assessment system based on the attention of online user. The system captures the video frames from two cameras periodically and synchronously, using the adaptive image binarization based on the linear average threshold for the pretreatment, then processing with edge detection and filtering in the cross-directions at the same time. System gets the position of computer screen and user eyeball. Analyzing the detection results comprehensively obtains the attention of online user by some judging conditions, and finally acquires the quality of user experience. Experimental results demonstrate the feasibility and efficiency.

A Fast Algorithm for Road Recognition in Remote Sensing Image

In general, the road extraction methods in remote sensing images mainly are edge detection, feature integration, and so on. A fast road recognition arithmetic is presented in this paper. First using adaptive binarization arithmetic, the path on remote sensing images is extracted. Then morphological method is used to process image. Finally, the extracted image superimposed with the original and get clear road. Simulation results shows that this algorithm is efficiency, the anti-noise ability is enhance, and more precision.

AN ADAPTIVE LAYER-BASED LOCAL BINARIZATION TECHNIQUE FOR DEGRADED DOCUMENTS

This paper presents a new technique for adaptive binarization of degraded document images. The proposed technique focuses on degraded documents with various background patterns and noise. It involves a preprocessing local background estimation stage, which detects for each pixel that is considered as background one, a proper grayscale value. Then, the estimated background is used to produce a new enhanced image having uniform background layers and increased local contrast. That is, the new image is a combination of background and foreground layers. Foreground and background layers are then separated by using a new transformation which exploits efficiently, both grayscale and spatial information. The final binary document is obtained by combining all foreground layers. The proposed binarization technique has been extensively tested on numerous documents and successfully compared with other well-known binarization techniques. Experimental results, which are based on statistical, visual and OCR criteria, verify the effectiveness of the technique.

Automatic circle pattern extraction and camera calibration using fast adaptive binarization and plane homography

A method to detect calibration patterns using fast adaptive binarization and plane homography for images having complicated backgrounds and taken under lighting condition of industrial fields is proposed. The preprocessing step of involving the calibration of a camera, as required to measure object dimensions, must be able to extract calibration points from a calibration pattern. However, proper lighting conditions for the camera calibration of a measurement system are rarely provided in industrial settings. In this paper, a plane homography of calibration points extracted by fast adaptive image binarization is used for the automatic detection of a local set of calibration points. The integral image method is used as a central part for the rapid processing of adaptive image binarization. The extracted point set provides a transformation matrix of the plane homography and guides the extraction of other calibration points for cluttered backgrounds with irregular lighting conditions. During the calibration of the camera, a calibration point filtering procedure by a data In/Out system iteratively selects more correct calibration points while rejecting outliers and noisy data. Experiments using actual images verify that the proposed method can be applied to camera calibration with poor quality images having cluttered backgrounds obtained under uneven illumination.

A multi-scale framework for adaptive binarization of degraded document images

In this work, a multi-scale binarization framework is introduced, which can be used along with any adaptive threshold-based binarization method. This framework is able to improve the binarization results and to restore weak connections and strokes, especially in the case of degraded historical documents. This is achieved thanks to localized nature of the framework on the spatial domain. The framework requires several binarizations on different scales, which is addressed by introduction of fast grid-based models. This enables us to explore high scales which are usually unreachable to the traditional approaches. In order to expand our set of adaptive methods, an adaptive modification of Otsu's method, called AdOtsu, is introduced. In addition, in order to restore document images suffering from bleed-through degradation, we combine the framework with recursive adaptive methods. The framework shows promising performance in subjective and objective evaluations performed on available datasets.

A binarization method with learning-built rules for document images produced by cameras

In this paper, we propose a novel binarization method for document images produced by cameras. Such images often have varying degrees of brightness and require more careful treatment than merely applying a statistical method to obtain a threshold value. To resolve the problem, the proposed method divides an image into several regions and decides how to binarize each region. The decision rules are derived from a learning process that takes training images as input. Tests on images produced under normal and inadequate illumination conditions show that our method yields better visual quality and better OCR performance than three global binarization methods and four locally adaptive binarization methods.

Determining membrane permeability of giant phospholipid vesicles from a series of videomicroscopy images

A technique for determining the permeability of a phospholipid membrane on a single giant unilamellar vesicle (GUV) is described, which complements the existing methods utilizing either a planar black lipid membrane or sub-micrometre-sized liposomes. A single GUV is transferred using a micropipette from a solution of a nonpermeable solute into an iso-osmolar solution of a solute with a higher membrane permeability. Osmotical swelling of the vesicle is monitored with a CCD camera mounted on a phase contrast microscope, and a sequence of images is obtained. On each image, the points on the vesicle contour are determined using Sobel filtering with adaptive binarization threshold, and from these, the vesicle radius is calculated with great accuracy. From the time dependence of the vesicle radius, the membrane permeability is obtained. Using a test set of data, the method provided a consistent estimate of the POPC membrane permeability for glycerol, P = 1.7 × 10−8 m s−1, with individual samples ranging from P = 1.61 × 10−8 m s−1 to P = 1.98 × 10−8 m s−1. This value is ≈40% lower than the one obtained on similar systems. Possible causes for this discrepancy are discussed.

Processing of Degraded Documents for Long-Term Archival Using Waferfiche™ Technology

Adaptive binarization techniques are proposed for the restoration of degraded documents for Waferfiche™ archival. Waferfiche™ is a compact archival solution for long-term preservation of documents in a human-accessible image based format. Bi-level images are preferable for the lithographic fabrication utilized in Waferfiche™ production. Binarization of degraded documents poses challenges due to the tonal variations in the foreground text and background paper. Using dispersion and edge based measures, our methods are specifically optimized for preserving information while eliminating unwanted noise. In addition, we present several pre/post-processing techniques that improve image quality. The effectiveness of our methodology is experimentally demonstrated by images captured from Waferfiches. Adaptive binarization using dispersion and edge based noise elimination thus provide an effective restoration method for degraded documents for Waferfiche™ archival.

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.

Layout Analysis for Camera-Based Whiteboard Notes

A domain where, even in the era of electronic document processing, hand- writing is still widely used is note-taking on a whiteboard. Such documents are either captured by a pen-tracking device or - which is much more challenging - by a cam- era. In both cases the layout analysis of realistic whiteboard notes is an open research problem. In this paper we propose a camera-based three-stage approach for the automatic lay- out analysis of whiteboard documents. Assuming a reasonable foreground-background separation of the handwriting it starts with a locally adaptive binarization followed by connected component extraction. The latter are then automatically classified as repre- senting either simple graphical elements of a mindmap or elementary text patches. In the final stage the text patches are subject to a clustering procedure in order to gener- ate hypotheses for those image regions where textual annotations of the mindmap can be found. In order to demonstrate the effectiveness of the proposed approach we report results of a writer independent experimental evaluation on a data set of mindmap images created by several different writers without any constraints on writing or drawing style.