In a classical paper, Chvátal introduced a rounding procedure for strengthening the polyhedral relaxation P of an integer program; applied recursively, the number of iterations needed to obtain the ...convex hull of the integer solutions in P is known as the Chvátal rank. Chvátal showed that this rank can be exponential in the input size L needed to describe P. We give a compact extended formulation of P, described by introducing binary variables, whose rank is polynomial in L.
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In this article, we propose the integration of a novel reinforcement learning technique into our generic and unified framework. This framework enables any continuous metaheuristic to operate in ...binary optimization, with the technique in question known as the Multi-Armed Bandit. Population-based metaheuristics comprise multiple individuals that cooperatively and globally explore the search space using their limited individual capabilities. Our framework allows these population-based metaheuristics to continue leveraging their original movements, designed for continuous optimization, once they are binary encoded. The generality of the framework has facilitated the instantiation of popular algorithms from the optimization, machine learning, and evolutionary computing communities. Furthermore, it permits the design of new and innovative optimization instances using various component strategies, reflecting the framework’s modularity. The results comparing two statistical techniques and three hybridizations coming from Machine Learning, have shown to obtain a better performance with the metahuristics in Grey Wolf Optimizer and Whale Optimization Algorithm.
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Preservation of precious knowledge present in Malayalam literature is a tedious task because of the presence of a huge amount of degradations in historical documents. One way to save these documents ...is to enhance the Malayalam manuscripts and storing them electronically. Here we are using historical Malayalam documents like poems, agreement copies and palm leaves as experimental dataset. In our proposed work is a novel binarization model which is based on modified Wellner’s algorithm. The degraded input image is converted into an integral image and then a modified version of Wellner’s algorithm is applied to it. This would enable us to enhance the document and further proceed with remaining phases of OCR. In binarization mainly we are focusing on the challenges from degraded documents like Non-uniform Background Illumination, Stains, bleed through etc. In our work we are developing a model which effectively binarizes the degraded Malayalam documents, especially the challenges like oil stain, smudge, and uneven illuminations. Finally, we will be able to validate and check the accuracy of the proposed model effectively. The proposed algorithm yielded an accuracy of 92%.
•A selectional autoencoder approach for document image binarization is studied.•The neural network is devoted to learning an image-to-image binarization.•Comprehensive experimentation with datasets ...of different typology is presented.•Results demonstrate that the approach is able to outperform the state of the art.
Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of document analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a global threshold. This approach has proven to outperform existing binarization strategies in a number of document types.
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•We propose a novel iteration deep learning which can improve the input image iteratively.•We apply the proposed iterative deep learning for document enhancement and binarization in two possible ...ways: recurrent refinement and stacked refinement.•Our proposed method provides a new, clean version of the degraded image, one that is suitable for visualization and which shows promising results for binarization using Otsu’s global threshold.
This paper presents a novel iterative deep learning framework and applies it to document enhancement and binarization. Unlike the traditional methods that predict the binary label of each pixel on the input image, we train the neural network to learn the degradations in document images and produce uniform images of the degraded input images, which in turn allows the network to refine the output iteratively. Two different iterative methods have been studied in this paper: recurrent refinement (RR) that uses the same trained neural network in each iteration for document enhancement and stacked refinement (SR) that uses a stack of different neural networks for iterative output refinement. Given the learned nature of the uniform and enhanced image, the binarization map can be easily obtained through use of a global or local threshold. The experimental results on several public benchmark data sets show that our proposed method provides a new, clean version of the degraded image, one that is suitable for visualization and which shows promising results for binarization using Otsu’s global threshold, based on enhanced images learned iteratively by the neural network.
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The intrinsic features of documents, such as paper color, texture, aging, translucency, the kind of printing, typing or handwriting, etc., are important with regard to how to process and enhance ...their image. Image binarization is the process of producing a monochromatic image having its color version as input. It is a key step in the document processing pipeline. The recent Quality-Time Binarization Competitions for documents have shown that no binarization algorithm is good for any kind of document image. This paper uses a sample of the texture of the scanned historical documents as the main document feature to select which of the 63 widely used algorithms, using five different versions of the input images, totaling 315 document image-binarization schemes, provides a reasonable quality-time trade-off.
•Hardware implementation of video compression and decompression processes.•Design and FPGA implementation of improved high throughput HEVC CABAC binarizer.•Design and FPGA implementation of improved ...high throughput HEVC CABAC De-binarizer.•New architectures of CABAC Binarization and de-binarization in H.265 video codec.
High efficiency video coding (HEVC) video codec applies different techniques in order to achieve high compression ratios and video quality that supports real-time applications. One of the critical techniques in HEVC is the Context adaptive Binary Arithmetic Coding (CABAC) which is type of entropy coding. CABAC comes at the cost of increased computational complexity, especially for parallelization and pipeline of these blocks: binarization, context modeling and binary arithmetic encoding. The Binarization (BZ) and de-Binarization (DBZ) methods are considered as important techniques in HEVC CABAC encoder and decoder respectively. Indeed, an important goal is to get high throughput in hardware architectures of CABAC BZ and DBZ in order to achieve high resolution applications. This work is the only one found on recent literature which focuses on design and implementation of full BZ and full DBZ compatible with H.265 and H.264. Consequently, a hardware architectures of BZ and DBZ are designed and implemented by using VHDL language, targeted an FPGA virtex4 xc4vsx25-12ff668 board and emulated with ModelSim. As a result, the implementation of BZ and DBZ can process 2 bins/cycle for each syntax element when operated at 697.83 MHz and 789.26 MHz, respectively. The proposed designs exhibits an improved high-throughput of 1395.66 Mbins/s for BZ and 1578.52 Mbins/s for the DBZ. The obtained Area Efficiencies in our proposed BZ and DBZ are about 0.544 Mbins/s/slices and 0.606 Mbins/s/slices, respectively, and it is better than many recent works.
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One of the most fundamental issues in image processing is the thresholding (binarization) method. This method is generally used for segmenting regions with different homogeneity in grayscale images. ...In other words, it performs clustering based on the intensity levels of pixels in an image histogram. This paper presents a new and effective approach to the global thresholding method of grayscale images. In the proposed method, alpha and beta regions are determined using the mean and standard deviation values of an image histogram. The optimum threshold value is obtained by calculating the average of gray-scale values of the alpha and beta regions. The experiments were carried out on three different image sets to demonstrate the effectiveness of the thresholding method. The result of experimental studies show that the proposed method achieves promising performance compared to many traditional, state-of-the-art thresholding and document binarization methods performed in H-DIBCO'14 (Document Image Binarization Competition), Human HT29 colon-cancer cells (BBBC008) and C. elegans live/dead assay (BBBC010) datasets based on various evaluation criteria.
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