Image Enhancement with Statistical Estimation

Contrast enhancement is an important area of research for the image analysis. Over the decade, the researcher worked on this domain to develop an efficient and adequate algorithm. The proposed method will enhance the contrast of image using Binarization method with the help of Maximum Likelihood Estimation (MLE). The paper aims to enhance the image contrast of bimodal and multi-modal images. The proposed methodology use to collect mathematical information retrieves from the image. In this paper, we are using binarization method that generates the desired histogram by separating image nodes. It generates the enhanced image using histogram specification with binarization method. The proposed method has showed an improvement in the image contrast enhancement compare with the other image.Comment: 9 pages,6 figures; ISSN:0975-5578 (Online); 0975-5934 (Print

Creating an artificial wine taster: Inferring the influence of must and yeast from the aroma profile of wines using artificial intelligence

The human brain is able to compute information from very complex olfactorical impressions. The special pattern of the concentrations of hundreds of aroma constituents allows an experienced wine taster to determine special features of the wine, for instance grape variety or vintage.Artificial Neural Networks are often used to recognize shapes and patterns like faces or finger prints. Here we use Artificial Neural Networks to mimic the abilities of a wine taster to deal with very complex olfactorical patterns. We produced 120 unique wines combining twelve different grape musts and ten yeast strains and determined the aroma profile (83 aroma constituents) of all wines. We analyzed the ability of a well trained neural network to recognize the used must variety and the fermenting yeast strain from unknown aroma profiles. Furthermore we investigated the capability to predict the aroma profile of a wine with a must variety/yeast strain combination that is new to the neural network.In 96 % of all trials the neural network identified the must that was used for wine production correctly (expected random propability: 8 %). An accurate identification of the yeast strain, used for fermentation, occurred in 67 % of all trials (propability by chance: 10 %).The aroma profiles of the must/yeast combinations new to the neural network were forecasted with a divergence of only 2.1 % compared to the actual wine of this production characterization. Thus we conclude that a comprehensive description of wines using neural networks is possible.

LayoutLM: Pre-training of Text and Layout for Document Image Understanding

Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the widespread use of pre-training models for NLP applications, they almost exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding. In this paper, we propose the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training. It achieves new state-of-the-art results in several downstream tasks, including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42). The code and pre-trained LayoutLM models are publicly available at \url{https://aka.ms/layoutlm}.Comment: KDD 202

User-driven Page Layout Analysis of historical printed Books

International audienceIn this paper, based on the study of the specificity of historical printed books, we first explain the main error sources in classical methods used for page layout analysis. We show that each method (bottom-up and top-down) provides different types of useful information that should not be ignored, if we want to obtain both a generic method and good segmentation results. Next, we propose to use a hybrid segmentation algorithm that builds two maps: a shape map that focuses on connected components and a background map, which provides information about white areas corresponding to block separations in the page. Using this first segmentation, a classification of the extracted blocks can be achieved according to scenarios produced by the user. These scenarios are defined very simply during an interactive stage. The user is able to make processing sequences adapted to the different kinds of images he is likely to meet and according to the user needs. The proposed “user-driven approach” is capable of doing segmentation and labelling of the required user high level concepts efficiently and has achieved above 93% accurate results over different data sets tested. User feedbacks and experimental results demonstrate the effectiveness and usability of our framework mainly because the extraction rules can be defined without difficulty and parameters are not sensitive to page layout variation