О методе цифровых водяных знаков на основе особенностей изображения и моментов Цернике

Рассматривается одна из задач компьютерной стеганографии – защита авторских прав на цифровые изображения. Описан метод на основе содержимого контейнера, стойкий к геометрическим искажениям. Проблема синхронизации ЦВЗ в изображении решается на основе особенностей изображения, выделенных с помощью детектора, использующего разницу в гауссианах. Моменты Цернике обеспечивают стойкость ЦВЗ к атакам удаления.Розглядається одна із задач комп’ютерної стеганографії – захист авторських прав на цифрові зображення. Описаний метод на основі контенту контейнера, що є стійким до геометричних спотворень. Проблема синхронізації ЦВЗ в зображенні розв’язується на основі особливостей зображення. Особливості виділені за допомогою детектора, що використовує різницю в гаусіанах. Моменти Церніке забеспечують стійкість ЦВЗ до атак видаленням.One problem of computer steganography is considered in the paper – digital image copyright interests. The container content-based method is described that is geometric effect robust. The synchronization in the image problem solution is image feature-based that were detected by the using difference of Gaussians detector. Zernike moments are protecting watermark from deletion attacks

Optimal designs for statistical analysis with Zernike polynomials

n.a. --Optimal design,Zernike polynomials,image analysis,D-optimality,E-optimality

A visual approach to sketched symbol recognition

There is increasing interest in building systems that can automatically interpret hand-drawn sketches. However, many challenges remain in terms of recognition accuracy, robustness to different drawing styles, and ability to generalize across multiple domains. To address these challenges, we propose a new approach to sketched symbol recognition that focuses on the visual appearance of the symbols. This allows us to better handle the range of visual and stroke-level variations found in freehand drawings. We also present a new symbol classifier that is computationally efficient and invariant to rotation and local deformations. We show that our method exceeds state-of-the-art performance on all three domains we evaluated, including handwritten digits, PowerPoint shapes, and electrical circuit symbols

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols

Algorithmic Efficiency of Stroke Gesture Recognizers: a Comparative Analysis

Gesture interaction is today recognized as a natural, intuitive way to execute commands of an interactive system. For this purpose, several stroke gesture recognizers become more efficient in recognizing end-user gestures from a training set. Although the rate algorithms propose their rates of return there is a deficiency in knowing which is the most recommended algorithm for its use. In the same way, the experiments known by the most successful algorithms have been carried out under different conditions, resulting in non-comparable results. To better understand their respective algorithmic efficiency, this paper compares the recognition rate, the error rate, and the recognition time of five reference stroke gesture recognition algorithms, i.e., $1,$P, $Q, !FTL, and Penny Pincher, on three diverse gesture sets, i.e., NicIcon, HHReco, and Utopiano Alphabet, in a user-independent scenario. Similar conditions were applied to all algorithms, to be executed under the same characteristics. For the algorithms studied, the method agreed to evaluate the error rate and performance rate, as well as the execution time of each of these algorithms. A software testing environment was developed in JavaScript to perform the comparative analysis. The results of this analysis help recommending a recognizer where it turns out to be the most efficient. !FTL (NLSD) is the best recognition rate and the most efficient algorithm for the HHreco and NicIcon datasets. However, Penny Pincher was the faster algorithm for HHreco datasets. Finally,$1 obtained the best recognition rate for the Utopiano Alphabet dataset

Drawing-Based Automatic Dementia Screening Using Gaussian Process Markov Chains

Screening tests play an important role for early detection of dementia. Among those widely used screening tests, drawing tests have gained much attention in clinical psychology. Traditional evaluation of drawing tests totally relies on the appearance of drawn picture, but does not consider any time-dependent behaviour. We demonstrated that the processing speed and direction can reflect the decline of cognitive function, and thus may be useful for disease screening. We proposed a model of Gaussian process Markov chains (GPMC) to study the complex associations within the drawing data. Specifically, we modeled the process of drawing in a state-space form, where a drawing state is composed of drawing direction and velocity with consideration of the processing time. For temporal modeling, our scope focused more on discrete-time Markov chains on continuous state space. Because of the short processing time of picture drawing, we applied higher-order of Markov chains to model long-term temporal correlation across drawing states. Gaussian process regression was used for universal function approximation to flexibly infer the state transition function. With Gaussian process prior to the distribution of function space, we could encode high-level function properties such as noisiness, smoothness and periodicity. We also derived an efficient training mechanism for complex Gaussian process regression on bivariate Markov chains. With GPMC, we present an optimal decision rule based on Bayesian decision theory. We applied our proposed method to a drawing test for dementia screening, i.e. interlocking pentagon-drawing test. We tested our models with 256 subjects who are aged from 65 to 95. Finally, comparing to the traditional methods, our models showed remarkable improvement in drawing test for dementia screening

Off-line Handwritten Kannada Text Recognition using Support Vector Machine using Zernike Moments

Abstract It is a well-known fact that building a character recognition system is one of the hottest areas of research as it is shown over the Internet and due to its wide range of prospects. The objective of this paper is to describe an OCR system for handwritten text documents in Kannada. The input to the system is a scanned image of a text and the output is a machine editable file compatible with most typesetting Kannada software. The system first extracts characters from the document image and a set of features are extracted from the character image using Zernike moments. The final recognition is achieved using support vector machine (SVM). The recognition is independent of the size of the handwritten text and the system is seen to deliver reasonable performance