Vol 26, No 1 (2016)

Welding defects detection and classification is very important to guarantee the welding quality. Over the last 30 years, there has been a large amount of research attempting to develop an automatic (or semiautomatic) system for the detection and classification of weld defects in continuous welds using radiography. In this paper, we describe an automatic system for classification of welding defects from radiographic images and compare with KNN and SVM classifiers. We classify and recognize the linear defects such as lack of penetrations, incomplete fusion and external undercut. Experimental results have shown the classification method is useful for the lengthy defects and obtained through our method is better than the two classifiers methods.

Watermarking is a means of implanting data in the form of text, symbols or any credential into the multimedia data such as image, audio or video. The conventional produces below-par accomplishment when the communication is implanted into the image LSB bits. With a view to rectify these defects and also to safeguard the confidential data and images from the bogus clients, we are introducing an innovative method. In the proposed method, we employ. Adaptive quin tree decomposition for fragmenting the original image into five blocks, and the signed communication from the ECC technique are implanted into the fifth block pixel values. Subsequently, the data revival and authentication procedure are done to check the authenticity or otherwise of the data and image revived. A group of data including images is employed to assess the efficiency of our innovative approach and amazing outcomes achieved from the novel approach that predict the robustness of the suggested decomposition method in image verification procedure. The performance outcomes also validate the proficiency of the proposed program for the confirmation of the image by means of watermarking. It has gone a long way in attaining progress in image certification, safety and security measures. Further, the efficiency of the novel system is put to test and contrasted with the outcomes of the current watermarking technique.

The technology of facial expression recognition is a challenging problem in the field of intelligent human-computer interaction. An algorithm based on the Gabor wavelet transformation and non-negative matrix factorization (G-NMF) is presented. The main process includes image preprocessing, feature extraction and classification. At first, the face region containing emotional information is obtained and normalized. Then, expressional features are extracted by Gabor wavelet transformation and the high-dimensional data are reduced by non-negative matrix factorization (NMF). Finally, two-layer classifier (TLC) is designed for expression recognition. Experiments are done on JAFFE facial expressions database. The results show that the method proposed has a better performance.

The study presents a new approach to the synthesis of multidimensional quasi-invariant control systems based on the formulation and solution of the synthesis problem as an pattern recognition problem. The capabilities and specific features of the new approach are illustrated by two mathematical models of control systems.

Knowledge about relative poses within a tractor/trailer combination is a vital prerequisite for kinematic modelling and trajectory estimation. In case of autonomous vehicles or driver assistance systems, for example, the monitoring of an attached passive trailer is crucial for operational safety. We propose a camerabased 3D pose estimation system based on a Kalman-filter. It is evaluated against previously published methods for the same problem.

A wide range of investigations is based on analysis of experimental data, which are represented as a function of two variables (height of the relief h(x, y), microhardness, color, etc.). In the studying of process or phenomenon change of these functions after the experiment is of great interest. On numerous occasions the main problem in comparative analysis of the data sets is the absence of a natural reference level (such as sea level), respect to which changes of the surface characteristics can be determined. We suggest the possible algorithm of determining of the reference level. Also there is its comparison with the standard methods in this paper. This method was used for AFM data processing in the study of effects of natural and artificial factors on the surface of human tooth enamel. The paper presents the main results of this research and shows the necessity of additional data processing using the developed method.

Quantitative analysis of histology slides can bring unique knowledge about the investigated sample. Unfortunately this is time consuming procedure. In this paper we suggest method to overcome this disadvantage. However, everything has its price. Semi-automatic evaluation cannot beat human operator by its precision, but it is able to process big amount of data in short time. In some fields it can be useful property.

This paper considers the problem of vehicle video detection and tracking. A solution based on the partitioning a video into blocks of equal length and detecting objects in the first and last frames of the block is proposed. Matching of vehicle locations in the first and last frames helps detect pairs of locations of the same object. Reconstruction of vehicle locations in the intermediate frames allows restoring separate parts of motion tracks. Combination of consecutive segments by matching makes it possible to reconstruct a complete track. Analysis of detection quality shows a true positive rate of more than 75% including partially visible vehicles, while the average number of false positives per frame is less than 0.3. The results of tracking of separate vehicles show that objects are tracked to the final frame. For the majority of them the average overlapping percent is not less efficient than the currently used Lucas-Kanade and Tracking-Learning-Detection methods. The average tracking accuracy of all vehicles makes about 70%.

The problem of face photo retrieval using sketches constructed based on a description provided by a witness is discussed. The status of this problem from primary concepts and the used terminology, to modern technologies for constructing sketches, real scenarios and search results is reviewed. The development history of systems for constructing facial composites (identikits and sketches) and the ideas realized in these systems are provided. The task of automatically searching through a database of original photo images using a face sketch is discussed, and the reasons of low performance of such search in real-world scenarios are brought to light. Requirements to databases of sketches in addition to the existing benchmark face databases and also methods of creation of such databases are formulated. Within this framework the methods for generation a population of sketches from the initial sketch to improve the performance of sketch-based photo image retrieval systems are discussed. A method to increase the index of similarity in pairs sketch-photo based on computation of an average sketch from the generated population is provided. It is shown that such sketches are more similar to original photo images and their use in the discussed problem may lead to good results. But for all that, the created sketches meet the requirements of the truthful scenario as allow possibility of incomplete information in verbal descriptions. Results of experiments on CUHK Face Sketch and CUHK Face Sketch FERET databases and also open access sketches and corresponding photo images are discussed.

Solving image analysis problems is not restricted to the pure delineation of algorithms suitable to tackle the task at hand. Rather these also need to be made available to the users promptly and equipped with handy user interfaces to foster progress in the intended field of application. Alida is a software framework to advance the integrated development of algorithms and appropriate user interfaces. It automatically generates user interfaces for implemented algorithms, offers an automatic documentation of analysis procedures, and ships with a graphical editor for designing complex workflows. Alida’s Java implementation is licensed under GPL 3.0 and publicly available at http://www.informatik.uni-halle.de/alida.

Automatic recognition of biological structures like membranes or synapses is important to analyze organic processes and to understand their functional behavior. To achieve this, volumetric images taken by electron microscopy or computer tomography have to be segmented into meaningful semantic regions. We are extending iterative context forests which were developed for 2D image data to image stack segmentation. In particular, our method is able to learn high-order dependencies and import contextual information, which often can not be learned by conventional Markov random field approaches usually used for this task. Our method is tested on very different and challenging medical and biological segmentation tasks.

In this work we investigate the problem of robust dynamic facial expression recognition. We develop a complete pipeline that relies on the LBP-TOP descriptors and the Bag-of-Words (BoW) model for basic expressions classification. Experiments performed on the standard dataset such as the Extended Cohn-Kanade (CK+) database show that the developed approach achieves the average recognition rate of 97.7%, thus outperforming state-of-the-art methods in terms of accuracy. The proposed method is quite robust as it uses only relevant parts of video frames such as areas around mouth, noise, eyes, etc. Ability to work with arbitrary length sequence is also a plus for practical applications, since it means there is no need for complex temporal normalization methods.

In SCARA robots, which are often used in industrial applications, all joint axes are parallel, covering three degrees of freedom in translation and one degree of freedom in rotation. Therefore, conventional approaches for the hand-eye calibration of articulated robots cannot be used for SCARA robots. In this paper, we present a new linear method that is based on dual quaternions and extends the work of Daniilid is 1999 (IJRR) for SCARA robots. To improve the accuracy, a subsequent nonlinear optimization is proposed. We address several practical implementation issues and show the effectiveness of the method by evaluating it on synthetic and real data.

Commercial gaze-tracking devices provide accurate measurements of the visual gaze and are applied to a broad range of problems in marketing, human-computer interaction, and health care technology. In some applications commercial systems are either unavailable or unaffordable. Therefore, developing low cost solutions using off the shelf components is worthwhile. In the paper at hand, we apply a hierarchy of Gaussian processes, a class of probabilistic function regressors, to the problem of visual gaze-tracking for consumer grade cameras. Gaussian process latent variable models lead to a lower dimensional manifold which represents the gaze space. Finally, a Gaussian process mapping from screen coordinates to gaze manifold enables us to seek for the users visual gaze point given a previously unseen eye-patch. In our experiments, we achieve mean errors of approximately 2 cm for a consumer grade webcam that is positioned 30-40 cm in front of the user.

A correction method of systematic error of observations of color coordinates of a photosensor array allowing correcting and estimating the colors of each pixel in the image is proposed. The estimates of color coordinates of uniform color are calculated according to the results of observation of the colors of individual pixels based on the covariance matrix of the observation errors and model of dependence of the observed color coordinates on genuine ones.

An algorithm is proposed for compression of indexed raster images (IRI) based on statistical coding using context simulation. A model and methods are developed in order to create an effective algorithm for compression of indexed graphic information. A detailed consideration is given to methods for increasing the compression ratio. The optimization of the algorithm, including computation parallelization, is presented. The proposed algorithm is compared with other universal and specialized compression algorithms.

The paper puts forward two theorems underlying a method of coordination of the descriptions of contours for the equivalence class with the group of affine transformations. High speed performance of the method is achieved by excluding the calculations of similarity estimates. Fundamentals of the method are explained by examples of coordination of the descriptions of contours of trapezoids.

In this work, a model of a geoacoustic emission signal constructed on the basis of methods of sparse approximation is presented and a model identification algorithm is proposed. The authors show that the simulation results make it possible to perform the classification of structural elements of the signal using symbolic approximation in the frequency domain.