KEYWORDS: Visualization, Data hiding, RGB color model, Steganography, Chemical elements, Information visualization, Computer security, Information security, Computing systems, Scientific research
In this paper we’ll describe new techniques of information hiding in visual containers. Presented methods will be connected with the carrier features, so secret distribution will be dependent from the content and pixel values. In traditional secret hiding solutions the way of information distribution is not dependent on the content or container’s features. Such approaches simply modify spatial or frequency parameters, without consideration of local carrier parameters. Such parameters can also determine the way of secret hiding and allow to split information over container in different manner, especially when we consider containers with different graphical features.
KEYWORDS: Clouds, Data processing, Computer security, Data analysis, Visualization, Classification systems, Fiber optic gyroscopes, Computing systems, Information security, Analytical research
In this paper will be presented new possible applications of cognitive vision or information systems for secure information distribution and management in Cloud infrastructures. Cognitive systems are dedicated to visual data interpretation and semantic analysis. Among new possibilities of application arise secure data distribution and management in distributed computer infrastructures like Cloud and Fog computing. In this paper cryptographic approaches for data division will be described, which allow to improve processes of secure and efficient information fusion with application of cognitive procedures. A few possible application of proposed approaches for visual information encryption and distribution will be also described.
In this paper will be presented new opportunities for developing innovative solutions based on blockchain technologies in creation of new data management algorithms, especially those focused on the distribution and processing of data in layered structures, where independent instances can fully control and verify all operations. Later an attempt will be made to generalize protocols, which will be proposed for management activities, towards their extension and the possibility of using them in hierarchical structures. Blockchain technology should enable the development of decentralized management protocols that will allow complete verifiability of all operations by all authorized parties that can collect, process and transmit such information. In practice such protocols will give the opportunity to create an effective and secure data sharing protocols in structures, where all authorized entities are equal and can independently verify the type of operations and instances that process such data.
In this paper will be presented classification of new cognitive information systems dedicated to cryptographic data
splitting and sharing processes. Cognitive processes of semantic data analysis and interpretation, will be used to describe
new classes of intelligent information and vision systems. In addition, cryptographic data splitting algorithms and
cryptographic threshold schemes will be used to improve processes of secure and efficient information management with
application of such cognitive systems. The utility of the proposed cognitive sharing procedures and distributed data
sharing algorithms will be also presented. A few possible application of cognitive approaches for visual information
management and encryption will be also described.
In this paper will be presented new opportunities for developing innovative solutions for semantic pattern classification
and visual cryptography, which will base on cognitive and bio-inspired approaches. Such techniques can be used for
evaluation of the meaning of analyzed patterns or encrypted information, and allow to involve such meaning into the
classification task or encryption process. It also allows using some crypto-biometric solutions to extend personalized
cryptography methodologies based on visual pattern analysis. In particular application of cognitive information systems
for semantic analysis of different patterns will be presented, and also a novel application of such systems for visual secret
sharing will be described. Visual shares for divided information can be created based on threshold procedure, which may
be dependent on personal abilities to recognize some image details visible on divided images.
KEYWORDS: Kinematics, Principal component analysis, Data modeling, Motion models, 3D modeling, Visualization, Multimedia, Data acquisition, Visual process modeling, Cognitive modeling
The motivation for this paper is to initially propose and evaluate two new kinematics models that were developed to describe motion capture (MoCap) data of karate techniques. We decided to develop this novel proposition to create the model that is capable to handle actions description both from multimedia and professional MoCap hardware. For the evaluation purpose we have used 25-joints data with karate techniques recordings acquired with Kinect version 2. It is consisted of MoCap recordings of two professional sport (black belt) instructors and masters of Oyama Karate. We have selected following actions for initial analysis: left-handed furi-uchi punch, right leg hiza-geri kick, right leg yoko-geri kick and left-handed jodan-uke block. Basing on evaluation we made we can conclude that both proposed kinematics models seems to be convenient method for karate actions description. From two proposed variables models it seems that global might be more useful for further usage. We think that because in case of considered punches variables seems to be less correlated and they might also be easier to interpret because of single reference coordinate system. Also principal components analysis proved to be reliable way to examine the quality of kinematics models and with the plot of the variable in principal components space we can nicely present the dependences between variables.
The aim of this paper is to present the novel proposition of the human motion modelling and recognition approach that enables real time MoCap signal evaluation. By motions (actions) recognition we mean classification. The role of this approach is to propose the syntactic description procedure that can be easily understood, learnt and used in various motion modelling and recognition tasks in all MoCap systems no matter if they are vision or wearable sensor based. To do so we have prepared extension of Gesture Description Language (GDL) methodology that enables movements description and real-time recognition so that it can use not only positional coordinates of body joints but virtually any type of discreetly measured output MoCap signals like accelerometer, magnetometer or gyroscope. We have also prepared and evaluated the cross-platform implementation of this approach using Lua scripting language and JAVA technology. This implementation is called Data Driven GDL (DD-GDL). In tested scenarios the average execution speed is above 100 frames per second which is an acquisition time of many popular MoCap solutions.
Gesture Description Language (GDL) is a classifier that enables syntactic description and real time recognition of full-body gestures and movements. Gestures are described in dedicated computer language named Gesture Description Language script (GDLs). In this paper we will introduce new GDLs formalisms that enable recognition of selected classes of movement trajectories. The second novelty is new unsupervised learning method with which it is possible to automatically generate GDLs descriptions. We have initially evaluated both proposed extensions of GDL and we have obtained very promising results. Both the novel methodology and evaluation results will be described in this paper.
The main contribution of this article is to evaluate the utility of different state-of-the-art deformable contour models for segmenting carotid lumen walls from computed tomography angiography images. We have also proposed and tested a new tracking-based lumen segmentation method based on our evaluation results. The deformable contour algorithm (snake) is used to detect the outer wall of the vessel. We have examined four different snakes: with a balloon, distance, and a gradient vector flow force and the method of active contours without edges. The algorithms were evaluated on a set of 32 artery lumens—16 from the common carotid artery (CCA)-the internal carotid artery section and 16 from the CCA-the external carotid artery section—in order to find the optimum deformable contour model for this task. Later, we evaluated different values of energy terms in the method of active contours without edges, which turned out to be the best for our dataset, in order to find the optimal values for this particular segmentation task. The choice of particular weights in the energy term was evaluated statistically. The final Dice’s coefficient at the level of 0.939±0.049 puts our algorithm among the best state-of-the-art methods for these solutions.
The original contribution is to propose an intensity-based segmentation algorithm for extracting the carotid artery bifurcation region and validate the proposed solution on real patients’ CTA data. The proposed homogeneity criteria allow the production of locally smooth segmentations and prevent excessive growth into neighboring tissues of similar densities. The obtained segmentation results are compared to manual findings of a radiologist and measured with the Dice similarity coefficient (D si ). This technique has been shown to be a reliable tool as effective as top state-of-the-art methods (D si =93.6%±3.5% ).
The proposed framework for cognitive analysis of perfusion computed tomography images is a fusion of image processing, pattern recognition, and image analysis procedures. The output data of the algorithm consists of: regions of perfusion abnormalities, anatomy atlas description of brain tissues, measures of perfusion parameters, and prognosis for infracted tissues. That information is superimposed onto volumetric computed tomography data and displayed to radiologists. Our rendering algorithm enables rendering large volumes on off-the-shelf hardware. This portability of rendering solution is very important because our framework can be run without using expensive dedicated hardware. The other important factors are theoretically unlimited size of rendered volume and possibility of trading of image quality for rendering speed. Such rendered, high quality visualizations may be further used for intelligent brain perfusion abnormality identification, and computer aided-diagnosis of selected types of pathologies.
Paper presents some new ideas introducing automatic understanding of the medical images semantic content. The idea under consideration can be found as next step on the way starting from capturing of the images in digital form as two-dimensional data structures, next going throw images processing as a tool for enhancement of the images visibility and readability, applying images analysis algorithms for extracting selected features of the images (or parts of images e.g. objects), and ending on the algorithms devoted to images classification and recognition. In the paper we try to explain, why all procedures mentioned above can not give us full satisfaction in many important medical problems, when we do need understand image semantic sense, not only describe the image in terms of selected features and/or classes. The general idea of automatic images understanding is presented as well as some remarks about the successful applications of such ideas for increasing potential possibilities and performance of computer vision systems dedicated to advanced medical images analysis. This is achieved by means of applying linguistic description of the picture merit content. After this we try use new AI methods to undertake tasks of the automatic understanding of images semantics in intelligent medical information systems. A successful obtaining of the crucial semantic content of the medical image may contribute considerably to the creation of new intelligent multimedia cognitive medical systems. Thanks to the new idea of cognitive resonance between stream of the data extracted form the image using linguistic methods and expectations taken from the representation of the medical knowledge, it is possible to understand the merit content of the image even if the form of the image is very different from any known pattern.
This paper presents new opportunities for applying linguistic description of the picture merit content and AI methods to undertake tasks of the automatic understanding of images semantics in intelligent medical information systems. A successful obtaining of the crucial semantic content of the medical image may contribute considerably to the creation of new intelligent multimedia cognitive medical systems. Thanks to the new idea of cognitive resonance between stream of the data extracted from the image using linguistic methods and expectations taken from the representaion of the medical knowledge, it is possible to understand the merit content of the image even if teh form of the image is very different from any known pattern. This article proves that structural techniques of artificial intelligence may be applied in the case of tasks related to automatic classification and machine perception based on semantic pattern content in order to determine the semantic meaning of the patterns. In the paper are described some examples presenting ways of applying such techniques in the creation of cognitive vision systems for selected classes of medical images. On the base of scientific research described in the paper we try to build some new systems for collecting, storing, retrieving and intelligent interpreting selected medical images especially obtained in radiological and MRI examinations.
This paper presents and discusses possibilities of application of selected algorithms belonging to the group of syntactic methods of patten recognition used to analyze and extract features of shapes and to diagnose morphological lesions seen on selected medical images. This method is particularly useful for specialist morphological analysis of shapes of selected organs of abdominal cavity conducted to diagnose disease symptoms occurring in the main pancreatic ducts, upper segments of ureters and renal pelvis. Analysis of the correct morphology of these organs is possible with the application of the sequential and tree method belonging to the group of syntactic methods of pattern recognition. The objective of this analysis is to support early diagnosis of disease lesions, mainly characteristic for carcinoma and pancreatitis, based on examinations of ERCP images and a diagnosis of morphological lesions in ureters as well as renal pelvis based on an analysis of urograms. In the analysis of ERCP images the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis, while in the case of kidney radiogram analysis the aim is to diagnose local irregularities of ureter lumen and to examine the morphology of renal pelvis and renal calyxes. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of description of features of shapes and context-free sequential attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing of width diagrams of the examined structures. Additionally, in order to support the analysis of the correct structure of renal pelvis a method using the tree grammar for syntactic pattern recognition to define its correct morphological shapes has been presented.
The paper presents specialist algorithms of morphologic analysis of shapes of selected organs of abdominal cavity proposed in order to diagnose disease symptoms occurring in the main pancreatic ducts and upper segments of ureters. Analysis of the correct morphology of these structures has been conducted with the use of syntactic methods of pattern recognition. Its main objective is computer-aided support to early diagnosis of neoplastic lesions and pancreatitis based on images taken in the course of examination with the endoscopic retrograde cholangiopancreatography (ERCP) method and a diagnosis of morphological lesions in ureter based on kidney radiogram analysis. In the analysis of ERCP images, the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis. In the case of kidney radiogram analysis the aim is to diagnose local irregularity of ureter lumen. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of shape features description and context-free attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing into diagrams of widths of the examined structures.
The presented paper treats a subject of elaboration of new algorithms for recognition of lesions and analysis of shape features of selected abdominal cavity organs visible on radiograms or tomograms. The aim of the methods is to determine and examine morphological shapes of the analyzed anatomical structures in order to diagnose cancerous lesions and inflammatory processes. The formulated target was accomplished in the case of the diagnosis of cancer and chronic inflammation of the pancreas made on the base of X- ray images obtained during the ERCP examinations. For this purpose an effective algorithm for thresholding of the ERCP images was employed. Hence it was possible to extract the pancreas duct together with morphological changes which could occur. Then, thanks to determination and application of special sequence of geometric operations (skeletonizing and rotations of contour points about a skeleton), a linear graph representing the width of pancreas duct and showing morphological changes was obtained. In order to find these changes the context-free attributed grammars, enabling description of all searched morphological changes were used. These attributes contained an additional information (height and width of the discovered change) used for recognition of ambiguous cases. For proper description and recognition of symptoms, for which the 2D analysis is required (i.e. e.g. large cavernous bulges), the language of shape features description with a special multidirectional sinquad distribution were employed. Research on usefulness of the proposed methods, performed so far, justified the application of syntactic methods to recognition of medical images, especially to support medical diagnostics.
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