Abstract The paper presents a fast algorithm for eye localization from thermal images. Due to blurred edges, lower quality of thermal images compared with visible-light images, the process may be more complicated. On the other hand, it seems relatively easy to designate specific areas of the eyes because of their relatively highest brightness resulting from the highest temperature in these face areas. It turns out, however, that the highest pixel brightness does not always determine unequivocally the position of the eyes. Therefore, the algorithm described here proposes a specialized method for segmentation of images containing eye areas and a specialized classifier. The first part of the algorithm is a block for generating features describing eye areas. The second stage is a decision block built based on a neural network allowing for effective classification of pre-designated areas. A suitable combination of these blocks into a single system allowed for correct analysis of images with rich geometric diversity (scale, position, orientation) and brightness distribution, reaching more than 91 % of correct localizations and time of analysis of a single image of hundredths of a second. Algorithms of this type can be used as the initial stages of facial recognition, emotion recognition, quantitative analysis of the face or modern human-computer interfaces. The described algorithm may be used for both conventional thermal images and thermograms (after prior conversion of temperature values into brightness values), which affects its potential application in medicine. The benefits of this study are twofold: fast localization of characteristic points of the eyes and the possibility of processing despite the typical problems of thermovision and image transformations.

A new capable content discovery platform based on multimedia data enrichment is presented in this paper. The platform, known as the IMCOP system, refers to the concept of intelligent discovery and delivery of multimedia content. Relevant state-of-the-art solutions are described in detail in the background section. The overall architecture and the main components of the IMCOP system are presented next. An original concept of Complex Multimedia Objects which extend the MPEG-7 standard to hold the processed data and bind it into content related collections is introduced. Selected results of tests illustrating how the IMCOP system performs in terms of responsiveness and stability under a particular workload are reported. Finally, IMCOP’s advantages in reference to other content discovery platforms are discussed and summed up.

Intelligent Transportation Systems (ITS) aim to improve safety, mobility and environmental performance of road transport. The INSIGMA project provides a fresh look at the possible innovations in this field, by enhancing the functionality and accuracy of ITS in urban environments. This paper describes the architecture, sensors, processing algorithms, output modules and advantages of the developed system. A comparison of existing ITS systems has been provided as background. Special attention has been given to performance and privacy issues, as the system includes social aspects such as location monitoring.

The security of citizens constitutes an issue of primary importance. Constant improvements in security should be one of the strategic activities of any government and its agencies such as police, city police, firemen, local administration, etc.

In this paper we propose a novel approach for pattern recognition problems with non-uniform classes of images. The main concept of this classification method is to describe classes of images with their fuzzy portraits. This approach is a good generalization of the algorithm. The fuzzy set is calculated as a preliminary result of the algorithm before the final decision or rejection that solves the problem of uncertainty at the boundaries of classes. We use the method to solve the problem of knife detection in still images. The main aim of this paper is to test fuzzy classification with feature vectors in a real environment. We used selected MPEG-7 descriptor schemes as feature vectors. The method was experimentally validated on a dataset of over 12,000 images. The article describes the results of six experiments which confirm the accuracy of our method. In addition we conducted a test with enhanced images, achieved with two state-of-the-art super-resolution algorithms.

The contemporary world presents us with numerous dangers and challenges. We hear about traffic accidents, crimes, confrontations between football hooligans, building disasters, etc. on the news almost every day. In addition, terrorism has become a growing threat. In order to prevent and combat such hazards, new intelligent solutions are required. Traditional urban monitoring and surveillance systems, with operators overseeing any activities taking place, are no longer sufficient, mainly because of the petabytes of data per minute that need to be analyzed. Such a vast volume of data in current urban monitoring systems is the result of widespread and growing use of various sensors, mainly digital cameras and microphones. Therefore, new intelligent approaches for image and audio processing and recognition are vital for modern security systems; in turn, they would not be effective without innovative data encryption and protection techniques, including those relying on digital watermarking.

In this paper, a novel application of Active Appearance Models to detecting knives in images is presented. In contrast to its popular applications in face segmentation and medical image analysis, we not only use this computer vision algorithm to locate an object that is known to exist in an analysed image, but–using an interest point typical of knives–also try to identify whether or not a knife exists in the image in question. We propose an entire detection scheme and examine its performance on a sample test set. The work presented in this paper aims to create a robust visual knife-detector to be used in security applications.

In this paper we present real data bit error rate (BER) performance evaluation of the recently proposed correlation-and-bit-aware improved spread spectrum (CAISS) watermarking scheme (Valizadeh and Wang, IEEE Trans Inf Forensics Secur 6(2):267–282, 2011 ). Our tests were performed in discrete cosine transform (DCT) domain. The results show significant improvement as compared with traditional spread spectrum technique. Tests performed under medium JPEG compression and fixed peak signal-to-noise ratio (PSNR) indicate that appropriate choice of CAISS parameters allows for over a three orders of magnitude smaller BER compared to spread spectrum technique without side information about correlation. CAISS also significantly outperforms traditional spread spectrum when watermarked images are subjected to additive Gaussian noise or downscaled before watermark decoding. We also compared CAISS with the improved spread spectrum (ISS) scheme and found that CAISS can perform significantly better than ISS (in terms of BER) after medium JPEG compression but gives almost the same results in presence of additive Gaussian noise and image scaling.

This paper deals with the design of a practical self-recovery mechanism for lossy compressed JPEG images. We extend a recently proposed model of the content reconstruction problem based on digital fountain codes to take into account the impact of emerging watermark extraction and block classification errors. In contrast to existing methods, our scheme guarantees a high and stable level of reconstruction quality. Instead of introducing reconstruction artifacts, emerging watermark extraction errors penalize the achievable tampering rates. We introduce new mechanisms that allow for handling high-resolution and color images efficiently. In order to analyze the behavior of our scheme, we derive an improved model to calculate the reconstruction success probability. We introduce a new hybrid mechanism for spreading the reference information over the entire image, which allows to find a good balance between the achievable tampering rates and the computational complexity. Such an approach reduced the watermark embedding time from the order of several minutes to the order of single seconds, even on mobile devices.

Multimedia, Communications, Services and Security" has been an important research topic in recent years and a key part of innovative ICT projects.In particular, numerous intelligent systems benefit from the integration of tools and solutions proposed by scientists, such as image, video and audio processing, development of intelligent sensors, interlinking heterogeneous subsystems with intra-and internet services, and providing reliable and secure communications channels.This special issue consists of eleven papers, which are briefly discussed as follows.In "Detection and Localization of Selected Acoustic Events in Acoustic Fields for Smart Surveillance Applications," the authors provide a method for determining the position of selected sound events in 3D space.When an event is detected (e.g.speech or impulse sound), the sound source is located so that it can be used in surveillance systems.The following two papers are related to Quality of Experience issues."Quality Assessment of Visual and Automatic Licence Plate Recognition" discusses the Quality of Experience in Automatic Licence Plate Recognition tasks based on surveillance videos streamed under constrained networking conditions.Based on the presented assumptions, the authors implement automatic recognition algorithms and compare their accuracy against user perception in order to obtain quality models and critical quality thresholds."A Journey from Subjective Psychophysical Experiments to Objective Quality Optimisation" presents two task-based video experiments in surveillance and medical applications.The problem of quality assessment, from subjective psychophysical experiments to objective quality models, is discussed.Example test results and models are provided.

Annotation watermarking is a technique that allows to associate content descriptions with digital images in a persistent and format independent manner. It is commonly used in medical applications and, hence, existing schemes have been designed to meet rigorous watermark transparency requirements. As a result, the effective capacity of such schemes is severely limited. In this paper, we present a new approach to annotation watermarking. We adopt the fountain coding paradigm and design a convenient watermark communication architecture which resembles a traditional packet network. Our approach allows for straightforward incorporation of content adaptivity, robustness against cropping and support for multiple data streams. In our study, we focus on high-capacity annotations and we assume different requirements with respect to the fidelity of the watermarked images. Our scheme is robust against lossy JPEG compression and cropping. This paper describes the principles of the proposed approach and presents the results of it’s experimental evaluation.

Intelligent monitoring is currently one of the most prominent research areas. Numerous aspects of such schemes need to be addressed by implementation of various modules covering a wide range of algorithms, beginning from video analytic modules, through quality assessment, up to integrity verification. The goal of this paper is to provide a brief overview of the most recent research results regarding various aspects of the video surveillance processing chain. Specifically, the paper describes a scheme for automatic recognition of the make and model of passing vehicles, the state-of-the-art in quality assessment for recognition tasks, and a system for verification of digital evidence integrity. Concluding remarks highlight the perspectives for further development of the described techniques, and the related research directions.

This paper presents a new model of the content reconstruction problem in self-embedding systems, based on an erasure communication channel. We explain why such a model is a good fit for this problem, and how it can be practically implemented with the use of digital fountain codes. The proposed method is based on an alternative approach to spreading the reference information over the whole image, which has recently been shown to be of critical importance in the application at hand. Our paper presents a theoretical analysis of the inherent restoration trade-offs. We analytically derive formulas for the reconstruction success bounds, and validate them experimentally with Monte Carlo simulations and a reference image authentication system. We perform an exhaustive reconstruction quality assessment, where the presented reference scheme is compared to five state-of-the-art alternatives in a common evaluation scenario. Our paper leads to important insights on how self-embedding schemes should be constructed to achieve optimal performance. The reference authentication system designed according to the presented principles allows for high-quality reconstruction, regardless of the amount of the tampered content. The average reconstruction quality, measured on 10000 natural images is 37 dB, and is achievable even when 50% of the image area becomes tampered.

In this paper we analyze the content reconstruction problem with the use of a revised erasure communication channel. Based on this approach, we propose a reconfigurable self-embedding system which can be adapted to different requirements. Our approach eliminates two major problems with the design of efficient content reconstruction algorithms and allows for theoretical analysis of the reconstruction performance. The presented theoretical results are verified using Monte Carlo simulations. The proposed scheme is experimentally evaluated in a number of possible configurations, and allows to achieve high reconstruction quality even with high tampering rates.

In this paper we address the issue of the trade-off between the tampering rate and the reconstruction quality of image authentication systems. We adopt the fountain coding paradigm and design an adaptive content reconstruction scheme. The scheme conforms the reconstruction quality of individual image fragments both to the local texture properties and to the specified requirements. Experimental evaluation confirms that a framework based on this approach is a valid and convenient model of the performance of the considered reconstruction problem.

Multimedia files often contain fragments with sensitive content that should not be visible to everyone. Such content is usually censored prior to distribution. We propose a technique that allows to blur selected fragments of the image while retaining details necessary for original appearance reconstruction in the image itself. The information is retained by means of a digital watermark. The protected image can be viewed in ordinary viewers and a dedicated decoder is required for content restoration. The decoder ensures that only authorized recipients are able to see the protected fragments. We evaluate the proposed approach in terms of protection capacity and reconstruction quality also under lossy compression. The results show that high image quality is achievable and the proposed scheme might be beneficial for certain applications. Moderate complexity of the decoding process makes it feasible for real-time usage scenarios.