Ioannis Pitas

Nonlinear Digital Filters

1. Introduction.- 2. Statistical preliminaries.- 3. Image formation.- 4. Median filters.- 5. Digital filters based on order statistics.- 6. Morphological image and signal processing.- 7. Homomorphie filters.- 8. Polynomial filters.- 9. Adaptive nonlinear filters.- 10. Generalizations and new trends.- 11. Algorithms and architectures.

Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines

In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection

Robust image watermarking in the spatial domain

The rapid evolution of digital image manipulation and transmission techniques has created a pressing need for the protection of the intellectual property rights on images. A copyright protection method that is based on hiding an ‘invisible’ signal, known as digital watermark, in the image is presented in this paper. Watermark casting is performed in the spatial domain by slightly modifying the intensity of randomly selected image pixels. Watermark detection does not require the existence of the original image and is carried out by comparing the mean intensity value of the marked pixels against that of the pixels not marked. Statistical hypothesis testing is used for this purpose. Pixel modifications can be done in such a way that the watermark is resistant to JPEG compression and lowpass filtering. This is achieved by minimizing the energy content of the watermark signal at higher frequencies while taking into account properties of the human visual system. A variation that generates image dependent watermarks as well as a method to handle geometrical distortions are presented. An extension to color images is also pursued. Experiments on real images verify the effectiveness of the proposed techniques.

Order statistics in digital image processing

A family of nonlinear filters based on order statistics is presented. A mathematical tool derived through robust estimation theory, order statistics has allowed engineers to develop nonlinear filters with excellent robustness properties. These filters are well suited to digital image processing because they preserve the edges and the fine details of an image much better than conventional linear filters. The probabilistic and deterministic properties of the best known and most widely used filter in this family, the median filter, are discussed. In addition, the authors consider filters that, while not based on order statistics, are related to them through robust estimation theory. A table that ranks nonlinear filters under a variety of performance criteria is included. Most of the topics treated are very active research areas, and the applications are varied, including HDTV, multichannel signal processing of geophysical and ECG/EEG data, and a variety of telecommunications applications. >

A novel method for automatic face segmentation, facial feature extraction and tracking

The present paper describes a novel method for the segmentation of faces, extraction of facial features and tracking of the face contour and features over time. Robust segmentation of faces out of complex scenes is done based on color and shape information. Additionally, face candidates are verified by searching for facial features in the interior of the face. As interesting facial features we employ eyebrows, eyes, nostrils, mouth and chin. We consider incomplete feature constellations as well. If a face and its features are detected once reliably, we track the face contour and the features over time. Face contour tracking is done by using deformable models like snakes. Facial feature tracking is performed by block matching. The success of our approach was verified by evaluating 38 different color image sequences, containing features as beard, glasses and changing facial expressions.

A method for signature casting on digital images

Signature (watermark) casting on digital images is an important problem, since it affects many aspects of the information market. We propose a method for casting digital watermarks on images and we analyze its effectiveness. The satisfaction of some basic demands in this area is examined and a method for producing digital watermarks is proposed. Moreover, immunity to subsampling is examined and simulation results are provided for the verification of the above mentioned topics.

Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification

In this paper, two supervised methods for enhancing the classification accuracy of the Nonnegative Matrix Factorization (NMF) algorithm are presented. The idea is to extend the NMF algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The first method employs discriminant analysis in the features derived from NMF. In this way, a two-phase discriminant feature extraction procedure is implemented, namely NMF plus Linear Discriminant Analysis (LDA). The second method incorporates the discriminant constraints inside the NMF decomposition. Thus, a decomposition of a face to its discriminant parts is obtained and new update rules for both the weights and the basis images are derived. The introduced methods have been applied to the problem of frontal face verification using the well-known XM2VTS database. Both methods greatly enhance the performance of NMF for frontal face verification

Information theory-based shot cut/fade detection and video summarization

New methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. The method for shot boundary detection relies on the mutual information (MI) and the joint entropy (JE) between the frames. It can detect cuts, fade-ins and fade-outs. The detection technique was tested on the TRECVID2003 video test set having different types of shots and containing significant object and camera motion inside the shots. It is demonstrated that the method detects both fades and abrupt cuts with high accuracy. The information theory measure provides us with better results because it exploits the inter-frame information in a more compact way than frame subtraction. It was also successfully compared to other methods published in literature. The method for key frame extraction uses MI as well. We show that it captures satisfactorily the visual content of the shot.

Image watermarking using DCT domain constraints

Watermarking algorithms are used for image copyright protection. The algorithms proposed select certain blocks in the image based on a Gaussian network classifier. The pixel values of the selected blocks are modified such that their discrete cosine transform (DCT) coefficients fulfil a constraint imposed by the watermark code. Two different constraints are considered. The first approach consists of embedding a linear constraint among selected DCT coefficients and the second one defines circular detection regions in the DCT domain. A rule for generating the DCT parameters of distinct watermarks is provided. The watermarks embedded by the proposed algorithms are resistant to JPEG compression.

Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication

A novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed. The starting point is to weigh the local similarity values at the nodes of an elastic graph according to their discriminatory power. Powerful and well-established optimization techniques are used to derive the weights of the linear combination. More specifically, we propose a novel approach that reformulates Fishers discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining statistical pattern recognition and support vector machines (SVM). Both linear and nonlinear SVM are then constructed to yield the optimal separating hyperplanes and the optimal polynomial decision surfaces, respectively. The method has been applied to frontal face authentication on the M2VTS database. Experimental results indicate that the performance of morphological elastic graph matching is highly improved by using the proposed weighting technique.