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In this work, we approach the analysis and segmentation of natural textured images by combining ideas from image analysis and probabilistic modeling. We rely on AM-FM texture models and, specifically, on the Dominant Component Analysis (DCA) paradigm for feature extraction. This method provides a low-dimensional, dense, and smooth descriptor, capturing the essential aspects of texture, namely, scale, orientation, and contrast. Our contributions are at three levels of the texture analysis and segmentation problems: First, at the feature extraction stage, we propose a Regularized Demodulation Algorithm that provides more robust texture features and we explore the merits of modifying the channel selection criterion of DCA. Second, we propose a probabilistic interpretation of DCA and Gabor filtering in general, in terms of Local Generative Models. Extending this point of view to edge detection facilitates the estimation of posterior probabilities for the edge and texture classes. Third, we propose the Weighted Curve Evolution scheme that enhances curve evolution-based segmentation methods by allowing for the locally adaptive combination of heterogeneous cues. Our segmentation results are evaluated on the Berkeley Segmentation Benchmark and compare favorably to current state-of-the-art methods. Index Terms--Texture analysis, image segmentation, AM-FM models, demodulation, generative models, curve evolution, cue combination.

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported. Index Terms--Audio activity detection, cross-correlation, cross-power spectral density, dialogue detection, indicator functions, speaker clustering.

The ability to accurately locate the boundaries of speech activity is an important attribute of any modern speech recognition, processing, or transmission system. The effort in this paper is the development of efficient, sophisticated features for speech detection in noisy environments, using ideas and techniques from recent advances in speech modeling and analysis, like presence of modulations in speech formants, energy separation and multiband filtering. First we present a method, conceptually based on a classic speech-silence discrimination procedure, that uses some newly developed, short-time signal analysis tools and provide for it a detection theoretic motivation. The new energy and spectral content representations are derived through filtering the signal in various frequency bands, estimating the Teager-Kaiser Energy for each and demodulating the most active one in order to derive the signal's dominant AM-FM components. This modulation approach demonstrated an improved robustness in noise over the classic algorithm, reaching an average error reduction of 33.5 % under 5-30-dB noise. Second, by incorporating alternative modulation energy features in voice activity detection, improvement in overall misclassification error of a high hit rate detector reached 7.5 % and 9.5 % on different benchmarks. [ABSTRACT FROM AUTHOR]

Asymmetric 3D to 2D face recognition has gained attention from the research community since the real-world application of 3D to 3D recognition is limited by the unavailability of inexpensive 3D data acquisition equipment. A 3D to 2D face recognition system explicitly relies on 3D facial data to account for uncontrolled image conditions related to head pose or illumination. We build upon such a system, which matches relit gallery textures with pose-normalized probe images, using the gallery facial meshes. The relighting process, however, is based on an assumption of indoor lighting conditions and limits recognition performance on outdoor images. In this paper, we propose a novel method for minimizing illumination difference by unlighting a 3D face texture via albedo estimation using lighting maps. The algorithm is evaluated on challenging databases (UHDB30, UHDB11, FRGC v2.0) with drastic lighting and pose variations. The experimental results demonstrate the robustness of our method for estimating the albedo from both indoor and outdoor captured images, and the effectiveness and efficiency for illumination normalization in face recognition.