Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Artificial Intelligence for Multimedia Signal Processing

Artificial intelligence technologies are also actively applied to broadcasting and multimedia processing technologies. A lot of research has been conducted in a wide variety of fields, such as content creation, transmission, and security, and these attempts have been made in the past two to three years to improve image, video, speech, and other data compression efficiency in areas related to MPEG media processing technology. Additionally, technologies such as media creation, processing, editing, and creating scenarios are very important areas of research in multimedia processing and engineering. This book contains a collection of some topics broadly across advanced computational intelligence algorithms and technologies for emerging multimedia signal processing as: Computer vision field, speech/sound/text processing, and content analysis/information mining

Local features for view matching across independently moving cameras.

PhD ThesisMoving platforms, such as wearable and robotic cameras, need to recognise the same place observed from different viewpoints in order to collaboratively reconstruct a 3D scene and to support augmented reality or autonomous navigation. However, matching views is challenging for independently moving cameras that directly interact with each other due to severe geometric and photometric differences, such as viewpoint, scale, and illumination changes, can considerably decrease the matching performance. This thesis proposes novel, compact, local features that can cope with with scale and viewpoint variations. We extract and describe an image patch at different scales of an image pyramid by comparing intensity values between learnt pixel pairs (binary test), and employ a cross-scale distance when matching these features. We capture, at multiple scales, the temporal changes of a 3D point, as observed in the image sequence of a camera, by tracking local binary descriptors. After validating the feature-point trajectories through 3D reconstruction, we reduce, for each scale, the sequence of binary features to a compact, fixed-length descriptor that identifies the most frequent and the most stable binary tests over time. We then propose XC-PR, a cross-camera place recognition approach that stores locally, for each uncalibrated camera, spatio-temporal descriptors, extracted at a single scale, in a tree that is selectively updated, as the camera moves. Cameras exchange descriptors selected from previous frames within an adaptive temporal window and with the highest number of local features corresponding to the descriptors. The other camera locally searches and matches the received descriptors to identify and geometrically validate a previously seen place. Experiments on different scenarios show the improved matching accuracy of the joint multi-scale extraction and temporal reduction through comparisons of different temporal reduction strategies, as well as the cross-camera matching strategy based on Bag of Binary Words, and the application to several binary descriptors. We also show that XC-PR achieves similar accuracy but faster, on average, than a baseline consisting of an incremental list of spatio-temporal descriptors. Moreover, XC-PR achieves similar accuracy of a frame-based Bag of Binary Words approach adapted to our approach, while avoiding to match features that cannot be informative, e.g. for 3D reconstruction

Signal Processing on Textured Meshes

In this thesis we extend signal processing techniques originally formulated in the context of image processing to techniques that can be applied to signals on arbitrary triangles meshes. We develop methods for the two most common representations of signals on triangle meshes: signals sampled at the vertices of a finely tessellated mesh, and signals mapped to a coarsely tessellated mesh through texture maps. Our first contribution is the combination of Lagrangian Integration and the Finite Elements Method in the formulation of two signal processing tasks: Shock Filters for texture and geometry sharpening, and Optical Flow for texture registration. Our second contribution is the formulation of Gradient-Domain processing within the texture atlas. We define a function space that handles chart discontinuities, and linear operators that capture the metric distortion introduced by the parameterization. Our third contribution is the construction of a spatiotemporal atlas parameterization for evolving meshes. Our method introduces localized remeshing operations and a compact parameterization that improves geometry and texture video compression. We show temporally coherent signal processing using partial correspondences

Machine learning based small bowel video capsule endoscopy analysis: Challenges and opportunities

YesVideo capsule endoscopy (VCE) is a revolutionary technology for the early diagnosis of gastric disorders. However, owing to the high redundancy and subtle manifestation of anomalies among thousands of frames, the manual construal of VCE videos requires considerable patience, focus, and time. The automatic analysis of these videos using computational methods is a challenge as the capsule is untamed in motion and captures frames inaptly. Several machine learning (ML) methods, including recent deep convolutional neural networks approaches, have been adopted after evaluating their potential of improving the VCE analysis. However, the clinical impact of these methods is yet to be investigated. This survey aimed to highlight the gaps between existing ML-based research methodologies and clinically significant rules recently established by gastroenterologists based on VCE. A framework for interpreting raw frames into contextually relevant frame-level findings and subsequently merging these findings with meta-data to obtain a disease-level diagnosis was formulated. Frame-level findings can be more intelligible for discriminative learning when organized in a taxonomical hierarchy. The proposed taxonomical hierarchy, which is formulated based on pathological and visual similarities, may yield better classification metrics by setting inference classes at a higher level than training classes. Mapping from the frame level to the disease level was structured in the form of a graph based on clinical relevance inspired by the recent international consensus developed by domain experts. Furthermore, existing methods for VCE summarization, classification, segmentation, detection, and localization were critically evaluated and compared based on aspects deemed significant by clinicians. Numerous studies pertain to single anomaly detection instead of a pragmatic approach in a clinical setting. The challenges and opportunities associated with VCE analysis were delineated. A focus on maximizing the discriminative power of features corresponding to various subtle lesions and anomalies may help cope with the diverse and mimicking nature of different VCE frames. Large multicenter datasets must be created to cope with data sparsity, bias, and class imbalance. Explainability, reliability, traceability, and transparency are important for an ML-based diagnostics system in a VCE. Existing ethical and legal bindings narrow the scope of possibilities where ML can potentially be leveraged in healthcare. Despite these limitations, ML based video capsule endoscopy will revolutionize clinical practice, aiding clinicians in rapid and accurate diagnosis

Inverse problem theory in shape and action modeling

In this thesis we consider shape and action modeling problems under the perspective of inverse problem theory. Inverse problem theory proposes a mathematical framework for solving model parameter estimation problems. Inverse problems are typically ill-posed, which makes their solution challenging. Regularization theory and Bayesian statistical methods, which are proposed in the context of inverse problem theory, provide suitable methods for dealing with ill-posed problems. Regarding the application of inverse problem theory in shape and action modeling, we first discuss the problem of saliency prediction, considering a model proposed by the coherence theory of attention. According to coherence theory, salience regions emerge via proto-objects which we model using harmonic functions (thin-membranes). We also discuss the modeling of the 3D scene, as it is fundamental for extracting suitable scene features, which guide the generation of proto-objects. The next application we consider is the problem of image fusion. In this context, we propose a variational image fusion framework, based on confidence driven total variation regularization, and we consider its application to the problem of depth image fusion, which is an important step in the dense 3D scene reconstruction pipeline. The third problem we encounter regards action modeling, and in particular the recognition of human actions based on 3D data. Here, we employ a Bayesian nonparametric model to capture the idiosyncratic motions of the different body parts. Recognition is achieved by comparing the motion behaviors of the subject to a dictionary of behaviors for each action, learned by examples collected from other subjects. Next, we consider the 3D modeling of articulated objects from images taken from the web, with application to the 3D modeling of animals. By decomposing the full object in rigid components and by considering different aspects of these components, we model the object up this hierarchy, in order to obtain a 3D model of the entire object. Single view 3D modeling as well as model registration is performed, based on regularization methods. The last problem we consider, is the modeling of 3D specular (non-Lambertian) surfaces from a single image. To solve this challenging problem we propose a Bayesian non-parametric model for estimating the normal field of the surface from its appearance, by identifying the material of the surface. After computing an initial model of the surface, we apply regularization of its normal field considering also a photo-consistency constraint, in order to estimate the final shape of the surface. Finally, we conclude this thesis by summarizing the most significant results and by suggesting future directions regarding the application of inverse problem theory to challenging computer vision problems, as the ones encountered in this work

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Image-set, Temporal and Spatiotemporal Representations of Videos for Recognizing, Localizing and Quantifying Actions

This dissertation addresses the problem of learning video representations, which is defined here as transforming the video so that its essential structure is made more visible or accessible for action recognition and quantification. In the literature, a video can be represented by a set of images, by modeling motion or temporal dynamics, and by a 3D graph with pixels as nodes. This dissertation contributes in proposing a set of models to localize, track, segment, recognize and assess actions such as (1) image-set models via aggregating subset features given by regularizing normalized CNNs, (2) image-set models via inter-frame principal recovery and sparsely coding residual actions, (3) temporally local models with spatially global motion estimated by robust feature matching and local motion estimated by action detection with motion model added, (4) spatiotemporal models 3D graph and 3D CNN to model time as a space dimension, (5) supervised hashing by jointly learning embedding and quantization, respectively. State-of-the-art performances are achieved for tasks such as quantifying facial pain and human diving. Primary conclusions of this dissertation are categorized as follows: (i) Image set can capture facial actions that are about collective representation; (ii) Sparse and low-rank representations can have the expression, identity and pose cues untangled and can be learned via an image-set model and also a linear model; (iii) Norm is related with recognizability; similarity metrics and loss functions matter; (v) Combining the MIL based boosting tracker with the Particle Filter motion model induces a good trade-off between the appearance similarity and motion consistence; (iv) Segmenting object locally makes it amenable to assign shape priors; it is feasible to learn knowledge such as shape priors online from Web data with weak supervision; (v) It works locally in both space and time to represent videos as 3D graphs; 3D CNNs work effectively when inputted with temporally meaningful clips; (vi) the rich labeled images or videos help to learn better hash functions after learning binary embedded codes than the random projections. In addition, models proposed for videos can be adapted to other sequential images such as volumetric medical images which are not included in this dissertation