Christophe De Vleeschouwer

Psychovisual approach to digital picture watermarking

In this paper, we wish to present a process enabling us to mark digital pictures with invisible and undetectable secret information. This so-called watermarking process is intended to be the basis of a complete copyright protection system. It consists of constructing a band-limited image from binary sequences with good correlation properties and in modulating some randomly selected carriers. The security relies on the secrecy of these carrier frequencies, which are deduced from a unique secret key. Then the amplitude of the modulated images is modified according to a masking criterion based on a model of the Human Visual System. The adding of the modulated images to the original is supposed to be invisible. The resulting image fully identifies the copyright owner since he is the only one able to detect and prove the presence of the embedded watermark thanks to his secret key. This paper also contains an analysis of the robustness of the watermark against compression and image processing

Low-cost perceptive digital picture watermarking method

This paper presents an additive watermarking technique for grey scale pictures, which can be extended to video sequences. It consists of embedding secretly a copyright information (a binary scale) in the picture without degrading its quality. Those bits are encoded through the phase of Maximal Length Sequences (MLS). MLS are sequences having good correlation properties, which means that the result of the autocorrelation is far greater than crosscorrelations, i.e. correlations made with shifted version of this sequence. This embedding is performed line by line going from the top to the bottom of the picture as the objective was to implement a low cost and real time embedding method able to work for common video equipments. The very embedding process is underlain by a masking criterion that guarantees the invisibility of the watermark. This perceptive criterion, deduced from physiological and psychophysic studies, has already proved its efficiency in a previously presented paper. It is combined with an edge and texture discrimination to determine the embedding level of the MLS, whose bits are actually spread over 32 by 8 pixel squares. Eventually, some preliminary results are presented, which analyze the efficacy of the decoding as well as the resistance of the watermark towards compression and robustness against malevolent treatments.

Involvement of human ribosomal proteins in nucleolar structure and p53-dependent nucleolar stress

The nucleolus is a potent disease biomarker and a target in cancer therapy. Ribosome biogenesis is initiated in the nucleolus where most ribosomal (r-) proteins assemble onto precursor rRNAs. Here we systematically investigate how depletion of each of the 80 human r-proteins affects nucleolar structure, pre-rRNA processing, mature rRNA accumulation and p53 steady-state level. We developed an image-processing programme for qualitative and quantitative discrimination of normal from altered nucleolar morphology. Remarkably, we find that uL5 (formerly RPL11) and uL18 (RPL5) are the strongest contributors to nucleolar integrity. Together with the 5S rRNA, they form the late-assembling central protuberance on mature 60S subunits, and act as an Hdm2 trap and p53 stabilizer. Other major contributors to p53 homeostasis are also strictly late-assembling large subunit r-proteins essential to nucleolar structure. The identification of the r-proteins that specifically contribute to maintaining nucleolar structure and p53 steady-state level provides insights into fundamental aspects of cell and cancer biology.

Personalized production of basketball videos from multi-sensored data under limited display resolution

Integration of information from multiple cameras is essential in television production or intelligent surveillance systems. We propose an autonomous system for personalized production of basketball videos from multi-sensored data under limited display resolution. The problem consists in selecting the right view to display among the multiple video streams captured by the investigated camera network. A view is defined by the camera index and the parameters of the image cropped within the selected camera. We propose criteria for optimal planning of viewpoint coverage and camera selection. Perceptual comfort is discussed as well as efficient integration of contextual information, which is implemented by smoothing generated viewpoint/camera sequences to alleviate flickering visual artifacts and discontinuous story-telling artifacts. We design and implement the estimation process and verify it by experiments, which shows that our method efficiently reduces those artifacts.

Resource Allocation for Personalized Video Summarization

We propose a hybrid personalized summarization framework that combines adaptive fast-forwarding and content truncation to generate comfortable and compact video summaries. We formulate video summarization as a discrete optimization problem, where the optimal summary is determined by adopting Lagrangian relaxation and convex-hull approximation to solve a resource allocation problem. To trade-off playback speed and perceptual comfort we consider information associated to the still content of the scene, which is essential to evaluate the relevance of a video, and information associated to the scene activity, which is more relevant for visual comfort. We perform clip-level fast-forwarding by selecting the playback speeds from discrete options, which naturally include content truncation as special case with infinite playback speed. We demonstrate the proposed summarization framework in two use cases, namely summarization of broadcasted soccer videos and surveillance videos. Objective and subjective experiments are performed to demonstrate the relevance and efficiency of the proposed method.

Formulating Team-Sport Video Summarization as a Resource Allocation Problem

We propose a flexible framework to summarize team-sport videos that have been originally produced for broadcast purposes. The framework is able to integrate both the knowledge about displayed content (e.g., level of interest, type of view, and so on), and the individual (narrative) preferences of the user. It builds on the partition of the original video sequence into independent segments, and creates local stories by considering multiple ways to render each segment. We discuss how to segment videos automatically based on production principles, and design parametric functions to evaluate the benefit of various local stories from a segment. Summarization by selection of local stories is then regarded as a resource allocation problem, and Lagrangian relaxation is performed to find the optimum. We investigate the efficiency of our framework by summarizing soccer, basketball and volleyball videos in our experiments.

Visual event recognition using decision trees

This paper presents a classifier-based approach to recognize dynamic events in video surveillance sequences. The goal of this work is to propose a flexible event recognition system that can be used without relying on a long-term explicit tracking procedure. It is composed of three stages. The first one aims at defining and building a set of relevant features describing the shape and movements of the foreground objects in the scene. To this aim, we introduce new motion descriptors based on space-time volumes. Second, an unsupervised learning-based method is used to cluster the objects, thereby defining a set of coarse to fine local patterns of features, representing primitive events in the video sequences. Finally, events are modeled as a spatio-temporal organization of patterns based on an ensemble of randomized trees. In particular, we want this classifier to discover the temporal and causal correlations between the most discriminative patterns. Our system is experimented and validated both on simulated and real-life data.

Discriminative Label Propagation for Multi-object Tracking with Sporadic Appearance Features

Given a set of plausible detections, detected at each time instant independently, we investigate how to associate them across time. This is done by propagating labels on a set of graphs that capture how the spatio-temporal and the appearance cues promote the assignment of identical or distinct labels to a pair of nodes. The graph construction is driven by the locally linear embedding (LLE) of either the spatio-temporal or the appearance features associated to the detections. Interestingly, the neighborhood of a node in each appearance graph is defined to include all nodes for which the appearance feature is available (except the ones that coexist at the same time). This allows to connect the nodes that share the same appearance even if they are temporally distant, which gives our framework the uncommon ability to exploit the appearance features that are available only sporadically along the sequence of detections. Once the graphs have been defined, the multi-object tracking is formulated as the problem of finding a label assignment that is consistent with the constraints captured by each of the graphs. This results into a difference of convex program that can be efficiently solved. Experiments are performed on a basketball and several well-known pedestrian datasets in order to validate the effectiveness of the proposed solution.

D-HAZY: A dataset to evaluate quantitatively dehazing algorithms

Dehazing is an image enhancing technique that emerged in the recent years. Despite of its importance there is no dataset to quantitatively evaluate such techniques. In this paper we introduce a dataset that contains 1400+ pairs of images with ground truth reference images and hazy images of the same scene. Since due to the variation of illumination conditions recording such images is not feasible, we built a dataset by synthesizing haze in real images of complex scenes. Our dataset, called D-HAZY, is built on the Middelbury [1] and NYU Depth [2] datasets that provide images of various scenes and their corresponding depth maps. Due to the fact that in a hazy medium the scene radiance is attenuated with the distance, based on the depth information and using the physical model of a hazy medium we are able to create a corresponding hazy scene with high fidelity. Finally, using D-HAZY dataset, we perform a comprehensive quantitative evaluation of several state of the art single-image dehazing techniques.

Coarse-to-fine textures retrieval in the JPEG 2000 compressed domain for fast browsing of large image databases

In many applications, the amount and resolution of digital images have significantly increased over the past few years. For this reason, there is a growing interest for techniques allowing to efficiently browse and seek information inside such huge data spaces. JPEG 2000, the latest compression standard from the JPEG committee, has several interesting features to handle very large images. In this paper, these features are used in a coarse-to-fine approach to retrieve specific information in a JPEG 2000 code-stream while minimizing the computational load required by such processing. Practically, a cascade of classifiers exploits the bit-depth and resolution scalability features intrinsically present in JPEG 2000 to progressively refine the classification process. Comparison with existing techniques is made in a texture-retrieval task and shows the efficiency of such approach.