While image editing tends to be popularized, it remains a time-consuming task requiring a minimal expertise. This proposal presents an innovative tool for performing guided image editing. Oriented by a quality metric based on color harmony theory, the user picks up the least harmonious color areas and retouches them based on our recommended color palette. This set of proposed colors ensure the maximization of the global color harmony for the considered picture. In few clicks, anyone is able to retouch incongruous colors while respecting his taste or intent.

This paper presents a novel technique for video shot detection, including hard cuts and gradual transitions. The method builds on various similarity criteria, namely histogram difference, motion estimation and distribution of intensity difference. While these metrics are well known, the novelty of the paper resides in the probabilistic framework to assess the similarity between two images. The a contrario framework, introduced in [1, 2] enables to control explicitly the number of false alarms given a background noise model. Experiments have been conducted on the Trecvid 2007 database: for all transitions (cuts and gradual), a recall of 0.95 and a precision of 0.96 was obtained.

Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera view-points, lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, data-driven approach to human analysis. In this work we aim to explore this approach using the large amount of images spanning a subspace of human appearance. We model this subspace by connecting images into a graph and propagating information through such a graph using a discriminatively-trained graphical model. We particularly address the problems of human pose estimation and action recognition and demonstrate how image graphs help solving these problems jointly. We report results on still images with human actions from the KTH dataset.

We present a probabilistic multi-cue tracking approach constructed by employing a novel randomized template tracker and a constant color model based particle filter. Our approach is based on deriving simple binary confidence measures for each tracker which aid priority based switching between the two fundamental cues for state estimation. Thereby the state of the object is estimated from one of the two distributions associated to the cues at each tracking step. This switching also brings about interaction between the cues at irregular intervals in the form of cross sampling. Within this scheme, we tackle the important aspect of dynamic target model adaptation under randomized template tracking which, by construction, possesses the ability to adapt to changing object appearances. Further, to track the object through occlusions we interrupt sequential resampling and achieve relock using the color cue. In order to evaluate the efficacy of this scheme, we put it to test against several state of art trackers using the VIVID online evaluation program and make quantitative comparisons.

Algorithms for probabilistic visual tracking hypothesize a distribution of the target state (location, scale, etc.) at every tracking step with an associated information content or equivalently, an uncertainty. One measure of this uncertainty is the differential entropy. In this paper, we present a unified way to approximate the differential entropy of tracking distributions, which then makes it suitable, among other factors, for a qualitative assessment of both deterministic and sequential Monte Carlo simulation based tracking algorithms. We then illustrate the usefulness of this assessment measure via tracking an object by choosing a set of randomly picked features on it, each individually tracked, removed according to an uncertainty analysis and replaced randomly, without any aid of a feature selection algorithm as in current use.

Video frame alignment with the original source signal is required in a variety of applications, for instance non-blind watermark detection. Robustness to temporal cropping, scene removal, frame-rate changes and other non-linear temporal distortions are required. This paper presents an algorithm for locating a video segment within a long video document with frame-accurate alignment. The video sequences are first summarized into one-dimensional "temporal profile" signals. Then frame-to-frame correspondence is obtained by a dynamic programming approach. An automatic assessment of the matching result is proposed. Extensive test results achieved 77% success rate.

A fingerprinting design extracts discriminating features, called fingerprints. The extracted features are unique and specific to each image/video. The visual hash is usually a global fingerprinting technique with crypto-system constraints. In this paper, we propose an innovative video content identification process which combines a visual hash function and a local fingerprinting. Thanks to a visual hash function, we observe the video content variation and we detect key frames. A local image fingerprint technique characterizes the detected key frames. The set of local fingerprints for the whole video summarizes the video or fragments of the video. The video fingerprinting algorithm identifies an unknown video or a fragment of video within a video fingerprint database. It compares the local fingerprints of the candidate video with all local fingerprints of a database even if strong distortions are applied to an original content.

This paper focuses on the use of hidden Markov models (HMMs) for structure analysis of sport videos. The video structure parsing relies on the analysis of the temporal interleaving of video shots, with respect to a priori information about video content and editing rules. The basic temporal unit is the video shot and visual features are used to characterize its type of view. Our approach is validated in the particular domain of tennis videos. As a result, typical tennis scenes are identified. In addition, each shot is assigned to a level in the hierarchy described in terms of point, game and set.

This paper focuses on the use of hidden Markov models (HMMs) for structure analysis of videos, and demonstrates how they can be efficiently applied to merge audio and visual cues. Our approach is validated in the particular domain of tennis videos. The basic temporal unit is the video shot. Visual features describe the audio events within a video shot. The video structure parsing relies on the analysis of the temporal interleaving of video shots, with respect to prior information about tennis content and editing rules. As a result, typical tennis scenes are identified. In addition, each shot is assigned to a level in the hierarchy described in terms of point, game and set.

We present a method for fully automatic three-dimensional (3D) reconstruction from a pair of weakly calibrated images in order to deal with the modeling of complex rigid scenes. A two-dimensional (2D) triangular mesh model of the scene is calculated using a two-step algorithm mixing sparse matching and dense motion estimation approaches. The 2D mesh is iteratively refined to fit any arbitrary 3D surface. At convergence, each triangular patch corresponds to the projection of a 3D plane. The proposed algorithm relies first on a dense disparity field. The dense field estimation modelized within a robust framework is constrained by the epipolar geometry. The resulting field is then segmented according to homographic models using iterative Delaunay triangulation. In association with a weak calibration and camera motion estimation algorithm, this 2D planar model is used to obtain a VRML-compatible 3D model of the scene.

In the present paper we propose a new algorithm for planar facets segmentation of sequences of uncalibrated images in order to recover 3D models of complex scenes. This is performed using a two-step algorithm. First a robust and regularized dense disparity field is computed under the epipolar geometry constraint. The resulting field is segmented according to homographic models using an iterative Delaunay triangulation.