International Journal of Image and Graphics

This paper introduces a graphical user interface approach to facilitate an efficient and timely generation of statistic data from input videos. By means of a carefully-designed graphical user interface, users can interactively add in various kinds of markers, known as the statistic inducers, on the screen of an input video to specify the areas of interest corresponding to the locations of relevant events. These inducers are in the form of two-dimensional points, lines, polygons, and grids, and can be put on the video screen with great ease. Using these inducers, we not only can efficiently customize the system for a given statistic generation task; in addition, we can also precisely constrain the time-consuming space-time video analysis process (as well as any additional analysis process like optical flow computation or object recognition) on the user-specified areas. To demonstrate the efficacy of the proposed approach, we developed a prototypic system and experimented it in two different statistic generation cases: dormitory light switching and road traffic. In both cases, we just need a few minutes of UI customization time to set up the inducers; once this is done, timely statistics can be automatically generated subsequently.

Many current watermarking algorithms insert data in the spatial or transform domains like the discrete cosine, the discrete Fourier, and the discrete wavelet transforms. In this paper, we present a data-hiding algorithm that exploits the singular value decomposition (SVD) representation of the data. We compute the SVD of the host image and the watermark and embed the watermark in the singular vectors of the host image. The proposed method leads to an imperceptible scheme for digital images, both in grey scale and color and is quite robust against attacks like noise and JPEG compression.

In this work, a novel efficient algorithm for visual object tracking in complex conditions is proposed. The main component of this work includes two parts: Bayesian decision based confident region extraction, and mean shift iteration based tracking. A unique characteristic of the proposed algorithm is that instead of tracking the entire object, the method automatically extracts the confident region of the object through fusing multiple cues in the Bayesian framework. Those cues contain object's color feature, motion character, and dynamic surrounding color information. We tested the performance of the algorithm with video sequences under difficult conditions (complex and dynamic background, fast camera motion, object maneuvering, rotations and partial occlusion) and achieved satisfied results.

A markerless human gait analysis system using uncalibrated monocular video is developed. The background model is trained for extracting the subject silhouette, whether in static scene or dynamic scene, in each video frame. Generic 3D human model is manually fit to the subject silhouette in the first video frame. We propose the silhouette chamfer, which contains the chamfer distance of silhouette and region information, as one matching feature. This, combined dynamically with the model gradient, is used to search for the best fit between subject silhouette and 3D model. Finally, we use the discrete Kalman filter to predict and correct the pose of the walking subject in each video frame. We propose a quantitative measure that can be used to identify tracking faults automatically. Errors in the joint angle trajectories can then be corrected and the walking cycle is interpreted. Experiments have been carried out on video captured in static indoor as well as outdoor scenes.

This paper presents a vision based technique to identify the unspoken phones using a small camera that is located on the headset of the speaker. The system is based on temporal integration of the video data to generate motion history image (MHI). The paper proposes the use of global features to classify the MHI and compares the use of image moments with Discrete Cosine Transform (DCT). A comparison between Zernike moments (ZM) with DCT indicates that while the accuracy of classification for both techniques is very comparable (96% for ZM and 94% for DCT) when there is no relative motion between the camera and the mouth, ZM is resilient to rotation of the camera and continues to gives good results despite rotation but DCT is sensitive to rotation.

Based on the accuracy of the system and its resilience to movement artefacts such as rotation, the authors propose the use of such a system for human computer interface. Such a system could be invaluable when it is important to communicate without making a sound, such as giving passwords when in an open office or in public spaces.

In this paper, we propose a watermarking scheme for non-uniform B-spline (NUBS) surface. Firstly, we first do sampling on a NUBS surface and get the sample points, then the watermark is embedded into the DCT coefficients of the sample points and the watermarked sample points are transformed back, finally the watermarked surface is reconstructed from watermarked sample points using global interpolation. A sign correlation detector is used to test for the presence of the watermark, and the original surface is not required at this stage. Experimental results show that our algorithm can preserve the shape of the original surface within a specified error, and that it is robust against attacks including knot insertion, order elevation, addition of white noise, rotation, scaling, translation and further watermarking.

In recent years, analysis of human motion has become an increasingly relevant research topic with applications as diverse as animation, virtual reality, security, and advanced human-machine interfaces. In particular, motion capture systems are well known nowadays since they are used in the movie industry. These systems require expensive multi-camera setups or markers to be worn by the user. This paper describes an attempt to provide a markerless low cost and real-time solution for home users. We propose a novel approach for robust detection and tracking of the user's body joints that exploits different algorithms as different sources of information and fuses their estimates with particle filters. This system may be employed for real-time animation of VRML or X3D avatars using an off-the-shelf digital camera and a standard PC.

This paper presents a graph-based ordering scheme of color vectors. A complete graph is defined over a filter window and its structure is analyzed to construct an ordering of color vectors. This graph-based ordering is constructed by finding a Hamiltonian path across the color vectors of a filter window by a two-step algorithm. The first step extracts, by decimating a minimum spanning tree, the extreme values of the color set. These extreme values are considered as the infimum and the supremum of the set of color vectors. The second step builds an ordering by constructing a Hamiltonian path among the vectors of color vectors, starting from the infimum and ending at the supremum. The properties of the proposed graph-based ordering of vectors are detailed. Several experiments are conducted to assess its filtering abilities for morphological and median filtering.