Segmenting hair regions from human images facilitates many tasks like hair synthesis and hair style trends forecast. However, hair segmentation is quite challenging due to hair/background confusion and large hair pattern diversity. To address these problems to some extent, this paper proposes a novel coarse-to-fine hair segmentation method. In our approach, firstly, the recently proposed “Active Segmentation with Fixation” (ASF) is used to coarsely define an enclosed candidate region with high-recall (but possibly low-precision) of hair pixels and exclude considerable part of the backgrounds which are easily confused with hair. Then Graph Cuts (GC) method is applied to the candidate regions to remove additional false positives by incorporating hair-specific information. Specifically, Bayesian method is employed to select some reliable hair and background regions (seeds) among the ones over-segmented by Mean Shift. SVM classifier is then learnt online from these seeds and explored to predict hair/background likelihood probability, which is subsequently fed into GC algorithm. The novelty of the proposed approach lies in three folds: 1) an elaborate design of hair segmentation framework, which utilizes ASF to reduce the candidate hair regions and adopts GC to achieve more accurate hair region contours; 2) the region-based strategy for seed selection; 3) the exploration of the discriminative method, SVM, to predict the probability of each pixel belonging to hair and background regions. Extensive experimental results demonstrate the approach outperforms recently proposed methods.

In this paper, we propose a multi-camera video stitching approach, which conducts borderline adjusting based on foreground information. In real time applications of video stitching, one problem is to deal with dynamic scenes, where foreground objects often cause broken objects like artifacts in panoramic video. To address this problem, we propose a foreground based borderline adjusting method to achieve smooth video stitching. In this method, foreground objects are extracted from different viewpoints, and the stitching borderlines are adjusted according to the foreground content in dynamic scenes. Guided by the adjusted borderlines, foreground objects are smoothly synthesized into panoramic video. Experiment results show that this approach obtains more than 81% correct rate for dynamic scenes, compared with 25%~50% correct rate on the condition of not utilizing foreground information, and achieves processing speed of 10~13 frames per second.

In this paper, a novel two-tier Bayesian based method is proposed for hair segmentation. In the first tier, we construct a Bayesian model by integrating hair occurrence prior probabilities (HOPP) with a generic hair color model (GHCM) to obtain some reliable hair seed pixels. These initial seeds are further propagated to their neighborhood pixels by utilizing segmentation results of mean shift, to obtain more seeds. In the second tier, all of these selected seeds are used to train a hair-specific Gaussian model, which are combined with HOPP to build the second Bayesian model for pixel classification. Mean shift results are further utilized to remove holes and spread hair regions. The experimental results illustrate the effectiveness of our approach.

For years, researchers in face recognition area have been representing and recognizing faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based face representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided. In this approach, a face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET face database show the validity of the proposed approach especially for partially occluded face images, and more impressively, we have achieved the best result on FERET face database.

Feature extraction for object representation plays an important role in automatic object detection system. As the spatial histograms consist of marginal distribution of image over local patches, object texture and shape are simultaneously preserved by the spatial histogram representation. In this paper, we propose methods of learning informative features for spatial histogram-based object detection. We employ Fisher criterion to measure the discriminability of each spatial histogram feature and calculate features correlation using mutual information. In order to construct compact feature sets for efficient classification, we propose informative selection algorithm to select uncorrelated and discriminative spatial histogram features. The proposed approaches are tested on two different kinds of objects: car and video text. The experimental results show that the proposed approaches are efficient in object detection.

This paper presents a tracking system based on the technique of gravity-center template to track faces and localize facial organs such as pupil, eyes, nose and mouth.

In the paper, a methodology for individual face synthesis using given orthogonal photos is proposed. An integrated speech-driven facial animation system is presented. Firstly, in order to capture a given subject's personal facial configuration, a novel coarse-to-fine strategy based on facial texture and deformable template is proposed to localize some facial feature points in the image of frontal view. The corresponding feature points in the profile are extracted by using the polygonal approximation. Secondly, all these feature points are aligned to fit the generic 3D face model to a specialized one to reflect the given person's facial configuration. Then a multi-direction texture-mapping technique is presented to synthesize a lifelike personal face. Finally, muscle-based expression and lip-motion models are built up. All the above technologies are integrated into a speech-driven face animation system. We are aiming at a MPEG-4 compatible video-driven face animation system.