Defocus blur usually causes performance degradation in establishing the visual correspondence between stereo images. We propose a blur-aware disparity estimation method that is robust to the mismatch of focus in stereo images. The relative blur resulting from the mismatch of focus between stereo images is approximated as the difference of the square diameters of the blur kernels. Based on the defocus and stereo model, we propose the relative blur versus disparity (RBD) model that characterizes the relative blur as a second-order polynomial function of disparity. Our method alternates between RBD model update and disparity update in each iteration. The RBD model in return refines the disparity estimation by updating the matching cost and aggregation weight to compensate the mismatch of focus. Experiments using both synthesized and real datasets demonstrate the effectiveness of our proposed algorithm.

Alpha matting for single image is an inherently under-constrained problem and thus normally requires user input. In this paper, an automatic, bottom-up matting algorithm using defocus cue is proposed. Different from most defocus matting algorithms, we first extract matting components by applying unsupervised spectral matting algorithm on single image. The defocus cue is then used for classifying matting components to form a complete foreground matte. This approach gives more robust result because focus estimation is used in component level rather than pixel level.

In this paper, we propose a novel approach to compute rotation-invariant features from histograms of local noninvariant patterns. We apply this approach to both static and dynamic local binary pattern (LBP) descriptors. For static-texture description, we present LBP histogram Fourier (LBP-HF) features, and for dynamic-texture recognition, we present two rotation-invariant descriptors computed from the LBPs from three orthogonal planes (LBP-TOP) features in the spatiotemporal domain. LBP-HF is a novel rotation-invariant image descriptor computed from discrete Fourier transforms of LBP histograms. The approach can be also generalized to embed any uniform features into this framework, and combining the supplementary information, e.g., sign and magnitude components of the LBP, together can improve the description ability. Moreover, two variants of rotation-invariant descriptors are proposed to the LBP-TOP, which is an effective descriptor for dynamic-texture recognition, as shown by its recent success in different application problems, but it is not rotation invariant. In the experiments, it is shown that the LBP-HF and its extensions outperform noninvariant and earlier versions of the rotation-invariant LBP in the rotation-invariant texture classification. In experiments on two dynamic-texture databases with rotations or view variations, the proposed video features can effectively deal with rotation variations of dynamic textures (DTs). They also are robust with respect to changes in viewpoint, outperforming recent methods proposed for view-invariant recognition of DTs.

Face recognition from low-resolution images is a common yet challenging case in real applications. Since the high-frequency information is lost in low-resolution images, it is necessary to explore robust information in the low frequency domain. In this paper, we propose an effective local frequency descriptor (LFD) for low resolution face recognition, by building upon the ideas behind local phase quantization (LPQ) and exploring both blur-invariant magnitude and phase information in the low frequency domain. The proposed descriptor is more descriptive than LPQ with more comprehensive information. In addition, a statistical uniform pattern definition method is introduced to improve the efficiency of the proposed descriptor. Experimental results on FERET and a real video database show that LFD is effective and robust for low-resolution face recognition.

This course is designed to demonstrate the use of the FCam, an Application Programming Interface (API) which allows for easy and precise control of camera systems. It lets programmers implement advanced computational photography applications. Such applications even allow the modification of basic camera routines such as metering, which are hidden inside black boxes in most camera systems. FCam is also an excellent tool for teaching and research on interactive mobile computational photography. The API has been open-sourced, and is currently implemented for Nokia N900 and an experimental Frankencamera (the F2). The Frankencamera architecture for computational cameras was presented at SIGGRAPH 2010. By end-2011, the FCam will hopefully be available on Nokia™ Linux phones, NVIDIA™ Tegra 3 development boards, and F3, the next version of the Stanford experimental camera. The F3 will be distributed to research universities within the US along with courseware in a National Science Foundation (NSF)-sponsored project. NVIDIA Tegras and Nokia™ Linux phones are available globally.

In this paper, recognition of blurred faces using the recently introduced Local Phase Quantization (LPQ) operator is proposed. LPQ is based on quantizing the Fourier transform phase in local neighborhoods. The phase can be shown to be a blur invariant property under certain commonly fulfilled conditions. In face image analysis, histograms of LPQ labels computed within local regions are used as a face descriptor similarly to the widely used Local Binary Pattern (LBP) methodology for face image description. The experimental results on CMU PIE and FRGC 1.0.4 datasets show that the LPQ descriptor is highly tolerant to blur but still very descriptive outperforming LBP both with blurred and sharp images.

In this paper, a Bayesian LBP operator is proposed. This operator is formulated in a novel Filtering, Labeling and Statistic (FLS) framework for texture descriptors. In the framework, the local labeling procedure, which is a part of many popular descriptors such as LBP, SIFT and VZ, can be modeled as a probability and optimization process. This enables the use of more reliable prior and likelihood information and reduces the sensitivity to noise. The BLBP operator pursues a label image, when given the filtered vector image, by maximizing the joint probability of two images under the criterion of MAP. The proposed approach is evaluated on texture retrieval schemes using entire Brodatz database. The result reveals BLBP operator’s efficient performance and FLS framework’s capability to in-depth analysis of the texture descriptors on a common background.

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed.