The psychometric property and validation of a fatalism scale.

Speech Communication, University of Georgia, Athens, GA 30602, USA.
Psychology & Health (Impact Factor: 1.95). 06/2009; 24(5):597-613. DOI: 10.1080/08870440801902535
Source: PubMed

ABSTRACT In this article, we conceptualised fatalism as a set of health beliefs that encompass the dimensions of predetermination, luck and pessimism. A 20-item scale was developed as a measurement instrument. Confirmatory factor analyses were performed to test the dimensionality of the scale. Three external variables (i.e. genetic determinism, perceived benefits of lifestyle change and intention to engage in healthy behaviour) were used as reference variables to test the construct validity of the scale. Data from a web-based national survey (N = 1218) showed that the scale was unidimensional on the second order, and with good reliability (alpha = 0.88). The relationships between the external variables and the first- and second-order factors provided evidence of the scale's external consistency and construct validity.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we present a novel re-texturing approach using intrinsic video. Our approach first indicates the regions of interest by contour-aware layer segmentation. The intrinsic video including reflectance and illumination components within the segmented region is recovered by our weighted energy optimization. We then compute the texture coordinates in key frames and the normals for the re-textured region using the optimization approach we develop. Meanwhile, the texture coordinates in non-key frames are optimized by our energy function. When the target sample texture is specified, the re-textured video is finally created by multiplying the re-textured reflectance component with the original illumination component within the replaced region. As shown in our experimental results, our method can produce high quality video re-texturing results with a variety of sample textures, and also the lighting and shading effects of the original videos are well preserved after re-texturing.
    Information Sciences 10/2014; 281:726-735. DOI:10.1016/j.ins.2014.02.134 · 3.89 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We present a method to decompose a video into its intrinsic components of reflectance and shading, plus a number of related example applications in video editing such as segmentation, stylization, material editing, recolorization and color transfer. Intrinsic decomposition is an ill-posed problem, which becomes even more challenging in the case of video due to the need for temporal coherence and the potentially large memory requirements of a global approach. Additionally, user interaction should be kept to a minimum in order to ensure efficiency. We propose a probabilistic approach, formulating a Bayesian Maximum a Posteriori problem to drive the propagation of clustered reflectance values from the first frame, and defining additional constraints as priors on the reflectance and shading. We explicitly leverage temporal information in the video by building a causal-anticausal, coarse-to-fine iterative scheme, and by relying on optical flow information. We impose no restrictions on the input video, and show examples representing a varied range of difficult cases. Our method is the first one designed explicitly for video; moreover, it naturally ensures temporal consistency, and compares favorably against the state of the art in this regard.
    ACM Transactions on Graphics 07/2014; 33(4):1-11. DOI:10.1145/2601097.2601135 · 3.73 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Achieving convincing visual consistency between virtual objects and a real scene mainly relies on the lighting effects of virtual-real composition scenes. The problem becomes more challenging in lighting virtual objects in a single real image. Recently, scene understanding from a single image has made great progress. The estimated geometry, semantic labels and intrinsic components provide mostly coarse information, and are not accurate enough to re-render the whole scene. However, carefully integrating the estimated coarse information can lead to an estimate of the illumination parameters of the real scene. We present a novel method that uses the coarse information estimated by current scene understanding technology to estimate the parameters of a ray-based illumination model to light virtual objects in a real scene. Our key idea is to estimate the illumination via a sparse set of small 3D surfaces using normal and semantic constraints. The coarse shading image obtained by intrinsic image decomposition is considered as the irradiance of the selected small surfaces. The virtual objects are illuminated by the estimated illumination parameters. Experimental results show that our method can convincingly light virtual objects in a single real image, without any pre-recorded 3D geometry, reflectance, illumination acquisition equipment or imaging information of the image.
    Sciece China. Information Sciences 09/2014; 57(9):1-14. DOI:10.1007/s11432-013-4936-0 · 0.70 Impact Factor

Full-text (2 Sources)

Available from
Jun 2, 2014