This article deals with the Bayesian inference of unknown parameters of the progressively censored Weibull distribution. It is well known that for a Weibull distribution, while computing the Bayes estimates, the continuous conjugate joint prior distribution of the shape and scale parameters does not exist. In this article it is assumed that the shape parameter has a log-concave prior density fu...

Underwater target detection plays a vital role in various application scenarios, ranging from scientific research to military and industrial operations. In this paper, method via the Cayley–Klein measure prior information of shape is proposed for issue hyperspectral underwater identification. Firstly, by analyzing data features targets backgrounds, background suppression algorithm based on deve...

We present a method to incorporate nonlinear shape prior constraints into segmenting different anatomical structures in medical images. Kernel space density estimation (KSDE) is used to derive the nonlinear shape statistics and enable building a single model for a class of objects with nonlinearly varying shapes. The object contour is coerced by image-based energy into the correct shape sub-dis...

Reconstructing 3D motion data is highly under-constrained due to several common sources of data loss during measurement, such as projection, occlusion, or miscorrespondence. We present a statistical model of 3D motion data, based on the Kronecker structure of the spatiotemporal covariance of natural motion, as a prior on 3D motion. This prior is expressed as a matrix normal distribution, compos...

Prior knowledge about shape may be quite important for image segmentation. In particular, a number of different methods have been proposed to compute the statistics on a set of training shapes, which are then used for a given image segmentation task to provide the shape prior. In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally li...

We introduce a category-independent shape prior for object segmentation. Existing shape priors assume class-specific knowledge, and thus are restricted to cases where the object class is known in advance. The main insight of our approach is that shapes are often shared between objects of different categories. To exploit this “shape sharing” phenomenon, we develop a non-parametric prior that tra...

We design an effective shape prior embedded human silhouettes extraction algorithm. Human silhouette extraction is found challenging because of articulated structures, pose variations, and background clutters. Many segmentation algorithms, including the Min-Cut algorithm, meet difficulties in human silhouette extraction. We aim at improving the performance of the Min-Cut algorithm by embedding ...

People readily perceive patterns of shading as 3‐D shapes. Owing to the generalised bas‐ relief ambiguity when extracting shape from shading, people must simultaneously estimate the shape of the surface and the nature of the light source. In many cases cues in the image will be insufficient to resolve all of the ambiguities present, and in such cases the human visual system may employ one of a ...