generally solve these two problems separately or iteratively. In this work, we propose a unified frame-work for simultaneous human parsing and pose estima-tion based on semantic parts. By utilizing Parselets and Mixture of Joint-Group Templates as the representations for these semantic parts, we seamlessly

. We propose an automatic algorithm that starts with an initial template model and then jointly optimizes for part segmentation, point-to-point surface correspondence, and a compact deformation model to best explain the input model collection. As output, the algorithm produces a set of probabilistic

Both the segmentation and detection tasks are formulated as binary classification problem. By maximizing the joint likelihood, part detection responses are grouped, merged, and assigned to multiple object hypotheses. The proposed approach is demonstrated with the class of pedestrians. Our approach

in edge maps. The use of symmetry matching as a joint correlation measure between pairs of edge elements further constrains the comparison of edge maps. In addition, a natural organization of groups of symmetry into a hierarchy leads to a graph-based representation of relational structure of components

), we use a spatiotemporal AND-OR graph to drive our joint inference, using Markov Chain Monte Carlo and dynamic programming. We also introduce a new formalism of spa-tiotemporal templates characterizing latent sub-events. For evaluation, we have collected and released a new aerial videos dataset using

This paper presents a method for learning 3D object tem-plates from view labeled object images. The 3D template is defined in a joint appearance and geometry space, and is composed of deformable planar part templates, which are placed at different positions and orientations. Appearance of each part

obtained and analyzed 17,311 assessments for 28 joints in 1,314 patients with RA from 2005 to 2011 from electronic clinical chart templates stored in the KURAMA (Kyoto University Rheumatoid Arthritis Management Alliance) database. Affected rates for swelling and tenderness were assessed for each of the 28

the background, we propose a novel online tracking algorithm with two stage sparse optimization to jointly minimize the target reconstruction error and maximize the discriminative power. As the target template and discriminative features usually have temporal and spatial relationship, dynamic group sparsity (DGS

C) family. It describes how these concepts are developed and managed, prescribes specific concept templates, introduces the Joint Concept Steering Group (JCSG), and describes joint experimentation as it relates to assessment of the JOpsC family. Upon approval by the Chairman of the Joint Chiefs of Staff

of thermodynamic variation. Our new approach relaxes assumptions of the target's underlying thermodynamic state, expanding thermodynamic state as a scalar field. Estimation within the deformable template setting poses geometric and thermodynamic variation as a joint inference. MMSE pose estimators