Using Object Models as Domain Knowledge in Perceptual Organization: An Approach for Object Category Identification in Video Sequences

Author(s):  
Gaurav Harit ◽  
Rajesh Bharatia ◽  
Santanu Chaudhury
2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Lizhen Wu ◽  
Yifeng Niu ◽  
Lincheng Shen

Even though several promising approaches have been proposed in the literature, generic category-level object detection is still challenging due to high intraclass variability and ambiguity in the appearance among different object instances. From the view of constructing object models, the balance between flexibility and discrimination must be taken into consideration. Motivated by these demands, we propose a novel contextual hierarchical part-driven conditional random field (CRF) model, which is based on not only individual object part appearance but also model contextual interactions of the parts simultaneously. By using a latent two-layer hierarchical formulation of labels and a weighted neighborhood structure, the model can effectively encode the dependencies among object parts. Meanwhile, beta-stable local features are introduced as observed data to ensure the discriminative and robustness of part description. The object category detection problem can be solved in a probabilistic framework using a supervised learning method based on maximum a posteriori (MAP) estimation. The benefits of the proposed model are demonstrated on the standard dataset and satellite images.


2019 ◽  
Vol 38 (9) ◽  
pp. 1013-1019 ◽  
Author(s):  
Roberto Martín-Martín ◽  
Clemens Eppner ◽  
Oliver Brock

We present a dataset with models of 14 articulated objects commonly found in human environments and with RGB-D video sequences and wrenches recorded of human interactions with them. The 358 interaction sequences total 67 minutes of human manipulation under varying experimental conditions (type of interaction, lighting, perspective, and background). Each interaction with an object is annotated with the ground-truth poses of its rigid parts and the kinematic state obtained by a motion-capture system. For a subset of 78 sequences (25 minutes), we also measured the interaction wrenches. The object models contain textured three-dimensional triangle meshes of each link and their motion constraints. We provide Python scripts to download and visualize the data. The data are available at https://turbo.github.io/articulated-objects/ and hosted at https://zenodo.org/record/1036660/ .


2001 ◽  
Author(s):  
Joseph S. Lappin ◽  
Duje Tadin ◽  
Emily Grossman

Author(s):  
Gregory K. W. K. Chung ◽  
Eva L. Baker ◽  
David G. Brill ◽  
Ravi Sinha ◽  
Farzad Saadat ◽  
...  

2007 ◽  
Author(s):  
Maritza Figueroa ◽  
Jessie Morrow ◽  
Stephanie Levy ◽  
Colleen Shearer ◽  
Charles J. Golden

1957 ◽  
Vol 55 (2) ◽  
pp. 273-273
Author(s):  
Clinton De Soto ◽  
H. Liebowitz

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.


Sign in / Sign up

Export Citation Format

Share Document