3,982 research outputs found
Probabilistic Search for Object Segmentation and Recognition
The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to be evaluated for their match to the data. The truncated probability is partly determined by prior knowledge of the objects and partly learned from data. Some experiments on sequence quality and object segmentation and recognition from stereo data are presented. The article recovers classic concepts from object recognition (grouping, geometric hashing, alignment) from the probabilistic perspective and adds insight into the optimal ordering of object hypotheses for evaluation. Moreover, it introduces point-relation densities, a key component of the truncated probability, as statistical models of local surface shape
Probabilistic Search for Object Segmentation and Recognition
The problem of searching for a model-based scene interpretation is analyzed
within a probabilistic framework. Object models are formulated as generative
models for range data of the scene. A new statistical criterion, the truncated
object probability, is introduced to infer an optimal sequence of object
hypotheses to be evaluated for their match to the data. The truncated
probability is partly determined by prior knowledge of the objects and partly
learned from data. Some experiments on sequence quality and object segmentation
and recognition from stereo data are presented. The article recovers classic
concepts from object recognition (grouping, geometric hashing, alignment) from
the probabilistic perspective and adds insight into the optimal ordering of
object hypotheses for evaluation. Moreover, it introduces point-relation
densities, a key component of the truncated probability, as statistical models
of local surface shape.Comment: 18 pages, 5 figure
Robust 3-Dimensional Object Recognition using Stereo Vision and Geometric Hashing
We propose a technique that combines geometric hashing with stereo vision. The idea is to use the robustness of geometric hashing to spurious data to overcome the correspondence problem, while the stereo vision setup enables direct model matching using the 3-D object models. Furthermore, because the matching technique relies on the relative positions of local features, we should be able to perform robust recognition even with partially occluded objects. We tested this approach with simple geometric objects using a corner point detector. We successfully recognized objects even in scenes where the objects were partially occluded by other objects. For complicated scenes, however, the limited set of model features and required amount of computing time, sometimes became a proble
Cumulative object categorization in clutter
In this paper we present an approach based on scene- or part-graphs for geometrically categorizing touching and
occluded objects. We use additive RGBD feature descriptors and hashing of graph configuration parameters for describing the spatial arrangement of constituent parts. The presented experiments quantify that this method outperforms our earlier part-voting and sliding window classification. We evaluated our approach on cluttered scenes, and by using a 3D dataset containing over 15000 Kinect scans of over 100 objects which were grouped into general geometric categories. Additionally, color, geometric, and combined features were compared for categorization tasks
Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval
Free-hand sketch-based image retrieval (SBIR) is a specific cross-view
retrieval task, in which queries are abstract and ambiguous sketches while the
retrieval database is formed with natural images. Work in this area mainly
focuses on extracting representative and shared features for sketches and
natural images. However, these can neither cope well with the geometric
distortion between sketches and images nor be feasible for large-scale SBIR due
to the heavy continuous-valued distance computation. In this paper, we speed up
SBIR by introducing a novel binary coding method, named \textbf{Deep Sketch
Hashing} (DSH), where a semi-heterogeneous deep architecture is proposed and
incorporated into an end-to-end binary coding framework. Specifically, three
convolutional neural networks are utilized to encode free-hand sketches,
natural images and, especially, the auxiliary sketch-tokens which are adopted
as bridges to mitigate the sketch-image geometric distortion. The learned DSH
codes can effectively capture the cross-view similarities as well as the
intrinsic semantic correlations between different categories. To the best of
our knowledge, DSH is the first hashing work specifically designed for
category-level SBIR with an end-to-end deep architecture. The proposed DSH is
comprehensively evaluated on two large-scale datasets of TU-Berlin Extension
and Sketchy, and the experiments consistently show DSH's superior SBIR
accuracies over several state-of-the-art methods, while achieving significantly
reduced retrieval time and memory footprint.Comment: This paper will appear as a spotlight paper in CVPR201
- …