Ground segmentation is an important preprocessing task for autonomous vehicles (AVs) with 3D LiDARs. However, the existing ground methods are very difficult to balance accuracy and computational complexity. This paper proposes a fast point cloud approach based on coarse-to-fine Markov random field (MRF) method. The method uses coarse result of improved local feature extraction algorithm instead...

Multichannel Gabor filters (MGFs) and Markov random fields (MRFs) are two common methods for texture classification. However, the two above methods make the implicit assumption that textures are acquired in the same viewpoint, which is unsuitable for rotation-invariant texture classification. In this paper, rotation-invariant (RI) texture features are developed based on MGF and MRF. A novel alg...

This paper presents a novel part-based geometry model for 3D object classes based on latent Dirichlet allocation (LDA). With all object instances of the same category aligned to a canonical pose, the bounding box is discretized to form a 3D space dictionary for LDA. To enhance the spatial coherence of each part during model learning, we extend LDA by strategically constructing a Markov random f...

A classification scheme incorporating spectral, textural, and contextual information is detailed in this paper. The gray level co-occurrence matrix (GLCM) is calculated to generate texture features. Those features are then subjected to a selection process for joining with spectral data in order to evaluate their discrimination capability in classification performance. The classification result ...

Dynamically changing urban areas require periodic automatic monitoring, but urban areas include various objects and different objects show diverse appearances. This makes it difficult to effectively detect urban areas. A region-growing method using the Markov random field (MRF) model is proposed for urban detection. It consists of three modules. First, it provides an automatic urban seed object...

In this paper we address the multispectral image contextual classification problem following a Maximum a Posteriori (MAP) approach. The classification model is based on a Bayesian paradigm, with the definition of a Gaussian Markov Random Field model (GMRF) for the observed data and a Potts model for the a priori knowledge. The MAP estimator is approximated by the Game Strategy Approach (GSA) al...

Integration is a crucial step in the reconstruction of complete 3D surface model from multiple scans. Ever-present registration errors and scanning noise make integration a nontrivial problem. In this paper, we propose a novel method for multi-view scan integration where we solve it as a labeling problem. Unlike previous methods, which have been based on various merging schemes, our labeling-ba...

This paper addresses the problem of obtaining an accurate 3D reconstruction from multiple views. Taking inspiration from the recent successes of using strong prior knowledge for image segmentation, we propose a framework for 3D reconstruction which uses such priors to overcome the ambiguity inherent in this problem. Our framework is based on an object-specific Markov Random Field (MRF)[10]. It ...

In this paper, we propose a new recognition method of lung nodules from X-ray CT images using 3D Markov random field(MRF) models. Pathological shadow candidates are detected by a mathematical morphology filter, and volume of interest(VOI) areas which include the shadow candidates are extracted. The probabilities of the hypotheses that the VOI areas come from nodules(which are candidates of canc...

A novel video object segmentation method is proposed which aims at combining color and motion information. The model has a multilayer structure: Each feature has its own layer, called feature layer, where a classical Markov random field (MRF) image segmentation model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model, called combined layer, wh...