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In this paper, an approach to the semantic segmentation of 3D LiDAR point clouds obtained from natural scenes is introduced. Using a state-of-the-art projection-based semantic segmentation model as the core segmentation network, several recent advances in projection-based 3D semantic segmentation methods are aggregated into a single model. These adaptions include: scan unfolding, soft-kNN post-processing, and multi-projection fusion. A novel Naïve Bayesian approach to multi-projection fusion which weights class probabilities based on the outputs of the base classifiers is proposed to further increase robustness. Quantitative and qualitative evaluations on several datasets, including scenes from both urban and natural environments; show that aggregating these adaptions into a single model can further improve the accuracy of state-of-the-art projection-based approaches. Finally, it is demonstrated that the novel Naïve Bayesian approach to multi-projection fusion addresses a number of the challenges inherent to natural data while also improving results on urban data. • Natural data is difficult to analyse due to its complex structures and features. • Aggregating existing adaptations can improve 3D semantic analysis. • Natural data analysis can be improved on without degrading urban data performance. • Projection-based methods are an effective means to semantic analysis of 3D data. • Fusion of multiple perspectives improves semantic analysis of natural 3D data. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A powerful network called Asymmetric Filtering-based Dense Convolutional Neural Network (AF D-CNN) addresses person re-identification problem. • Joint Bayesian and re-ranking techniques are used to obtain the ranking lists which do not need dimensionality reduction. • Extensive experiments conducted on several datasets show that the proposed method achieves the state-of-the-art performance. Abstract Person re-identification aims at matching individuals across multiple camera views under surveillance systems. The major challenges lie in the lack of spatial and temporal cues, which makes it difficult to cope with large variations of lighting conditions, viewing angles, body poses and occlusions. How to extract multimodal features including facial features, physical features, behavioral features, color features, etc is still a fundamental problem in person re-identification. In this paper, we propose a novel Convolutional Neural Network, called Asymmetric Filtering-based Dense Convolutional Neural Network (AF D-CNN) to learn powerful features, which can extract different levels' features and take advantage of identity information. Moreover, instead of using typical metric learning methods, we obtain the ranking lists by merging Joint Bayesian and re-ranking techniques which do not need dimensionality reduction. Finally, extensive experiments show that our proposed architecture performs well on four popular benchmark datasets (CUHK01, CUHK03, Market-1501, DukeMTMC-reID). [ABSTRACT FROM AUTHOR]

This paper presents a new general framework for image segmentation. A level set formulation is used to model the boundaries of the image regions and a new Multilabel Fast Marching is introduced for the evolution of the region contours toward the segmentation result. Statistical tests are performed to yield an initial estimate of high-confidence subsets of the image regions. Furthermore, the velocities for the propagation of the region contours are defined in accordance with the a posteriori probability of the respective regions, leading to the Bayesian Level Set methodology described in this paper. Typical segmentation problems are considered and experimental results are given to illustrate the robustness of the method against noise and its performance in precise region boundary localization. [Copyright &y& Elsevier]

The multiframe super-resolution (SR) technique aims to obtain a high-resolution (HR) image by using a set of observed low-resolution (LR) images. In the reconstruction process, artifacts may be possibly produced due to the noise, especially in presence of stronger noise. In order to suppress artifacts while preserving discontinuities of images, in this paper a multiframe SR method is proposed by involving the reconstruction properties of the half-quadratic prior model together with the quadratic prior model using a convex combination. Moreover, by analyzing local features of the underlined HR image, these two prior models are combined by using an automatically calculated weight function, making both smooth and discontinuous pixels handled properly. A variational Bayesian inference (VBF) based algorithm is designed to efficiently and effectively seek the solution of the proposed method. With the VBF framework, motion parameters and hyper-parameters are all determined automatically, leading to an unsupervised SR method. The efficiency of the hybrid prior model is demonstrated theoretically and practically, which shows that our SR method can obtain better results from LR images even with stronger noise. Extensive experiments on several visual data have demonstrated the efficacy and superior performance of the proposed algorithm, which can not only preserve image details but also suppress artifacts. [ABSTRACT FROM AUTHOR]

Structure information has been increasingly incorporated into computer vision, however most trackers have ignored the inner spatial structure of the object. In this paper, we develop a simple yet robust tracking algorithm based on local structural cell graph (LSCG). This approach exploits both partial and spatial information of the target via representing the object with local structural cells (LSCs) and constructing a graph to model the spatial structure between the inner parts of the object. The tracking is formulated as matching LSCG, whose nodes are target parts and edges are the interaction between two parts. Within the Bayesian framework, we achieve object tracking by matching graphs between the reference and candidates. Eventually, the candidate with the highest similarity is the target. In addition, an updating strategy is adopted to help our tracker adapt to the fast time-varying object appearance. Experimental results demonstrate that the proposed method outperforms several state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Abstract: Recently, there has been a growing interest in the problem of learning mixture models from data. The reasons and motivations behind this interest are clear, since finite mixture models offer a formal approach to the important problems of clustering and data modeling. In this paper, we address the problem of modeling non-Gaussian data which are largely present, and occur naturally, in several computer vision and image processing applications via the learning of a generative infinite generalized Gaussian mixture model. The proposed model, which can be viewed as a Dirichlet process mixture of generalized Gaussian distributions, takes into account the feature selection problem, also, by determining a set of relevant features for each data cluster which provides better interpretability and generalization capabilities. We propose then an efficient algorithm to learn this infinite model parameters by estimating its posterior distributions using Markov Chain Monte Carlo (MCMC) simulations. We show how the model can be used, while comparing it with other models popular in the literature, in several challenging applications involving photographic and painting images categorization, image and video segmentation, and infrared facial expression recognition. [Copyright &y& Elsevier]

Abstract: In this paper we present a framework for accumulating on-line a model of a moving object (e.g., when manipulated by a robot). The proposed scheme is based on Bayesian filtering of local features, filtering jointly position, orientation and appearance information. The work presented here is novel in two aspects: first, we use an estimation mechanism that updates iteratively not only geometrical information, but also appearance information. Second, we propose a probabilistic version of the classical n-scan criterion that allows us to select which features are preserved and which are discarded, while making use of the available uncertainty model. The accumulated representations have been used in three different contexts: pose estimation, robotic grasping, and driver assistance scenario. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a probabilistic graphical model to represent weakly annotated images. We consider an image as weakly annotated if the number of keywords defined for it is less than the maximum number defined in the ground truth. This model is used to classify images and automatically extend existing annotations to new images by taking into account semantic relations between keywords. The proposed method has been evaluated in visual-textual classification and automatic annotation of images. The visual-textual classification is performed by using both visual and textual information. The experimental results, obtained from a database of more than 30,000 images, show an improvement by 50.5% in terms of recognition rate against only visual information classification. Taking into account semantic relations between keywords improves the recognition rate by 10.5%. Moreover, the proposed model can be used to extend existing annotations to weakly annotated images, by computing distributions of missing keywords. Semantic relations improve the mean rate of good annotations by 6.9%. Finally, the proposed method is competitive with a state-of-art model. [Copyright &y& Elsevier]