Performance of 6DoF pose estimation techniques from RGB/RGBD images has improved significantly with sophisticated deep learning frameworks. These frameworks require large-scale training data based on real/synthetic RGB/RGB-D information. Difficulty of obtaining adequate training data has limited the scope of these frameworks for ubiquitous application areas. Also, fast pose estimation at inference time often requires high-end GPU(s) that restricts the scope for its application in mobile hardware. To address the requirement of training data adequacy, we propose a novel domain adaptation strategy to train from textureless CAD models with synthetic depth information only, and facilitate inferring poses from RGB images only. To allow faster inference on mobile hardware, we propose two lightweight architectures with a trade-off between ease of training and performance required by different applications. Experiments show comparable performance to the state-of-the-art in the challenging T-LESS dataset, with an inference time of ~ 200 ms using CPU on Google Pixel 2.

Development of an automatic streaming video segmentation method is crucial for many video analysis applications. However, consistency of temporal segmentation and scalability for real-time applications are difficult to achieve. This work proposes a linear-time video segmentation method which is scalable and temporally consistent for streaming videos. A Gaussian Mixture Model (GMM) is used to segment each frame while a recursive filtering updates the parameters of the GMM. This hybrid methodology can uniquely propagate Gaussian clusters through each new frame, update the variance recursively, and create or remove clusters as necessary. In this way, the model automatically manipulates the number of clusters in run-time and adapts to any video sequence over streaming frames maintaining temporal coherence. The method needs a distance threshold value as the main parameter. The creation and removal of new clusters are governed by a cluster similarity criterion that can be based on user-defined distance measure. The experimental results are presented with two possible distance measures. The performance of the proposed method on several datasets is found to be comparable to state-of-the-art video segmentation algorithms.

Background suppression has found several application areas in computer vision. Among others, abandoned object detection and moving object detection under dynamic backgrounds are two major application areas. Gaussian mixture model (GMM) is one of the most popular methods and has been applied in both of these application areas. However, the aforementioned areas present contradicting challenges for GMM. Abandoned objects need the GMM to slowly get adapted to prevent accidental assimilation of the foreground, while dynamic backgrounds need quick assimilation to prevent noisy detection. A novel dual GMM based on pixel history is proposed to provide practical solutions to both of these contradictory challenges. The method uses separate GMMs to represent foreground and background modes. A foreground mode is transferred to the GMM representing the background based on its history of occurrence. Thus, a quick assimilation is offered to dynamic backgrounds while abandoned objects face a very slow assimilation. In each case, old background is preserved. Extensive experiments are done to demonstrate the effectiveness of the proposed method and to compare it with a number of well known methods in literature.

Moving object detection is a popular research area due to its vast application in computer vision. Based on application areas, a number of moving object detection methods have been discovered. This work proposes a real-time method based on 3D histogram and temporal multiple mode selection, suited towards a vast majority of dynamic and noisy backgrounds, congested backgrounds and slow foregrounds. It is a generalization of [1] demonstrating improved performance. The temporal distribution of a video sequence can provide a history of the foreground motion and relatively static background. However, a dynamic background is identified by multiple intensity levels in the temporal distribution, and hence, cannot be properly modeled by a single histogram mode. The proposed multiple mode selection process involves identifying the dominant modes of the temporal distribution and assigning them to a multimodal background. The work provides a detailed analysis of the proposition, a clear description of the proposed algorithm and an extensive number of tests with some of the well-known methods in literature. It also demonstrates the improvement over its predecessor in several types of scenarios covered by a large number of datasets. Both qualitative and quantitative comparisons are carried out to demonstrate the applicability and accuracy of the proposed method.

The proposed work is targeted toward improving the Gaussian mixture model (GMM) for the background suppression-based moving object detection. The GMM has been widely used for moving object detection due to its high applicability. However, the GMM cannot properly model noisy or nonstationary backgrounds and fails to discriminate between the foreground and background modes. The extensions to GMM provide increased accuracy in expense of complex implementation and reduced applicability. In response, this work proposes two simple improvements: 1) a novel distance measure based on local support weights and histogram of gradients to provide distinct cluster values; and 2) use of background layer concept to properly segment the foreground. The method also uses variable number of clusters for generalization. The main advantages of the method are implicit use of pixel relationships through distance measure with least modification to the conventional GMM and effective background noise removal through the use of background layer concept with no postprocessing involved. The extensive experimentations on various types of video sequences are performed to validate the improvement in accuracy compared to the GMM and a number of state-of-the-art methods.

Segmentation of a medical image based on the modeling and estimation of the tissue intensity probability density functions via a Gaussian mixture model has recently received great attention. However, the Gaussian distribution is unbounded and symmetrical around its mean. This study presents a new bounded asymmetric mixture model for analyzing both univariate and multivariate data. The advantage of the proposed model is that it has the flexibility to fit different shapes of observed data such as non-Gaussian, nonsymmetric, and bounded support data. Another advantage is that each component of the proposed model has the ability to model the observed data with different bounded support regions, which is suitable for application on image segmentation. Our method is intuitively appealing, simple, and easy to implement. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood function. Numerical experiments are presented where the proposed model is tested in various images from simulated to real 3- D medical ones.

Segmentation of medical image based on the modeling and estimation of the tissue intensity probability density functions via Gaussian mixture model (GMM) has recently received great attention. However, Gaussian distribution is unbounded and symmetrical around its mean. This study presents a new bounded asymmetric mixture model for analyzing both univariate and multivariate data. The advantage of the proposed model is that it has the flexibility to fit different shapes of observed data such as non-Gaussian, non-symmetric, and bounded support data. Another advantage is that each component of the proposed model has the ability to model the observed data with different bounded support regions, which is suitable for application on image segmentation. Our method is intuitively appealing, simple, and easy to implement. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood function. Numerical experiments are presented where the proposed model is tested in various images from simulated to real 3D medical ones.

This paper presents a new finite mixture model for image segmentation. First, in order to take into account the spatial dependencies in an image, existing mixture models use a constant temperature parameter (β) throughout the image for every label. The constant value of β reduces the impact of noise in homogeneous regions but negatively affects segmentation along the border of two regions. We propose a new way to use a different value of β throughout the image. Secondly, in order to incorporate the correlation between each centre pixel and its neighboring pixels, existing mixture model gives the same importance to all pixels in a neighborhood window. We assign different weights to different pixels appearing in the window, which is based on the fact that the clique strength should be reduced with distance. Thirdly, our model is based on the Student's-t distribution, which is heavily tailed and more robust than Gaussian. We exploit Dirichlet distribution and Dirichlet law to incorporate the spatial relationships between pixels in an image. Finally, expectation maximization (EM) algorithm is adopted to maximize the data log-likelihood and to optimize the parameters. The performance is compared to other existing models based on the model-based techniques, demonstrating superiority of the proposed model for image segmentation.

This paper aims toward improving background suppression from video frames by incorporating multiresolution features in Gaussian mixture model (GMM). GMM has proven its place for background modeling due to its better applicability and robustness compared with other popular methods in literature. However, GMM fails in a number of situations such as noisy and non-stationary background, slow foregrounds, and illumination variation. Extensions to GMM have also been proposed to increase accuracy in expense of increased complexity, decrease in execution speed, and reduced applicability. In view of the above, this paper aims to provide a methodology to assimilate useful multiresolution features with GMM that considerably improves the performance. The contributions of this paper are: 1) a novel framework to incorporate wavelet subbands in GMM to improve its performance; 2) an approach to incorporate variable number of clusters in the aforesaid framework; and 3) a generic platform to use any multiresolution decomposition based GMM for background suppression. Extensive experimentations on several video sequences are performed to verify the improvement in accuracy compared with conventional GMM as well as a number of state-of-the-arts approaches. Along with qualitative and quantitative analysis, justification on the use of multiresolution is provided for clarification.

This paper introduces a bilateral filtering based mixture model for image segmentation. The mixture model uses Markov Random Field (MRF) to incorporate spatial relationship among neighboring pixels into the Gaussian Mixture Model (GMM) in order to perform a segmentation that is robust against noise and other environmental factors. The bilateral filtering is used to smooth the posterior probability map as part of the MRF used. The advantage of the proposed model is its simplified structure so that the Expectation Maximization algorithm can be directly applied to the log-likelihood function to compute the optimum parameters of the mixture model. The method has been extensively tested on synthetic and natural images and compared with some of the state-of-the-arts algorithms currently available. The experimental results show that the proposed method is comparable to the other methods in terms of accuracy and quality and simpler in terms of implementation.

We present a novel multiresolution analysis based stereo matching method using curvelets and modified adaptive support weight. Multiresolution analysis has long been applied to stereo correspondence. However, previous methods suffer from false matches arising from textureless region or repetitive textures and fattening effect due to area based matching. In the proposed approach, we have reduced false matches by using curvelet coefficients in different scales and orientations. Curvelet coefficients can uniquely represent different image points and increase matching accuracy. The fattening effect is reduced using support weights modified for curvelets. The proposed method is verified and compared with state-of-the art methods by extensive tests, and good results are obtained.