An Adaptive Smoothness Parameter Strategy for Variational Optical Flow Model

The smoothness parameter is used to balance the weight of the data term and the smoothness term in variational optical flow model, which plays very significant role for the optical flow estimation, but existing methods fail to obtain the optimal smoothness parameters (OSP). In order to solve this problem, an adaptive smoothness parameter strategy is proposed. First, an amalgamated simple linear iterative cluster (SLIC) and local membership function (LMF) algorithm is used to segment the entire image into several superpixel regions. Then, image quality parameters (IQP) are calculated, respectively, for each superpixel region. Finally, a neural network model is applied to compute the smoothness parameter by these image quality parameters of each superpixel region. Experiments were done in three public datasets (Middlebury, MPI_Sintel, and KITTI) and our self-constructed outdoor dataset with the proposed method and other existing classical methods; the results show that our OSP method achieves higher accuracy than other smoothness parameter selection methods in all these four datasets. Combined with the dual fractional order variational optical flow model (DFOVOFM), the proposed model shows better performance than other models in scenes with illumination inhomogeneity and abnormity. The OSP method fills the blank of the research of adaptive smoothness parameter, pushing the development of the variational optical flow models.

Semantic Correspondence with Geometric Structure Analysis

This article studies the correspondence problem for semantically similar images, which is challenging due to the joint visual and geometric deformations. We introduce the Flip-aware Distance Ratio method (FDR) to solve this problem from the perspective of geometric structure analysis. First, a distance ratio constraint is introduced to enforce the geometric consistencies between images with large visual variations, whereas local geometric jitters are tolerated via a smoothness term. For challenging cases with symmetric structures, our proposed method exploits Curl to suppress the mismatches. Subsequently, image correspondence is formulated as a permutation problem, for which we propose a Gradient Guided Simulated Annealing (GGSA) algorithm to perform a robust discrete optimization. Experiments on simulated and real-world datasets, where both visual and geometric deformations are present, indicate that our method significantly improves the baselines for both visually and semantically similar images.

A Novel OpenMVS-Based Texture Reconstruction Method Based on the Fully Automatic Plane Segmentation for 3D Mesh Models

The Markov Random Field (MRF) energy function, constructed by existing OpenMVS-based 3D texture reconstruction algorithms, considers only the image label of the adjacent triangle face for the smoothness term and ignores the planar-structure information of the model. As a result, the generated texture charts results have too many fragments, leading to a serious local miscut and color discontinuity between texture charts. This paper fully utilizes the planar structure information of the mesh model and the visual information of the 3D triangle face on the image and proposes an improved, faster, and high-quality texture chart generation method based on the texture chart generation algorithm of the OpenMVS. This methodology of the proposed approach is as follows: (1) The visual quality on different visual images of each triangle face is scored using the visual information of the triangle face on each image in the mesh model. (2) A fully automatic Variational Shape Approximation (VSA) plane segmentation algorithm is used to segment the blocked 3D mesh models. The proposed fully automatic VSA-based plane segmentation algorithm is suitable for multi-threaded parallel processing, which solves the VSA framework needed to manually set the number of planes and the low computational efficiency in a large scene model. (3) The visual quality of the triangle face on different visual images is used as the data term, and the image label of adjective triangle and result of plane segmentation are utilized as the smoothness term to construct the MRF energy function. (4) An image label is assigned to each triangle by the minimizing energy function. A texture chart is generated by clustering the topologically-adjacent triangle faces with the same image label, and the jagged boundaries of the texture chart are smoothed. Three sets of data of different types were used for quantitative and qualitative evaluation. Compared with the original OpenMVS texture chart generation method, the experiments show that the proposed approach significantly reduces the number of texture charts, significantly improves miscuts and color differences between texture charts, and highly boosts the efficiency of VSA plane segmentation algorithm and OpenMVS texture reconstruction.

Fast Variational Alignment of non-flat 1D Displacements for Applications in Neuroimaging

BackgroundIn the context of signal analysis and pattern matching, alignment of 1D signals for the comparison of signal morphologies is an important problem. For image processing and computer vision, 2D optical flow (OF) methods find wide application for motion analysis and image registration and variational OF methods have been continuously improved over the past decades.New MethodWe propose a variational method for the alignment and displacement estimation of 1D signals. We pose the estimation of non-flat displacements as an optimization problem with a similarity and smoothness term similar to variational OF estimation. To this end, we can make use of efficient optimization strategies that allow real-time applications on consumer grade hardware.ResultsWe apply our method to two applications from functional neuroimaging: The alignment of 2-photon imaging line scan recordings and the denoising of evoked and event-related potentials in single trial matrices. We can report state of the art results in terms of alignment quality and computing speeds.Existing MethodsExisting methods for 1D alignment target mostly constant displacements, do not allow native subsample precision or precise control over regularization or are slower than the proposed method.ConclusionsOur method is implemented as a MATLAB toolbox and is online available. It is suitable for 1D alignment problems, where high accuracy and high speed is needed and non-constant displacements occur.

REDUCTION OF THE FRONTO-PARALLEL BIAS FOR WIDE-BASELINE SEMI-GLOBAL MATCHING

<p><strong>Abstract.</strong> Semi-Global Matching (SGM) is a widely-used technique for dense image matching that is popular because of its accuracy and speed. While it works well for textured scenes, it can fail on slanted surfaces particularly in wide-baseline configurations due to the so-called fronto-parallel bias. In this paper, we propose an extension of SGM that utilizes image warping to reduce the fronto-parallel bias in the data term, based on estimating dominant slanted planes. The latter are also used as surface priors improving the smoothness term. Our proposed method calculates disparity maps for each dominant slanted plane and fuses them to obtain the final disparity map. We have quantitatively evaluated our approach outperforming SGM and SGM-P on synthetic data and demonstrate its potential on real data by qualitative results. In this way, we underscore the need to tackle the fronto-parallel bias in particular for wide-baseline configurations in both the data term and the smoothness term of SGM.</p>

Scene Flow Estimation Based on Adaptive Anisotropic Total Variation Flow-Driven Method

Scene flow estimation based on disparity and optical flow is a challenging task. We present a novel method based on adaptive anisotropic total variation flow-driven method for scene flow estimation from a calibrated stereo image sequence. The basic idea is that diffusion of flow field in different directions has different rates, which can be used to calculate total variation and anisotropic diffusion automatically. Brightness consistency and gradient consistency constraint are employed to establish the data term, and adaptive anisotropic flow-driven penalty constraint is employed to establish the smoothness term. Similar to the optical flow estimation, there are also large displacement problems in the estimation of the scene flow, which is solved by introducing a hierarchical computing optimization. The proposed method is verified by using the synthetic dataset and the real scene image sequences. The experimental results show the effectiveness of the proposed algorithm.

GLOBAL PATCH MATCHING

This paper introduces a novel global patch matching method that focuses on how to remove fronto-parallel bias and obtain continuous smooth surfaces with assuming that the scenes covered by stereos are piecewise continuous. Firstly, simple linear iterative cluster method (SLIC) is used to segment the base image into a series of patches. Then, a global energy function, which consists of a data term and a smoothness term, is built on the patches. The data term is the second-order Taylor expansion of correlation coefficients, and the smoothness term is built by combing connectivity constraints and the coplanarity constraints are combined to construct the smoothness term. Finally, the global energy function can be built by combining the data term and the smoothness term. We rewrite the global energy function in a quadratic matrix function, and use least square methods to obtain the optimal solution. Experiments on Adirondack stereo and Motorcycle stereo of Middlebury benchmark show that the proposed method can remove fronto-parallel bias effectively, and produce continuous smooth surfaces.

“Cellular-Cut”-Interactive n-Dimensional Image Segmentation Using Cellular Automata

Interactive segmentation of images has become an integral part of image processing applications. Several graph based segmentation techniques have been developed, which depend upon global minimization of the energy cost function. An adequate scheme of interactive segmentation still needs a skilled initialization of regions with user-defined seeds pixels distributed over the entire image. We propose an iterative segmentation technique based on Cellular Automaton which focuses to reduce the user efforts required to provide initialization. The existing algorithms based on Cellular Automaton only use local smoothness term in label propagation making them highly sensitive to user-defined seeds pixels. To reduce the sensitivity towards initial user definition of regions, global constraints are introduced along with local information to propagate labels. The results obtained are comparable to the state-of-the-art interactive segmentation techniques on a standard dataset.

BOUNDARY DEPTH INFORMATION USING HOPFIELD NEURAL NETWORK

Depth information is widely used for representation, reconstruction and modeling of 3D scene. Generally two kinds of methods can obtain the depth information. One is to use the distance cues from the depth camera, but the results heavily depend on the device, and the accuracy is degraded greatly when the distance from the object is increased. The other one uses the binocular cues from the matching to obtain the depth information. It is more and more mature and convenient to collect the depth information of different scenes by stereo matching methods. In the objective function, the data term is to ensure that the difference between the matched pixels is small, and the smoothness term is to smooth the neighbors with different disparities. Nonetheless, the smoothness term blurs the boundary depth information of the object which becomes the bottleneck of the stereo matching. This paper proposes a novel energy function for the boundary to keep the discontinuities and uses the Hopfield neural network to solve the optimization. We first extract the region of interest areas which are the boundary pixels in original images. Then, we develop the boundary energy function to calculate the matching cost. At last, we solve the optimization globally by the Hopfield neural network. The Middlebury stereo benchmark is used to test the proposed method, and results show that our boundary depth information is more accurate than other state-of-the-art methods and can be used to optimize the results of other stereo matching methods.

BOUNDARY DEPTH INFORMATION USING HOPFIELD NEURAL NETWORK

Depth information is widely used for representation, reconstruction and modeling of 3D scene. Generally two kinds of methods can obtain the depth information. One is to use the distance cues from the depth camera, but the results heavily depend on the device, and the accuracy is degraded greatly when the distance from the object is increased. The other one uses the binocular cues from the matching to obtain the depth information. It is more and more mature and convenient to collect the depth information of different scenes by stereo matching methods. In the objective function, the data term is to ensure that the difference between the matched pixels is small, and the smoothness term is to smooth the neighbors with different disparities. Nonetheless, the smoothness term blurs the boundary depth information of the object which becomes the bottleneck of the stereo matching. This paper proposes a novel energy function for the boundary to keep the discontinuities and uses the Hopfield neural network to solve the optimization. We first extract the region of interest areas which are the boundary pixels in original images. Then, we develop the boundary energy function to calculate the matching cost. At last, we solve the optimization globally by the Hopfield neural network. The Middlebury stereo benchmark is used to test the proposed method, and results show that our boundary depth information is more accurate than other state-of-the-art methods and can be used to optimize the results of other stereo matching methods.