Contour Generator Points for Threshold Selection and a Novel Photo-Consistency Measure for Space Carving

Space carving has emerged as a powerful method for multiview scene reconstruction. Although a wide variety of methods have been proposed, the quality of the reconstruction remains highly-dependent on the photometric consistency measure, and the threshold used to carve away voxels. In this paper, we present a novel photo-consistency measure that is motivated by a multiset variant of the chamfer distance. The new measure is robust to high amounts of within-view color variance and also takes into account the projection angles of back-projected pixels. Another critical issue in space carving is the selection of the photo-consistency threshold used to determine what surface voxels are kept or carved away. In this paper, a reliable threshold selection technique is proposed that examines the photo-consistency values at contour generator points. Contour generators are points that lie on both the surface of the object and the visual hull. To determine the threshold, a percentile ranking of the photo-consistency values of these generator points is used. This improved technique is applicable to a wide variety of photo-consistency measures, including the new measure presented in this paper. Also presented in this paper is a method to choose between photo-consistency measures, and voxel array resolutions prior to carving using receiver operating characteristic (ROC) curves

3D Object Pose Estimation Using Chamfer Matching and Flexible CAD File Base

Estimating the object pose is an interesting topic in the industrial robotic vision field. By having an accurate result for detecting object pose, it means the system performs the task as the target in the bin-picking technique. The methods which are developed are varies widely. But the challenge for this paper is estimating a 3D object using mono camera accurately. The object which is used in this paper has the symmetric rotational shape, in this case is the sprayer. In this paper, the camera uses a tool from the Blender Software, such that the ground truth is measurable and it will be the reference for calculating the error. The applied algorithms of this paper are Border Line Extraction Algorithm utilized in the template generation step as the reference template, Directional Chamfer Matching for detecting the coarse pose, and Lavenberg-Marquardt Method to optimize the object pose result. The result achieves the average error of the coarse pose for x and y position (translation pose) are 2.05 mm and 0.71 mm. Meanwhile for the optimized pose, the average error for x and y position (translation pose) are 1.82 mm and 0.24 mm. Regarding the rotational pose, the average error of the rotational coarse pose with respect to x and z axis are 0.01 degree and 0.45 degree. Whereas the average error of the rotational optimized pose with respect to x and z axis are 2.88 degree and 0.82 degree

Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision

Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.Authors want to give thanks to the Xunta de Galicia under Grant ED481A and the Spanish Ministry of Economy and Competitiveness under the National Science Program TEC2017-84197-C4-2-R

Discrete Optimization Methods for Segmentation and Matching

This dissertation studies discrete optimization methods for several computer vision problems. In the first part, a new objective function for superpixel segmentation is proposed. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. I present a new graph construction for images and show that this construction induces a matroid. The segmentation is then given by the graph topology which maximizes the objective function under the matroid constraint. By exploiting submodular and monotonic properties of the objective function, I develop an efficient algorithm with a worst-case performance bound of $\frac{1}{2}$ for the superpixel segmentation problem. Extensive experiments on the Berkeley segmentation benchmark show the proposed algorithm outperforms the state of the art in all the standard evaluation metrics. Next, I propose a video segmentation algorithm by maximizing a submodular objective function subject to a matroid constraint. This function is similar to the standard energy function in computer vision with unary terms, pairwise terms from the Potts model, and a novel higher-order term based on appearance histograms. I show that the standard Potts model prior, which becomes non-submodular for multi-label problems, still induces a submodular function in a maximization framework. A new higher-order prior further enforces consistency in the appearance histograms both spatially and temporally across the video. The matroid constraint leads to a simple algorithm with a performance bound of $\frac{1}{2}$. A branch and bound procedure is also presented to improve the solution computed by the algorithm. The last part of the dissertation studies the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem, chamfer matching remains to be the preferred method when speed and robustness are considered. In this dissertation, I significantly improve the accuracy of chamfer matching while reducing the computational time from linear to sublinear (shown empirically). It is achieved by incorporating edge orientation information in the matching algorithm so the resulting cost function is piecewise smooth and the cost variation is tightly bounded. Moreover, I present a sublinear time algorithm for exact computation of the directional chamfer matching score using techniques from 3D distance transforms and directional integral images. In addition, the smooth cost function allows one to bound the cost distribution of large neighborhoods and skip the bad hypotheses. Experiments show that the proposed approach improves the speed of the original chamfer matching up to an order of 45 times, and it is much faster than many state of art techniques while the accuracy is comparable. I further demonstrate the application of the proposed algorithm in providing seamless operation for a robotic bin picking system

ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours

Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned. Recent ConvNet-based architectures that were proposed to align image structures tend to fail with contour representation of shapes, mostly due to the use of proximity-insensitive pixel-wise similarity measures as loss functions in their training processes. This work presents a novel ConvNet, "ProAlignNet" that accounts for large scale misalignments and complex transformations between the contour shapes. It infers the warp parameters in a multi-scale fashion with progressively increasing complex transformations over increasing scales. It learns --without supervision-- to align contours, agnostic to noise and missing parts, by training with a novel loss function which is derived an upperbound of a proximity-sensitive and local shape-dependent similarity metric that uses classical Morphological Chamfer Distance Transform. We evaluate the reliability of these proposals on a simulated MNIST noisy contours dataset via some basic sanity check experiments. Next, we demonstrate the effectiveness of the proposed models in two real-world applications of (i) aligning geo-parcel data to aerial image maps and (ii) refining coarsely annotated segmentation labels. In both applications, the proposed models consistently perform superior to state-of-the-art methods.Comment: Accepted at CVPR 202

Object Pose Estimation in Monocular Image Using Modified FDCM

In this paper, a new method for object detection and pose estimation in a monocular image is proposed based on FDCM method. it can detect object with high speed running time, even if the object was under the partial occlusion or in bad illumination. In addition, It requires only single template without any training process. The Modied FDCM based on FDCM with improvments, the LSD method was used in MFDCM instead of the line tting method, besides the integral distance transform was replaced with a distance transform image, and using an angular Voronoi diagram. In addition, the search process depends on Line segments based search instead of the sliding window search in FDCM. The MFDCM was evaluated by comparing it with FDCM in dierent scenarios and with other four methods: COF, HALCON, LINE2D, and BOLD using D-textureless dataset. The comparison results show that MFDCM was at least 14 times faster than FDCM in tested scenarios. Furthermore, it has the highest correct detection rate among all tested method with small advantage from COF and BLOD methods, while it was a little slower than LINE2D which was the fasted method among compared methods. The results proves that MFDCM able to detect and pose estimation of the objects in the clear or clustered background from a monocular image with high speed running time, even if the object was under the partial occlusion which makes it robust and reliable for real-time applications