Textureless Surface Research Articles

A Robust Texture-Less Gray-Scale Surface Matching Method Applied to a Liquid Crystal Display TV Diffuser Plate Assembly System

In most liquid crystal display (LCD) backlight modules (BLMs), diffuser plates (DPs) play the essential role in blurring the backlight. A common BLM consists of multiple superimposed optical films. In a vision-based automated assembly system, to ensure sufficient accuracy, each of the multiple cameras usually shoots a local corner of the DP and jointly estimates the target pose, guiding the robot to assemble the DP on the BLM. In general, DPs are typical of texture-less objects with simple shapes. Due to the image background of superimposed multilayer optical films, the robustness of the most common detection methods must be improved to meet industrial needs. To solve the above problem, a texture-less surface matching method based on gray-scale images was proposed. An augmented and normalized gray-scale vector represents the texture-less gray-scale surface in a low-dimensional space. The cosine distance is then used to calculate the similarity between the template and matching vectors, combined with shape-based matching (SBM); the proposed method can obtain high robustness when detecting DPs. An image database from actual production lines was used in the experiment. In comparative tests with the NCC, SBM, YOLOv5s, and YOLOv5x methods, our proposed method had the best precision at all confidence thresholds. Although recall was slightly inferior to SBM, the comprehensive evaluation F1-Score reached 0.826, significantly outperforming the other methods. Regarding localizing accuracy, our algorithm also performed best, reaching 5.7 pixels. Although the time consumption of a single prediction is about 0.6 s, it can still meet industrial needs. These experimental results show that the proposed method has high robustness in detecting DPs and is especially suitable for vision-based automatic assembly tasks in BLM.

OrangeStereo: A navel orange stereo matching network for 3D surface reconstruction

Three-dimensional (3D) surface reconstruction is crucial to accurately detecting navel orange external qualities such as size and shape, and binocular stereo vision is promising for practical sorting scenarios due to its high frame rates and ease of deployment. For objects with texture-less surface, such as navel oranges, it is very challenging to match the corresponding points using traditional stereo matching algorithms because the detected feature points have high similarity in color as well as structure. Most of the existing deep learning-based stereo matching algorithms focus on the stereo matching performance of the whole image and lack the attention to the matching performance of the main region. Meanwhile, simple background with less texture will bring more uncertainty to the matching of corresponding points, which affects the robustness of the model. The OrangeStereo was proposed based on the GwcNet structure to achieve a better corresponding points matching effect of navel oranges, which can provide more accurate 3D surface information. The Structural Features Extraction (SFE) module was introduced after the initial feature extraction module to extract stable structural features and avoid the limitations of using single convolutional features. The Attention Weights Generation (AWG) module was utilized at matching cost calculation stage to help suppress redundant information and make the network pay more attention to the matching performance of specific regions. The improved loss function assigned a greater weight to the loss of the navel orange region using semantic information from left image to make the network more focused on the matching effect of the navel orange region. An ablation study was conducted to confirm the efficiency of the SFE module, AWG module, and the improved loss function of the algorithm. The ablation study contained five sub-experiments, including the baseline model, adding the SFE and the AWG modules respectively, and whether to add the improved loss function based on the addition of previous two modules. Additionally, PSMNet, GwcNet, RealtimeStereo, and ACVNet, four typical stereo matching methods, were contrasted with the OrangeStereo. The proposed OrangeStereo achieved better performance with EPE, RMSE, bad-1, bad-3, bad-5, and inference time of 0.60 pixels, 3.53 pixels, 2.37 %, 1.83 %, 1.62 %, and 33 ms in the orange region, respectively. The R2 and RMSE for the depth of orange utilizing our method were 0.982 and 0.81 mm, respectively. The proposed algorithm can be employed to obtain accurate depth information of fruits quickly, and it is expected to be used in the 3D reconstruction of fruits in the actual commercial fruit sorting lines to achieve more accurate external quality assessment.

Texture-less surface reconstruction using shape-based image augmentation

The development of intelligent Robot-Assisted Minimally Invasive Surgery demands geometric reconstruction from endoscopic images. However, images of human tissue surfaces are commonly texture-less. Obtaining the dense depth map of a texture-less scene is very difficult because traditional feature-based 3D reconstruction methods cannot detect enough features to build dense correspondences for depth computation. Given this problem, this study proposes a novel reconstruction method based on our shape-based image augmentation method. The main contribution of this method is the provision of a novel means to resolve the texture-less problem mainly on the input data level. In our method, we first calculate two shape gradient maps using Shape-From-Shading (SFS) method and we build Fast Point Feature Histogram (FPFH) 3D descriptor map according to the shape. Second, a series of augmented images can be computed by combining shape gradient maps, FPFH map, and the original image with different weights. Finally, we detect features on the new augmented images. Based on feature calculated sparse depth information and SFS calculated dense shape information, we further integrate a rectified dense depth map. Experiments show that our method can reconstruct texture-less surfaces with good accuracy.

Hybrid cost aggregation for dense stereo matching

Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions.

INCORRECT MATCH DETECTION METHOD FOR ARCTIC SEA-ICE RECONSTRUCTION USING UAV IMAGES

Abstract. Shapes and surface roughness, which are considered as key indicators in understanding Arctic sea-ice, can be measured from the digital surface model (DSM) of the target area. Unmanned aerial vehicle (UAV) flying at low altitudes enables theoretically accurate DSM generation. However, the characteristics of sea-ice with textureless surface and incessant motion make image matching difficult for DSM generation. In this paper, we propose a method for effectively detecting incorrect matches before correcting a sea-ice DSM derived from UAV images. The proposed method variably adjusts the size of search window to analyze the matching results of DSM generated and distinguishes incorrect matches. Experimental results showed that the sea-ice DSM produced large errors along the textureless surfaces, and that the incorrect matches could be effectively detected by the proposed method.

Photogrammetric measurements of 3D printed microfluidic devices

Additive manufacturing (AM) processes are being more frequently applied in several fields ranging from the industrial to the biomedical, in large part owing to their advantages which make them suitable for several applications such as scaffolds for tissue engineering, dental procedures, and 3D models to improve surgical planning. Moreover, these processes are particularly suited for the fabrication of microfluidic devices and labs-on-a-chip (LOC) designed to work with biological samples and chemical reaction mixtures.An aspect not sufficiently investigated is related to the dimensional verification of these devices. The main criticality is the texture-less surface that characterizes the AM products and strongly affects the effectiveness of most currently available 3D optical measuring instruments.In this study, a passive photogrammetric scanning system has been used as a non-destructive and low-cost technique for the reconstruction and measurement of 3D printed microfluidic devices. Four devices, manufactured with stereolithography (SLA), fused deposition modelling (FDM) a Stratasys trademark, also known as fused filament fabrication (FFF), and Polyjet have been reconstructed and measured, and the results have been compared to those obtained with optical profilometry that is considered as the gold standard.

How Graphene Islands Are Unidirectionally Aligned on the Ge(110) Surface

The unidirectional alignment of graphene islands is essential to the synthesis of wafer-scale single-crystal graphene on Ge(110) surface, but the underlying mechanism is not well-understood. Here we report that the necessary coalignment of the nucleating graphene islands on Ge(110) surface is caused by the presence of step-pattern; we show that on the preannealed Ge(110) textureless surface the graphene islands appear nonpreferentially orientated, while on the Ge(110) surfaces with natural step pattern, all graphene islands emerge coaligned. First-principles calculations and theoretical analysis reveal this different alignment behaviors originate from the strong chemical binding formed between the graphene island edges and the atomic steps on the Ge(110) surface, and the lattice matching at edge-step interface dictates the alignment of graphene islands with the armchair direction of graphene along the [-110] direction of the Ge(110) substrate.

Texture Illumination Separation for Single-Shot Structured Light Reconstruction.

Active illumination based methods have a trade-off between acquisition time and resolution of the estimated 3D shapes. Multi-shot approaches can generate dense reconstructions but require stationary scenes. Single-shot methods are applicable to dynamic objects but can only estimate sparse reconstructions and are sensitive to surface texture. We present a single-shot approach to produce dense shape reconstructions of highly textured objects illuminated by one or more projectors. The key to our approach is an image decomposition scheme that can recover the illumination image of different projectors and the texture images of the scene from their mixed appearances. We focus on three cases of mixed appearances: the illumination from one projector onto textured surface, illumination from multiple projectors onto a textureless surface, or their combined effect. Our method can accurately compute per-pixel warps from the illumination patterns and the texture template to the observed image. The texture template is obtained by interleaving the projection sequence with an all-white pattern. The estimated warps are reliable even with infrequent interleaved projection and strong object deformation. Thus, we obtain detailed shape reconstruction and dense motion tracking of the textured surfaces. The proposed method, implemented using a one camera and two projectors system, is validated on synthetic and real data containing subtle non-rigid surface deformations.

Comparative Studies on the Solvent Quality and Atmosphere Humidity for Electrospinning of Nanoporous Polyetherimide Fibers

Implications regarding requirements of performing a successive electrospinning and producing nanoporous polyetherimide (PEI) fibers are discussed through electrospinning PEI solutions of three nonvolatile solvents, that is, dimethylforamide (DMF), dimethylacetamide (DMAc), and N-methyl-2-pyrrolidone (NMP), under atmospheres of constant temperature and varying levels of relative humidity (RH). The results demonstrate depending on nature of miscibility area in ternary phase diagram, a minimum RH is necessary to stabilize fiber formation. Furthermore, RH of operating environment affects diameter and both surface and interior morphologies of PEI electrospun fibers through involving the rate of phase demixing and viscoelasticity of solution. Considering fibers produced from NMP solutions because of delayed demixing, solvent drying precedes phase demixing or takes place in a comparable rate in high RH which leads to solid cross-section and texture-less surface with slight porosity. By choosing DMF or DMAc as electrospinning solvent, thicker fibers with rough surface and porous cross-section are expected.

3D object model reconstruction from image sequence based on photometric consistency in volume space

This paper presents a system that can reconstruct a photorealistic 3D object model from an image sequence captured at arbitrary viewpoints. The whole system consists of four steps: camera calibration, volumetric modeling, polygonal model formation and texture mapping. We adopt the shape-from-silhouette approach for volumetric modeling. There are two common types of object surface that are difficult to reconstruct--textureless surface and concave surface. To tackle the problems, we propose to perform the volumetric modeling based on the constraints of viewpoint proximity and photometric consistency in the volume space. The volumetric model is converted to the mesh model for efficient manipulation. Finally, the texture map is generated from the image sequence to give the 3D model a photorealistic appearance. Some reconstructed object models are presented to demonstrate the superior performance of our system as compared with the conventional modeling technique based on the photo-consistency in the image space.

Meso-scale texture of mesophase pitch and its spun fiber

The mesoscopic textures of the mesophase pitch and its spun fiber were examined in the as-prepared and solvent extracted forms using high resolution scanning electron microscopy (HR-SEM). HR-SEM revealed spherical or elliptical grains of 10–50 nm size. Such units were defined as microdomains in the insoluble fraction of the mesophase pitch. The solvent of high dissolving activity reduced the size of the grains, while maintaining their basic shape. No porosity was found in the surface of the microdomain. The as-spun fiber showed a flat textureless plane on its surface along the fiber axis. The solvent extraction closed up the spherical micrograin of 10 nm, as viewed perpendicular to the surface. A tilt angle of 10 ° to the surface of the fiber revealed the pleat alignment of insoluble microdomains. The carbonization after the stabilization of the fiber provided pleat alignment of microdomains on the surface when the fiber was tilted to be observed. Pleat microdomains observed on the surfaces of as-extracted and carbonized fibers were basically the same. The insoluble fraction in the mesophase pitch forms the particular microdomains in the extracted pitch. Thus, the insoluble microdomains in the pitch were deformed into the pleat unit through the spinning. The soluble fraction which coats the mesoscopic units of the insoluble fraction to give the textureless surface of the as-spun fiber may shrink during carbonization to follow the shapes of the insoluble microdomains. The unique shape of the insoluble fraction may reflect the channel structure observed in polymer blends, since microdomains in the fiber reflect the shape and size of those in the pitch after the spinning. The shape, size and arrangement of microdomains in the fiber may be one of the key factors in finding ways to control the mechanical properties of mesophase pitch-based carbon fibers.

Machine vision for dimensional measurements in mines

Fast growing mechanization and automation in the mining industry has been adopted to increase production, productivity and safety, but it requires quick, precise and automatic measurements. In particular, the measurement of deformations around a coal-mining face has been an extremely difficult task due to the time and space constraints of such sites. In a continuing drive to reduce the inherent risks of danger during working around such difficult areas, much routine inspection work is increasingly being undertaken by remote measuring systems. The advent of digital photogrammetry provides an opportunity for real-time dimensional measurement of such inaccessible areas. In this paper, some examples of reliability testing of automatic measurements using grey scale digital images of mining structures is described. A CCD (charged coupled device) based active triangulation system is combined with image understanding algorithms to provide machine vision capability and for automatic measurement of the textureless and featureless surface of sandstone strata. This paper also presents some results of initial laboratory investigations conducted to test the reliability of basic components of the system.

Digital Photogrammetry for Automatic Close Range Measurement of Textureless and Featureless Objects

Diffraction grating based simultaneous projection of a large number of distinct and circular laser dots was adopted to solve correspondence and accuracy problems during automatic three dimensional measurement of a textureless and featureless object at close range. After rigid placement of the projector over the telescope of a geodimeter, accurate calibration and derivation of a camera model of the virtual active camera (the projector) was done without a control field and using a bundle adjustment procedure. While automatic detection and labelling of the actively placed target points over any given object remained a problem, algorithms were developed to locate and label these target points placed over a nearly flat and textureless surface. A conventional thresholding method was combined with least squares matching (LSM) to provide automatic subpixel image co‐ordinate measurement of the target points placed over a textureless object but, considering the real object conditions, semi‐automatic image co‐ordinate measurement was done by LSM. The system provided an intersection parallax of 0.3 mm during a laboratory test scan of a planar object at 4.5 m distance.