Gaussian Image Research Articles

Representation of object's shape by multiple electric images in electrolocation.

Weakly electric fish generate an electric field by discharging an electric organ located on the tail region. An object near the fish modulates the self-generated electric field. The modulated field enables the fish to perceive objects even in complete darkness. The ability to perceive objects is provided by the electrosensory system of the fish. Electroreceptors distributed on the fish's skin surface can sense the modulated field, on the basis of transdermal voltage across the skin surface, called electric images. The fish can extract object's features such as lateral distance, size, shape, and electric property from an electric image. Although previous studies have demonstrated the relationship between electric-image features and object's distance and size, it remains unclear what features of an electric image represent the object's shape. We make here a hypothesis that shape information is not represented by a single image but by multiple images caused by the object's rotation or fish movement around the object. To test the hypothesis, we develop a computational model that can predict electric images produced by the rotation of differently shaped objects. We used five different shapes of resistive objects: a circle, a square, an equilateral triangle, a rectangle, and an ellipsoid. We show that differently shaped objects of a fixed arrangement generate similar Gaussian electric images, irrespective of their shapes. We also show that the features of an electric image such as the peak amplitude, half-maximum width, and peak position exhibit the angle-dependent variations characteristic to object rotation, depending on object shapes and lateral distances. Furthermore, we demonstrate that an integration effect of the peak amplitude and half-maximum width could be an invariant measure of object shape. These results suggest that the fish could perceive an object shape by combining those image features produced during exploratory behaviors around the object.

Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration

The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in ordinary images. However, since remote sensing images are more complex and more blurry than ordinary images, most of the existing methods are not good enough for remote sensing image restoration. To address such problem, we propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to reconstruct high-quality images. We build local and global memory connections to combine image detail with global information. To further reduce parameters and ease time consumption, we propose Downsampling Units, shrinking the spatial size of feature maps. We verify its capability on two representative applications, Gaussian image denoising and single image super-resolution (SR). DMCN is tested on three remote sensing datasets with various spatial resolution. Experimental results indicate that our method yields promising improvements and better visual performance over the current state-of-the-art. The PSNR and SSIM improvements over the second best method are up to 0.3 dB.

Research on optimization of image fast feature point matching algorithm

The author studied the feature point extraction and matching based on BRISK and ORB algorithms, experimented with the advantages of both algorithms, and ascertained optimal pyramid layer and inter-layer scale parameters used in features extraction and matching for the same scale image and different scale images with BRISK and ORB algorithm, and analyzed the effectiveness of different parameters combinations on the accuracies of feature extraction and matching and proposed method to determine parameters based on the results. In addition, comparing with the traditional algorithm, using the optimal algorithm with the parameters combining Gaussian denoising, graying, and image sharpening, the ratio of feature points for detection improved 3%; the number of effective matching points increased by nearly 2%. Meanwhile, an algorithm experiment on UAV image mosaic was carried out. The transition of mosaic image color was more natural, and there was no clear mosaic joint with the stitching effect, which indicated that the optimized parameters and the extracted feature point pairs can be used for matrix operations and the algorithm is suitable for UAV image mosaic processing.

ASSESSING THE ACCURACY AND PRECISION OF IMPERFECT POINT CLOUDS FOR 3D INDOOR MAPPING AND MODELING

Abstract. In recent years, growing public interest in three-dimensional technology has led to the emergence of affordable platforms that can capture 3D scenes for use in a wide range of consumer applications. These platforms are often widely available, inexpensive, and can potentially find dual use in taking measurements of indoor spaces for creating indoor maps. Their affordability, however, usually comes at the cost of reduced accuracy and precision, which becomes more apparent when these instruments are pushed to their limits to scan an entire room. The point cloud measurements they produce often exhibit systematic drift and random noise that can make performing comparisons with accurate data difficult, akin to trying to compare a fuzzy trapezoid to a perfect square with sharp edges. This paper outlines a process for assessing the accuracy and precision of these imperfect point clouds in the context of indoor mapping by integrating techniques such as the extended Gaussian image, iterative closest point registration, and histogram thresholding. A case study is provided at the end to demonstrate use of this process for evaluating the performance of the Scanse Sweep 3D, an ultra-low cost panoramic laser scanner.

Fast and robust local feature extraction for 3D reconstruction

Large-scale 3D reconstruction based on structure from motion (SFM) has attracted much attention from the computer vision community. Local feature extraction, which is typically used to locate correspondences between images, is one of the most important components of SFM. In this paper, we propose a fast and robust local feature extraction method, called OOD, for SFM-based 3D reconstruction. First, the adaptive and generic accelerated segment test (AGAST) detector is used to detect keypoints, and the image moment is used to define an orientation for these keypoints. Second, a novel descriptor based on the oriented difference of Gaussians is proposed to describe the keypoints, which can be computed directly from the difference of Gaussian image. Finally, a comprehensive evaluation is conducted using several benchmark datasets. Our experimental results indicate that the OOD method outperforms state-of-the-art methods.

Adaptive Residual Networks for High-Quality Image Restoration.

Image restoration methods based on convolutional neural networks have shown great success in the literature. However, since most of networks are not deep enough, there is still some room for the performance improvement. On the other hand, though some models are deep and introduce shortcuts for easy training, they ignore the importance of location and scaling of different inputs within the shortcuts. As a result, existing networks can only handle one specific image restoration application. To address such problems, we propose a novel adaptive residual network (ARN) for high-quality image restoration in this paper. Our ARN is a deep residual network, which is composed of convolutional layers, parametric rectified linear unit layers, and some adaptive shortcuts. We assign different scaling parameters to different inputs of the shortcuts, where the scaling is considered as part parameters of the ARN and trained adaptively according to different applications. Due to the special construction of ARN, it can solve many image restoration problems and have superior performance. We demonstrate its capabilities with three representative applications, including Gaussian image denoising, single image super resolution, and JPEG image deblocking. Experimental results prove that our model greatly outperforms numerous state-of-the-art restoration methods in terms of both peak signal-to-noise ratio and structure similarity index metrics, e.g., it achieves 0.2-0.3 dB gain in average compared with the second best method at a wide range of situations.

Bayesian method application for color demosaicking

This study presents a Bayesian approach based on a color image demosaicking algorithm. The proposed method is composed of pointwise and patchwise measurements. The estimation of the missing pixel is formulated as a maximum a posteriori and a minimum energy function. By utilizing Bayesian theory and some prior knowledge, the missing color information is estimated with a statistics-based approach. Under the maximum a posteriori and Bayesian framework, the desired target image corresponds to the optimal reconstruction given the mosaicked image. Compared with existing demosaicking methods, the proposed algorithm improves the CPSNR, S-CIELAB, FSIM, and zipper effect measurements while maintaining high efficiency. Moreover, it handles Gaussian and Poisson noisy images better than other conventional images.

Disjointed force polyhedra

This paper presents a new computational framework for 3D graphic statics based on the concept of disjointed force polyhedra. At the core of this framework are the Extended Gaussian Image and area-pursuit algorithms, which allow more precise control of the face areas of force polyhedra, and consequently of the magnitudes and distributions of the forces within the structure. The explicit control of the polyhedral face areas enables designers to implement more quantitative, force-driven constraints and it expands the range of 3D graphic statics applications beyond just shape explorations. The significance and potential of this new computational approach to 3D graphic statics is demonstrated through numerous examples, which illustrate how the disjointed force polyhedra enable force-driven design explorations of new structural typologies that were simply not realisable with previous implementations of 3D graphic statics.

A framework for fast automatic image cropping based on deep saliency map detection and gaussian filter

ABSTRACTExtracting robust visual saliency map and image cropping are fundamental problems in computer vision, graphics, and so on. It is not easy task to accurately detect and crop the entire salient object from images with complex background. In this paper, a deep learning strategy is adopted to train a large data-set of images, to get saliency map from the input image using graph-based segmentation and gray level adjustment to enhance and extract more accurate and clear saliency map. Furthermore, the Gaussian filter and image scaling used along with cropping method to keep better presentation of the visual object. The important task of overall framework is to take care about relevant image contents as well as to identify more region of interest and get optimum rectangle from the saliency map with minimum and maximum rectangular windows. Quality and low computational complexity have been focused while performing the cropping operation because automatic and efficient cropping technique should not only rely on geometric constraints, but it should be fast enough to consider the important image contents. The applied method use different data-set of images, to ensure the efficiency of this technique and the experimental results show that the framework is not only fast as well as much better for image cropping. We used Matlab and Caffe framework for efficient experimental results.

On integrability in Grassmann geometries: integrable systems associated with fourfolds in Gr(3,5)

Let Gr(d; n) be the Grassmannian of d-dimensional linear subspaces of an n-dimensional vector space V n. A submanifold X Gr(d; n) gives rise to a differential system ⊂(X) that governs d-dimensional submanifolds of V n whose Gaussian image is contained in X. Systems of the form Σ(X) appear in numerous applications in continuum mechanics, theory of integrable systems, general relativity and differential geometry. They include such wellknown examples as the dispersionless Kadomtsev-Petviashvili equation, the Boyer-Finley equation, Plebansky's heavenly equations, and so on. In this paper we concentrate on the particularly interesting case of this construction where X is a fourfold in Gr(3; 5). Our main goal is to investigate differential-geometric and integrability aspects of the corresponding systems Σ(X). We demonstrate the equivalence of several approaches to dispersionless integrability such as • the method of hydrodynamic reductions, • the method of dispersionless Lax pairs, • integrability on solutions, based on the requirement that the characteristic variety of system Σ(X) defines an Einstein-Weyl geometry on every solution, • integrability on equation, meaning integrability (in twistor-theoretic sense) of the canonical GL(2;R) structure induced on a fourfold X ⊂ Gr(3; 5). All these seemingly different approaches lead to one and the same class of integrable systems Σ(X). We prove that the moduli space of such systems is 6-dimensional. We give a complete description of linearisable systems (the corresponding fourfold X is a linear section of Gr(3; 5)) and linearly degenerate systems (the corresponding fourfold X is the image of a quadratic map P4 99K Gr(3; 5)). The fourfolds corresponding to `generic' integrable systems are not algebraic, and can be parametrised by generalised hypergeometric functions.

Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time.

Most man-made environments, such as urban and indoor scenes, consist of a set of parallel and orthogonal planar structures. These structures are approximated by the Manhattan world assumption, in which notion can be represented as a Manhattan frame(MF). Given a set of inputs such as surface normals or vanishing points, we pose an MF estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. Conventionally, this problem can be solved by a branch-and-bound framework, which mathematically guarantees global optimality. However, the computational time of the conventional branch-and-bound algorithms is rather far from real-time. In this paper, we propose a novel bound computation method on an efficient measurement domain for MF estimation, i.e., the extended Gaussian image(EGI). By relaxing the original problem, we can compute the bound with a constant complexity, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for various synthetic and real-world data. We also show the versatility of our approach through three different applications: extension to multiple MF estimation, 3D rotation based video stabilization, and vanishing point estimation(line clustering).

Integrable Systems in Four Dimensions Associated with Six-Folds in Gr(4, 6)

AbstractLet Gr(d, n) be the Grassmannian of d-dimensional linear subspaces of an n-dimensional vector space V. A submanifold X ⊂ Gr(d, n) gives rise to a differential system Σ(X) that governs d-dimensional submanifolds of V whose Gaussian image is contained in X. We investigate a special case of this construction where X is a six-fold in Gr(4, 6). The corresponding system Σ(X) reduces to a pair of first-order PDEs for 2 functions of 4 independent variables. Equations of this type arise in self-dual Ricci-flat geometry. Our main result is a complete description of integrable systems Σ(X). These naturally fall into two subclasses.• Systems of Monge–Ampère type. The corresponding six-folds X are codimension 2 linear sections of the Plücker embedding Gr(4, 6)$ \hookrightarrow \mathbb{P}^{14}$.• General linearly degenerate systems. The corresponding six-folds X are the images of quadratic maps $\mathbb{P}^{6}\dashrightarrow \ $Gr(4, 6) given by a version of the classical construction of Chasles.We prove that integrability is equivalent to the requirement that the characteristic variety of system Σ(X) gives rise to a conformal structure which is self-dual on every solution. In fact, all solutions carry hyper-Hermitian geometry.

Active contour model based on local and global Gaussian fitting energy for medical image segmentation

An improved active contour model is proposed for medical image segmentation in this paper, which integrates the local and global intensity information of the image effectively, with the local fitting term, the active model can attract the contour to stop at the true image edges, the global fitting term is based on the statistical numerical function and level set method. by subtracting the Gaussian convolution image with the original image, the difference images is used to replace the original image in the evolution equation, and the average intensity of the difference image inside and outside the contour is also used to substitute the average intensity of the original image during the level set evolutionary process, the experiments shows the proposed method can have a better segmentation performance with less iterations while dealing with the medical images with intensity inhomogeneity.

Super resolution reconstruction of [formula omitted]-CT image of rock sample using neighbour embedding algorithm

X-ray computed tomography (μ-CT) is considered to be the most effective way to obtain the inner structure of rock sample without destructions. However, its limited resolution hampers its ability to probe sub-micro structures which is critical for flow transportation of rock sample. In this study, we propose an innovative methodology to improve the resolution of μ-CT image using neighbour embedding algorithm where low frequency information is provided by μ-CT image itself while high frequency information is supplemented by high resolution scanning electron microscopy (SEM) image. In order to obtain prior for reconstruction, a large number of image patch pairs contain high- and low- image patches are extracted from the Gaussian image pyramid generated by SEM image. These image patch pairs contain abundant information about tomographic evolution of local porous structures under different resolution spaces. Relying on the assumption of self-similarity of porous structure, this prior information can be used to supervise the reconstruction of high resolution μ-CT image effectively. The experimental results show that the proposed method is able to achieve the state-of-the-art performance.

Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments

Acquiring 3D data with LiDAR systems involves scanning multiple scenes from different points of view. In actual systems, the ICP algorithm (Iterative Closest Point) is commonly used to register the acquired point clouds together to form a unique one. However, this method faces local minima issues and often needs a coarse initial alignment to converge to the optimum. This paper develops a new method for registration adapted to indoor environments and based on structure priors of such scenes. Our method works without odometric data or physical targets. The rotation and translation of the rigid transformation are computed separately, using, respectively, the Gaussian image of the point clouds and a correlation of histograms. To evaluate our algorithm on challenging registration cases, two datasets were acquired and are available for comparison with other methods online. The evaluation of our algorithm on four datasets against six existing methods shows that the proposed method is more robust against sampling and scene complexity. Moreover, the time performances enable a real-time implementation.

Gaussian Pyramid: Comparative Analysis of Hardware Architectures

This paper addresses a comparison of architectures for the hardware implementation of Gaussian image pyramids. Main differences between architectural choices are in the sensor front-end. One side is for architectures consisting of a conventional sensor that delivers digital images and which is followed by digital processors. The other side is for architectures employing a non-conventional sensor with per-pixel embedded preprocessing structures for Gaussian spatial filtering. This later choice belongs to the general category of “artificial retina” sensors which have been for long claimed as potentially advantageous for enhancing throughput and reducing energy consumption of vision systems. These advantages are very important in the internet of things context, where imaging systems are constantly exchanging information. This paper attempts to quantify these potential advantages within a design space in which the degrees of freedom are the number and type of ADCs, single-slope, SAR, cyclic, $\Sigma \Delta$ , and pipeline, and the number of digital processors. Results show that speed and energy advantages of preprocessing sensors are not granted by default and are only realized through proper architectural design. The methodology presented for the comparison between focal-plane and digital approaches is a useful tool for imager design, allowing for the assessment of focal-plane processing advantages.

Optimal build orientation based on material changes for FGM parts

The frequent changing of materials in the functionally gradient material (FGM) part printing process is detrimental to the build time, material, quality, and system stability. Thus, the number of material changes (NMCs) is adopted as a criterion to select the best build orientation. In this paper, the NMCs of a FGM part are formulated as a multiple of a NMC and a slicing function. First, an FGM part is voxelized and discretized into material patterns. Then, the NMC function for each layer is obtained by counting the number of material patterns in a layer. Finally, the voxel model is adaptively sliced with the improved layer depth normal image techniques. This algorithmic procedure can compute the NMCs for an arbitrary orientation of any FGM parts. To find the best orientation, the entire orientation space is converted to an extended Gaussian image (EGI) using a Gaussian map, considering the geometric and material data for a FGM part. To accelerate the searching process, a point clustering algorithm is designed to select the-most-possible candidate orientations to reduce the orientation space to 1/10 ~ 1/20. Five example FGM parts are tested to demonstrate the algorithm’s abilities and efficiency.

ANALISIS RESTORASI CITRA KABUR ALGORITMA WIENER MENGGUNAKAN INDEKS KUALITAS CITRA

Degradation and additional noise in an image will make the quality decreases. Image restoration is needed to restore the image quality to be similar to the original state. Damage to the image can restored include: blurred image, the image with noise spots, dual image, over-saturated color, and the pixel error. To make theblur image is modeled as a convolution between the original image with the point spread function (PSF) which is a point or object spectrum will be spread out so that objects appear to fade. Image restoration is done by passing a blurry image on a filter. In this study discussed Wiener image restoration algorithm using the input image is degraded motion blur and Gaussian blur. Quality image restoration results were analyzed using the image quality index, by comparing the image of the restoration of the original image as a reference. Further image restoration results used as the input image is then processed using Index Image Analysis GUI application. Each of the input image must have a resolution and dimensions that are identical to a reference image. The results showed that by providing opaqueness different models on the same image, the degree of blurring that occurs will be different. Image quality index results for the restoration of degraded image higher than the Gaussian blur image of the restoration of degraded image motion blur. Image quality index results for the restoration of degraded image motion blur ranged from 0.84229 up to 0.87146. Image quality index results for the restoration of degraded Gaussian blur images ranging from 0.86969 up to 0.90025.Keywords: Restoration, blur image, PSF, Wiener algorithm, the image quality index.

Tactile control based on Gaussian images and its application in bi-manual manipulation of deformable objects

The field of in-hand robot manipulation of deformable objects is an open and key issue for the next-coming robots. Developing an adaptable and agile framework for the tasks where a robot grasps and manipulates different kinds of deformable objects, is a main goal in the literature. Many research works have been proposed to control the manipulation tasks using a model of the manipulated object. Despite these techniques are precise to model the deformations, they are time consuming and, using them in real environments is almost impossible because of the large amount of objects which the robot could find. In this paper, we propose a model-independent framework to control the movements of the fingers of the hands while the robot executes manipulation tasks with deformable objects. This technique is based on tactile images which are obtained as a common interface for different tactile sensors, and uses a servo-tactile control to stabilize the grasping points, avoid sliding and adapt the contacts’ configuration regarding to position and magnitude of the applied force. Tactile images are obtained using a combination of dynamic Gaussians, which allows the creation of a common representation for tactile data given by different sensors with different technologies and resolutions. The framework was tested on different manipulation tasks where the objects are deformed, and without using a model of them.

Gaussian mixture image restoration based on maximum correntropy criterion

Using Gaussian mixture model (GMM) as a patch prior has achieved great success in image restoration (IR). However, based on the derivations, the objective function of the conventional GMM-based IR methods can be expressed as the minimum of mean square error (MMSE) criterion based optimisation problem. As outliers may exist in the patch cluster obtained even within a spatially constrained window and MMSE is sensitive to outliers, using the MMSE criterion based strategy would lead to error results. A maximum correntropy criterion (MCC) based IR method with GMM as a patch prior is proposed. The proposed method can automatically identify outliers and assign weight for each patch within the k-nearest-neighbour patches with respect to each exemplar patch in the image and thus can robustly estimate the Gaussian parameters which results in more accurate estimation of the image patches. Finally, an effective iterative optimisation algorithm is designed to solve the proposed objective function under the MCC criterion. The experimental results for image inpainting demonstrate the capabilities of the proposed method.