Search

Contour map has contour lines that are significant in building a digital elevation model (DEM). During the digitization and preprocessing of contour maps, the contour line intersects with each other or breaks apart resulting in broken contour segments. These broken segments impose a greater risk while building DEM leading to a faulty model. The endpoints of the broken segments are matched and reconnected accurately and efficiently using the concept of minimum Euclidean distance and gradient direction. Elevation value is extracted from the map, and a digital elevation model is built in 3D space.

Restoring a damaged image is a challenging topic in the field of image restoration. The famous previous method for restoring a degraded image are filters (inverse and wiener) and maximum a posteriori (MAP) formulation. However, that method has limited performance for restoring damaged images. In this paper, the multi mirroring method have been implemented for reconstructing damaged image which based on gradient direction. Firstly, the method will detect damaged image areas and then the multi mirroring method is implemented for filling a damaged image area. The simulation result shows that the proposed method has good result and capable to restore the damaged image.

The article offers a new method to detect brightness edges. It requires no image pre-smoothing and forms a contour image with minimum number of short lines. It is an analogue and, at the same time, an alternative to the widely known Canny border detection method. It differs from it by the method of calculating partial derivatives and by the choice of thresholds in the automatic mode. The processing time of one frame in the proposed method is 3 times less than in the Canny method.

In order to improve efficient of compressed sensing image reconstruction, an improved gradient projection algorithm of compressed sensing theory is proposed. In improved Gradient Projection algorithm, the pursuit direction is updated by search at negative gradient direction, thus the gradient direction is a single direction, because the traditional gradient projection algorithm searching at alternating searching method,the efficient of gradient projection algorithm is higher than the traditional gradient projection algorithm, Experiment results show that, compared with the GPSR algorithm, the IGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities. (2014) Trans Tech Publications, Switzerland.

In edge detection algorithms, there is a common redundancy problem, especially when the gradient direction is close to -135°, -45°, 45°, and 135°. Double edge effect appears on the edges around these directions. This is caused by the discrete calculation of non-maximum suppression. Many algorithms use edge points as feature for further task such as line extraction, curve detection, matching and recognition. Redundancy is a very important factor of algorithm speed and accuracy. We find that most edge detection algorithms have redundancy of 50% in the worst case and 0% in the best case depending on the edge direction distribution. The common redundancy rate on natural images is approximately between 15% and 20%. Based on Canny’s framework, we propose a restriction in the hysteresis step. Our experiment shows that proposed restricted hysteresis reduce the redundancy successfully. INTRO – INteractive RObotics research network

SUMMARY This paper proposes a method of accurately detecting the boundary of narrow stripes, such as lane markings, by employing gradient cues of edge points. Using gradient direction cues, the edge points at the two sides of the boundary of stripes are classified into two groups before the Hough transform is applied to extract the boundary lines. The experiments show that the proposed method improves significantly the performance in terms of the accuracy of boundary detection of narrow stripes over the con

Extracting axis of 3D blood vessel images is very important and useful to quantify blood vessel in medical diagnosis. According to blood vessel features, we constructed the energy constraint equation of blood vessel. The initial skeleton curve of blood vessel images obtained by thinning algorithm dynastically converges to the position of the axis under energy constraint equation and along gradient direction of the distance field of blood vessel images. When the equation energy reaches a minimum value, the initial skeleton curve also fixes in the axis position at this time. Experimental results show that the position of the blood vessels axis extracted by this method is accurate, and the axis preserves topology and connectivity.

In this paper a face location technology based on gradient distributions is presented. It begins with vertical location by use of vertical integral projection on the two-valued image of the original image, and then proceeds with horizontal location according to the distributions of gradient direction. Experiments have proved this technology fast and efficient.

Face recognition under changing lighting conditions is a challenging problem in computer vision. In this paper, we analyze the relative strengths of different lighting insensitive representations, and propose efficient classifier combination schemes that result in better recognition rates. We consider two experimental settings, wherein we study the performance of different algorithms with (and without) prior information on the different illumination conditions present in the scene. In both settings, we focus on the problem of having just one exemplar per person in the gallery. Based on these observations, we design algorithms for integrating the individual classifiers to capture the significant aspects of each representation. We then illustrate the performance improvement obtained through our classifier combination algorithms on the illumination subset of the PIE dataset, and on the extended Yale-B dataset. Throughout, we consider galleries with both homogenous and heterogeneous lighting conditions.

Quality control is a crucial issue in a float glass factory, and defects existing in float glass can dramatically depress glass grade. Manual inspection in float glass quality control cannot catch up with the development of float glass industry, and automatic glass defect inspection has been a trend. An online defects inspection method for float glass based on machine vision is presented in this paper, and a distributed online defect inspection system for float glass fabrication is realized. This method inspects defects through detecting the change of image gray levels caused by the difference in optic character between glass and defects. A series of image processing algorithms are set up around the analysis of glass image and the requirements of online inspection system such as reliability, real-time, and veracity. Image filtration based on gradient direction is used to filter noise and reserve the source information of defects. Downward threshold based on adaptive surface removes the background composed with stripes and strengthens defect features. Distortion part and core part of defects are obtained through fixed threshold and OTSU algorithms with gray range restricted, respectively. The fake defects (insects, dust, etc.) are eliminated based on the texture of real defects. The application of an inspection system based on this method in Wuhan glass factory proves this inspection method is effective, accurate, and reliable.

Depth image based rendering (DIBR) is one of the key techniques in converting the 2D videos into the stereoscopic 3D ones. Warping process is the major step in DIBR, which generates the virtual left and right views with a number of black disocclusion areas. The disocclusion filling process is the main task in DIBR. In this paper, an improved disocclusion filling approach is proposed, which is based on the texture and gradient analysis. Inpainting algorithm is applied first based on the background texture and depth information to repair the disocclusion areas, which can be effectively reduce the large sizes of the disocclusion areas. Then a new gradient direction template is proposed and applied to fill the left disocclusion areas. Experiments are conducted to compare with other disocclusion filling approaches. Our results are promising.

Robots are now used routinely to perform crystallization experiments and many laboratories now have imaging systems to record the results. These images must be evaluated rapidly and the results fed back into optimization procedures. Software to analyse the images is being developed; described here are methods to restrict the area of the image to be analysed in order to speed up processing. Properties of the gradient of greyscale images are used to identify first the well and then the crystallization drop for various crystallization trays and different imaging systems. Methods are discussed to identify artefacts in the images that are not related to the experimental outcome, but can cause problems for the machine-learning algorithms used in classification and waste time during analysis. Gradient angles are exploited to eliminate faults in the crystallization trays, bubbles and splatter droplets prior to analysis.

Face detection from cluttered images is challenging due to the wide variability of face appearances and the complexity of image backgrounds. This paper proposes a classification-based method for locating frontal faces in cluttered images. To improve the detection performance, we extract gradient direction features from local window images as the input of the underlying two-class classifier. The gradient direction representation provides better discrimination ability than the image intensity, and we show that the combination of gradient directionality and intensity outperforms the gradient feature alone. The underlying classifier is a polynomial neural network (PNN) on a reduced feature subspace learned by principal component analysis (PCA). The incorporation of the residual of subspace projection into the PNN was shown to improve the classification performance. The classifier is trained on samples of face and non-face images to discriminate between the two classes. The superior detection performance of the proposed method is justified in experiments on a large number of images.

In many applications, such as medical diagnosis, correctness of volume rendered images is very important. The most commonly used method for gradient calculation in these volume renderings is the Central Difference Method (CDM), due to its ease of implementation and fast computation. In this paper, artifacts from using CDM for gradient calculation in volume rendering are studied. Gradients are, in general, calculated by CDM with second-order accuracy, \(\mathscr {O}(\varDelta x^2)\). We first introduce a simple technique to find the equations for any desired order of CDM. We then compare the \(\mathscr {O}(\varDelta x^2)\), \(\mathscr {O}(\varDelta x^4)\), and \(\mathscr {O}(\varDelta x^6)\) accuracy versions, using the \(\mathscr {O}(\varDelta x^6)\) version as “ground truth”. Our results show that, unsurprisingly, \(\mathscr {O}(\varDelta x^2)\) has a greater number of errors than \(\mathscr {O}(\varDelta x^4)\), with some of those errors leading to changes in the appearance of images. In addition, we found that, in our implementation, \(\mathscr {O}(\varDelta x^2)\) and \(\mathscr {O}(\varDelta x^4)\) had virtually identical computation time. Finally, we discuss conditions where the higher-order versions may in fact produce less accurate images than the standard \(\mathscr {O}(\varDelta x^2)\). From these results, we provide guidance to software developers on choosing the appropriate CDM, based upon their use case.

We proposed a simple and dynamic wavelet-based algorithm for enhancement of the satellite image sharpness or blurriness of a satellite image. Four set of methods are followed here (Denoising, Decomposing, Sharpness Estimation and Filtering). First Denoising is done on the input images and then it operates by initially decomposing the input image through a multilevel separable DWT. After this, the log-energies of the DWT sub bands are computed. A Scalar Index corresponding to the input image's sharpness is computed through the weighted average of the computed log-energies. Along with the Scalar Sharpness Index, a Block based algorithm is presented to determining the local perceived sharpness. This method is the simplest, fastest and accurate comparing to the currently best-performing techniques for the sharpness estimation. In this Paper A robust quantization-based image watermarking scheme, called the gradient direction watermarking (GDWM) is introduced for satellite enhanced image watermarking. In GDWM, the watermark bits are embedded by quantizing the angles of significant gradient vectors at multiple wavelet scales. To quantize the gradient direction, the DWT coefficients are modified based on the derived relationship between changes in the coefficients and the change in the gradient direction. This Watermarking technique is more robust for various sizes of watermark image. When compared to the existing watermarking techniques the proposed enhanced watermarking technique shows best results.

In this paper, a novel template matching algorithm named radial ring code histograms (RRCH) for multi-objects positioning is proposed. It is invariant to translation, rotation and illumination changes. To improve the identification ability of multi objects with different rotation angles, radial gradient codes using relative angle between gradient direction and position vector is proposed. Adjustable weights in different regions make it possible to adapt various type objects. Experiments using a LED sorting equipment demonstrate that our algorithm results in correct positioning for multi objects in complicated environments with noise and illumination invariance.

AUV localization is not accurate based on sequence images if moving target is as landmark, so the moving target detection algorithm is studied based on global motion estimation, which detects and eliminates moving target according to the motion inconsistency of the moving target. Generally grid block matching is used in the global motion estimation, it can’t effectively dispose the dynamic background, and the gradient direction invariant moments descriptors method of free circular neighborhood based on feature points is proposed, which is effective for the background rotating and light changing in two adjacent images. For the matching points, the parameters of global motion are estimated robustly combined with normalized linear estimation method and least median squares. Experiments show that the designed algorithm can effectively estimate parameters of global motion, and eliminate the motion target as mismatch.

An image mosaic method that can be used when creating advertisements or posters is proposed in this study. Mosaic is a method that expresses an entire image using an arbitrary number of cells. Photomosaic generates new images using a combination of photos. In this paper, we propose a new mosaic algorithm that generates an abstract artistic mosaic image by filling a region that is divided by a boundary using a unit image, which is an image that only has a shape and no allocated color. A unit image can be changed diversely through rotation or shifting, and the corresponding region is filled by using the gradient direction and edge information of the input image. For this, we extract and use information from input image such as color, edge and gradient. In result we can generate various abstractive images which can be used in advertisement and multimedia contents market.

In this paper, we propose a new angle quantization index modulation (AQIM) method, called the difference AQIM (DAQIM) method. The proposed method aims to improve the watermarking performance against gain attacks. Unlike the original AQIM method [1], the DAQIM method quantizes the difference of the two angles instead of the angles themselves. The experimental results demonstrate that our proposed method outperforms both the AQIM and the gradient direction watermarking (GDWM) [2] methods in terms of document-to-watermark ratio (DWR).

Depth images have advantages of simple processing, fog penetration, and little affection by light, thus a body shape detection algorithm based on depth image was proposed to judge personnel evacuation. This study started by making body shape dataset using a depth sensor, then extracting the HOG-depth feature. The best parameters were found, including the range of gradient direction and the number of bins. Next step was to train and classify the body shape dataset using different classifiers, and gentle Adaboost algorithm based on CART weak classifiers got the best result. Then we discussed the effect of traversal method of sliding window, and found a better pixel number of every moving step. At last, the intellectualized control method under actual personnel evacuating situation was completed from the view of software implementation.

Text information in natural scene images is important for various kinds of applications. In this paper a novel method based on stroke width to detect text in unconstrained natural scene images is proposed. Firstly, we use the stroke width transform to generate a rough estimation of stroke width map, then use K-Means clustering and the elbow method to find some specific stroke width values that are both dominant and consistent. Secondly, in order to generate better edge detection and gradient direction results we use these specific stroke width values as the size parameters in the superpixel algorithm to generate smooth and uniform region boundaries. Finally, we try to refine the stroke width map and recover valid edge pixels by applying stroke width regularized constraints on the improved edge detection and gradient direction results computed from these region boundaries. Our method was evaluated on three benchmark datasets: ICDAR 2005, 2011 and 2013, and the experimental results show that it achieves state-of-the-art performance.

In this paper, we present a novel family of multiscale local feature descriptors, a theoretically and intuitively well justified variant of SURF which is straightforward to implement but which nevertheless is capable of demonstrably better performance with comparable computational cost. Our family of descriptors, called Gauge-SURF (G-SURF), is based on second-order multiscale gauge derivatives. While the standard derivatives used to build a SURF descriptor are all relative to a single chosen orientation, gauge derivatives are evaluated relative to the gradient direction at every pixel. Like standard SURF descriptors, G-SURF descriptors are fast to compute due to the use of integral images, but have extra matching robustness due to the extra invariance offered by gauge derivatives. We present extensive experimental image matching results on the Mikolajczyk and Schmid dataset which show the clear advantages of our family of descriptors against first-order local derivatives based descriptors such as: SURF, Modified-SURF (M-SURF) and SIFT, in both standard and upright forms. In addition, we also show experimental results on large-scale 3D Structure from Motion (SfM) and visual categorization applications.

Kertas kerja ini dikemukakan untuk menjelaskan tentang pembetulan pencongan dan erotan imej jawi. Satu algoritma mudah dan pantas ini menggunakan histogram orientasi cerunan sama seperti yang telah dicadangkan oleh Sun dan Si(1997) terhadap teks Latin telah dicadangkan untuk mengatasi masalah pencongan dan erotan terhadap teks Jawi. Algoritma bermula dengan melaksanakan operasi cerunan imej; diikut oleh penentuan histogram orientasi dan licinkannya menggunakan penapis median. Nilai maksimum histogram dicari untuk mendapatkan sudut pencong; diikuti oleh pembetulan awal dengan menggunakan fungsi polinominal. Sudut dihitung secara analitik; dan akhir sekali imej diputarkan mengikut sudut pencong yang diperoleh. Erotan teks Jawi juga telah menggunakan kaedah yang sama untuk mendapatkan sudut erotan dan kemudian diricih supaya dapat menghasilkan imej yang betul. Hasil daripada algoritma ini menjanjikan keputusan yang baik. Kata kunci: Histogram orientasi cerunan; topeng penjejak sisi sobel; pencong; erotan; penapis median; dan sudut pencong This paper discusses an enhancement algorithm of skew and slant orientation of the Jawi images. A simple and quick algorithm that uses a gradient orientation histogram, similar to the one proposed by Sun and Si(1997) for the Latin text is suggested to overcome this skew and slant problem in Jawi text. This algorithm start by carrying out the image gradient operation, followed by determining the orientation histogram and smoothing algorithm with a median filter. The histogram´s maximum value is generated to obtain the skew angle followed by the correcting procedure of the early value obtained by using polynomial function. The angle is calculated analytically and finally converting the image accordingly. The slanted Jawi text uses the same approach as the above to get the slant angle. This image is sheared in order to get the righ image. The outputs of this study show promising and convincing results. Key words: Gradient orientation histogram; the mask of sobel edge detector; skew; slant; median filter; and skew angle

A lane mark detection is used in various driver assist systems for vehicle electronic technology. One of the subjects for the vision system is improvement of robustness. Various methods have been tried to achieve it. In this work, we tried to improve the approach for the robustness. Our main idea is the noise reduction based on narrowing a width of search area. The proposed method uses the road model based on the generalized hough transformation. We use the gradient direction to reduce the number of votes and the method is Kernel-based Hough transform. It is shown that the method can achieve better detection result.

An algorithm based on oriented centroid for image matching is designed. The main contribution lies in providing a stable feature direction of pixels called oriented centroid which takes place of the main gradient direction of the traditional SIFT (Scale Invariant Feature Transform) algorithm. The computation cost required to calculate the oriented centroid based feature direction is much less than that required to estimated the main gradient direction, leading to the considerable improvement of the algorithm's real-time performance. Experimental results verify that the algorithm does not lose performance in terms of robustness and accuracy compared with the standard SIFT algorithm.

Edge detection with sub pixel accuracy is a vital step of vision measurement technology. In this paper, a novel sub-pixel edge detecting algorithm based on orthogonal moment is proposed. The proposed approach locates the edge points with different method for different edges. For the ideal edges, it uses the orthogonal moment based method, and for the blurred edges, the centroid based method is used. Experimental results demonstrate that the proposed method prevails in robustness and high precision compared with traditional method.

Face alignment aims to fit a deformable landmark-based mesh to a facial image so that all facial features can be located accurately. In discriminative face alignment, an alignment score function, which is treated as the appearance model, is learned such that moving along its gradient direction can improve the alignment. This paper proposes a new face model named “Optimal Gradient Pursuit Model”, where the objective is to minimize the angle between the gradient direction and the vector pointing toward the ground-truth shape parameter. We formulate an iterative approach to solve this minimization problem. With extensive experiments in generic face alignment, we show that our model improves the alignment accuracy and speed compared to the state-of-the-art discriminative face alignment approach.

In this paper a fast face location method based on gradient distributions is presented. This method can locate face with certain rotation, or light variation or with glasses or beard in the image. It begins with vertical location by use of vertical integral projection on the two-valued image of the original image, and then proceeds with horizontal location according to the distributions of gradient direction. Experiments have proved this method fast and efficient.

The existing iris location algorithms are of low executing speed and poor robustness. In order to improve the accuracy of iris location, an iris location algorithm based on gradient direction information is proposed according to both the ring structural characteristics of iris and the gray distribution features of eye image in this paper. Experiments shows that the algorithm is effective and can improve the speed and accuracy of iris location.

When roads appear as irregular linear targets, most of traditional methods which use local information often give a false alarm of linear feature. In order to overcome this disadvantage, we present an approach to extract main roads in aerial images with oriented filters. Firstly, the leading gradient direction of the local areas is estimated. Then the image is filtered along this direction. We use steerable filters to control the output and get the maximum output response along the road direction. Based on the steps above, we make much improvement in details such as the determination for the size of the sub-block image, the processing of image edge pixels and the processing of orientation angel areas and so on. Experiments on remote sensing images of different environmental settings show that our proposed scheme achieves high accuracy in the road extraction.

Mammography is the standard examination method for the early detection of breast cancer. In the last decade, computer assisted detection systems have been developed that assist the physician in the detection of suspicious regions in mammograms. However, recent clinical studies indicate that state of the art CAD systems might have a negative impact on the accuracy of screening mammography. Therefore, besides additional clinical studies, better evaluations of state of the art detection approaches are necessary. In this contribution three methods for the detection of spiculated masses in mammograms are evaluated and compared. All three of them are based on gradient orientation images. To detect masses, the methods use circular neighbourhoods with different sizes around a single pixel. The number of orientations in every neighbourhood is used by every method in different ways to form a result. The main contribution is the first fair comparison of the performance of different detection approaches for spiculated masses. Furthermore, a novel gradient direction analysis is introduced. The analysis is an extension to the three approaches, which increases the performance for one of the three approaches.

A novel approach for circle detection based on the Hough transform is presented. It avoids the use of trigonometric functions in the transform equations. Instead of using traditional circle parameterization, which involves gradient direction information, this method uses three edge points to accumulate evidence for circle centers. The method can be extended to the detection of multiple circles where the gradient direction information is only used as a guide to select candidate edge points. A set of rules is proposed for the selection of triple edge points to define circle parameters which can guarantee that the points selected will not be on different circles. The responses for different circles produced in the parameter space using the proposed method are shown to be more compact around the true centers of the circles, thus, removing the need for postprocessing in the parameter space. The application of this method not only reduces the processing time, but also avoids the detection inaccuracy caused by the edge gradient direction errors. >

Although direct volume rendering is a powerful tool for visualizing complex structures within volume data, the size and complexity of the parameter space controlling the rendering process makes generating an informative rendering challenging. In particular, the specification of the transfer function-the mapping from data values to renderable optical properties-is frequently a time consuming and unintuitive task. Ideally, the data being visualized should itself suggest an appropriate transfer function that brings out the features of interest without obscuring them with elements of little importance. We demonstrate that this is possible for a large class of scalar volume data, namely that where the regions of interest are the boundaries between different materials. A transfer function which makes boundaries readily visible can be generated from the relationship between three quantities: the data value and its first and second directional derivatives along the gradient direction. A data structure we term the histogram volume captures the relationship between these quantities throughout the volume in a position independent, computationally efficient fashion. We describe the theoretical importance of the quantities measured by the histogram volume, the implementation issues in its calculation, and a method for semiautomatic transfer function generation through its analysis. We conclude with results of the method on both idealized synthetic data as well as real world datasets.

This work is aimed at the extraction of vascular structures and the formation of high level structural entities, i.e. vascular branches. The proposed approach is based on a string matching technique to associate contour lines. The lines are encoded using the direction of lines and the gradient direction. The measure of similarity between strings is performed using Wagner and Fischer's (1974) algorithm. A first step realizes a two string matching to associate contour lines. It is then extended to three string matching to form triplets (including the centerlines and contour lines).

In this paper, we propose a novel occlusion robust face recognition algorithm in gradient direction domain (GDD) using scattering operator. The proposed algorithm transforms image into the GDD to remove pseudo-edges, then scattering operator is used to extract face feature from face image in GDD. Since scattering operators can effectively extract the structural information in face owing to locally translation invariant and deformation stability, the proposed approach is robust to occlusion and varying expression. Our scheme has demonstrated the state-of-the-art performance on several datasets. Especially, our method on the sunglasses images and the scarf in AR database achieves a recognition rate of 100 and 95% respectively, which significantly outperforms most existing methods.

建物内部のような人工的環境では, ロボットの視覚の対象となるシーンはほとんど直線からなると考えられる.本論文では, ロボットの視覚のための, 濃淡画像から線分を抽出する新しいアルゴリズムを提案する.このアルゴリズムでは, 原画像における対象物の直線状の境界線上でその濃度勾配がほぼ等しいことに着目し, まず, 原画像中の画素のうち, 以下の性質をもつ画素を連結し, 小領域を生成する.(1) その画素の濃度勾配の大きさが与えられたしきい値より大きい.(2) その画素の濃度勾配の方向が与えられた範囲内におさまる.(3) 画素同志が互いに隣接しあう.この小領域は原画像上の対象物の境界線上に細長く生成される.このような小領域に最小二乗法を適用して線分をあてはめることにより線分を抽出することができる.本手法の特徴は以下のとおりである.(1) 原画像に対するただ1回の走査ですべての小領域を生成することができるため, 短時間で画像処理をするのに適当である.(2) 得られた小領域に対して線分をあてはめる際に統計的な計算を用いるため局所的な雑音に強い.(3) 生成された小領域の慣性等価だ円のだ円率によって, その小領域の細長さ, すなわち線分らしさが評価できる.本論文では, 提案した手法により画像を処理した実験例も示し, 本手法が有効であることを実証する.