Corner Detection Algorithm Research Articles

Built-up area detection from high resolution remote sensing images using geometric features

Based on characteristic of High Spatial Resolution(HSR) image, an adaptive local geometric invariant feature extraction method based on Gabor transform and Tensor Voting(TV) is proposed and applied to building area extraction from HSR remote sensing images.First, image geometric features are analyzed. Second, the feasibility of extracting built-up area using local feature points and probability density estimation are determined. This study provides specific methods and steps. First, considering the abundant geometric features of high spatial resolution remote sensing images, multi-scale and multi-direction Gabor filter banks are adopted in detecting the singularity of images contain building area. In order to extract edge information of buildings, only real part Gabor coefficients are used. Meanwhile, we also measured the influence of Gabor kernels of different size on geometric feature extraction experiments, and the optimal scale parameter intervals for geometric feature extraction of remote sensing images with different spatial resolutions are thus determined. Second, it is a fact that the response of Gabor filter at each pixel is a measure for orientation certainty, thus, we introduce the orientation tensor which represents an ideal direction in the direction of the unit vector perpendicular to frequency. By weighting the orientation tensor with Gabor coefficients ans summing over it to complete the information fusion, the resulting tensor givens an estimate for local orientation and orientation uncertainty at image position. The tensors are then used as an initial estimate for global context refinement using tensor voting and the points are classified based on the their likelihoods of being part of a feature type, non-maximal suppression is used to extract geometric features. A key advantage of combining the Gabor filtering and tensor voting is that it eliminates the need for any thresholds therefore removing any data dependencies. In order to achieve a reliable extraction of local invariant feature such as corners from built-up area, three criterions are further proposed to refine the first stage result. Finally, each local building corner indicates a building be detected in image. However, only one of them is not sufficient alone to detecting a building. In fact, the more local points a buildings has, the more probable its detection becomes. Based on that fact, a probability density estimate method is generated to describe the probability that the pixel belongs to the building area, and the Otsu method is used to automatically extract the polygon area of the residential area. Experiments were carried out using image data sets such as Google and GF-2 with resolution higher than 1 meter, results showing that the proposed method can achieve higher accuracy in building area detection compared with the state-of-the-art corner detection algorithm such as Harris corner and High-speed corner detect method.

用于聚焦型光场相机标定的棋盘角点检测算法

用于聚焦型光场相机标定的棋盘角点检测算法

Offline Signature Recognition and Forgery Detection using Deep Learning

Abstract Authentication plays a very important role to manage security. In the modern era, it is one, in all the priorities. With the appearance of technology, the interaction with machines is turning automatic. Therefore, the need of authentication increases rapidly for various security purposes. Because of this, the biometric-based authentication has gained a drastic momentum. It is a kind of boon over other techniques. However, this event is not a replacement of drawback but varied ways are adopted to verify folks. Signature is one of the first broadly practiced biometric features for the verification of an individual. This paper proposes a method for the pre-processing of signatures to make verification simple. It also proposed a novel method for signature recognition and signature forgery detection with verification using Convolution Neural Network (CNN), Crest-Trough method and SURF algorithm & Harris corner detection algorithm. The proposed system attains an accuracy of 85-89% for forgery detection and 90-94% for signature recognition.

Symbol recognition system based on 3D stereo vision

With the continuous development of computer science and technology, symbol recognition systems may be converted from two-dimensional space to three-dimensional space. Therefore, this article mainly introduces the symbol recognition system based on 3D stereo vision. The three-dimensional image is taken by the visual coordinate measuring machine in two places on the left and right. Perform binocular stereo matching on the edge of the feature points of the two images. A corner detection algorithm combining SUSAN and Harris is used to detect the left and right camera calibration templates. The two-dimensional coordinate points of the object are determined by the image stereo matching module, and the three-dimensional discrete coordinate points of the object space can be obtained according to the transformation relationship between the image coordinates and the actual object coordinates. Then draw the three-dimensional model of the object through the three-dimensional drawing software. Experimental data shows that the logic resources and memory resources occupied by image preprocessing account for 30.4% and 27.4% of the entire system, respectively. The results show that the system can calibrate the internal and external parameters of the camera. In this way, the camera calibration result will be more accurate and the range will be wider. At the same time, it can effectively make up for the shortcomings of traditional modeling techniques to ensure the measurement accuracy of the detection system.

Robust Corner Finding Based on Multi-Scale K-Cosine Angle Detection

Corner is widely utilized in computer vision and image processing. As a representative contour-based corner detection algorithm, RJ detector is first proposed to use the K-cosine to estimate curvature of digital curves for corner finding. However, such influential approach is quite sensitive to the geometric transformations and noise due to its dynamic smoothing scale. To overcome this drawback and enhance its performance further, this paper presents a multi-scale version of RJ detector. First, we adopt fixed region of radius (RoS) to avoid its sensitiveness to geometric transformations; second, the technique of scale product is employed to enhance curvature extreme peaks and suppress noise for improving localization. Extensive experiments on several corner detection datasets are conducted for evaluating its performances. And the experimental results demonstrate that such simple idea endows RJ an incredible improvement and MSRJ achieves the competitive performance compared with state-of-the-arts corner detectors under measure metrics of average repeatability and localization error.

An Improvement to a Class of Intensity based Spatial Domain Corner Detection Algorithms using Image Fission and Fusion

Corners of an object are important as features for the representation and analysis of its shape in computer vision. Corner detection, particularly in real scenes, is still a challenge. Most of the corner detectors found in the literature generate a number of false corners, which is not acceptable in real-life applications. In this paper, an improvement to a class of corner detection algorithms is presented using image fission/fusion. In this approach, a grayscale image is first divided into several bit-planes. A corner detector is applied on all the bit-planes simultaneously and a threshold (bitplane) is obtained using the concept of information gain. Finally, all the higher bit-plane corners are recombined (up to some thresholded bit-plane) to obtain the final set of corners. Here the corner detection algorithm is considered as a binary classification problem. Experimental results show that this improved approach reduces the number of erroneous corner detection relative to existing spatial domain corner detection algorithms. The improvements are established with the help of a number of performance measures proposed by various researchers. The proposed approach works better with respect to computational time also. This approach can easily be utilized in different low-level image processing applications.

Overlapping citrus segmentation and reconstruction based on Mask R-CNN model and concave region simplification and distance analysis

Accurate segmentation and reconstruction of overlapping citrus target contours is the primary problem for picking robots. In view of the poor effect of existing research methods on the segmentation and reconstruction of overlapping citrus fruit target contours under complex background, a segmentation and reconstruction method based on region simplification and distance analysis is proposed. Firstly, the overlapping citrus region (Mask region) is obtained by using the previously trained Mask R-CNN model. Then, the convex hull curve of the region is obtained by the roll-wrapped convex shell algorithm, and the region enclosed by the convex curve and the Mask region are pixel-operated. The concave region is polygon-simplified; then the vertices of the polygon are extracted by the Shi-Tomasi corner detection algorithm, and the contour segmentation points are determined by analysing the distance from each vertex to the contour convex-hub curve. Finally, the segmentation contour is reconstructed by the least squares fitting method. The experimental results show that the average error of the proposed method for the reconstruction of overlapping citrus contours is 3.21%, the non-coincidence degree and time are 4.13% and 0.273s respectively, which superior to RANSAC algorithm and Hough transform algorithm. It can satisfy the recognition requirements of overlapping citrus in natural environment for citrus picking robots.

Mapping Detection of Marine Data Based on Space Remote Sensing Technology

Chen, Y.; Ma, Q.; Liu, C., and Shu, Q., 2019. Mapping detection of marine data based on space remote sensing technology. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 717–722. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the ability of marine data cartographic detection and map reconstruction, a method of marine data cartographic detection based on spatial remote sensing technology is proposed. A cartographic detection technology of marine data based on space remote sensing technology is proposed. Spatial remote sensing technology is used to remove the non-significant interference of ocean surface information in the color spots of ocean currents and monsoon. The square grid is divided into square grid in the space of marine data cartographic detection feature distribution, the remote sensing information features of marine data mapping are calculated, and the random logarithmic fluctuation error vector is selected to obtain the regular size fluctuation integer vector of marine data cartography. The spatial remote sensing image segmentation method based on topological theory calculates the regular size information of marine data cartography, and preserves the stability of Harris corner detection algorithm. The algorithm combines the scale invariance of SURF algorithm and improves the accuracy and efficiency of feature matching in cartographic detection of marine data. The simulation results show that the marine data cartographic detection technology based on spatial remote sensing technology has strong anti-interference performance, accurate calibration of marine data map feature points, less repetition points, good robustness to scale, wave fluctuation and noise, and improves the accuracy and rate of ocean data mapping detection.

Automatic large quantity landmark pairs detection in 4DCT lung images.

To automatically and precisely detect a large quantity of landmark pairs between two lung computed tomography (CT) images to support evaluation of deformable image registration (DIR). We expect that the generated landmark pairs will significantly augment the current lung CT benchmark datasets in both quantity and positional accuracy. A large number of landmark pairs were detected within the lung between the end-exhalation (EE) and end-inhalation (EI) phases of the lung four-dimensional computed tomography (4DCT) datasets. Thousands of landmarks were detected by applying the Harris-Stephens corner detection algorithm on the probability maps of the lung vasculature tree. A parametric image registration method (pTVreg) was used to establish initial landmark correspondence by registering the images at EE and EI phases. A multi-stream pseudo-siamese (MSPS) network was then developed to further improve the landmark pair positional accuracy by directly predicting three-dimensional (3D) shifts to optimally align the landmarks in EE to their counterparts in EI. Positional accuracies of the detected landmark pairs were evaluated using both digital phantoms and publicly available landmark pairs. Dense sets of landmark pairs were detected for 10 4DCT lung datasets, with an average of 1886 landmark pairs per case. The mean and standard deviation of target registration error (TRE) were 0.47±0.45mm with 98% of landmark pairs having a TRE smaller than 2mm for 10 digital phantom cases. Tests using 300 manually labeled landmark pairs in 10 lung 4DCT benchmark datasets (DIRLAB) produced TRE results of 0.73±0.53mm with 97% of landmark pairs having a TRE smaller than 2mm. A new method was developed to automatically and precisely detect a large quantity of landmark pairs between lung CT image pairs. The detected landmark pairs could be used as benchmark datasets for more accurate and informative quantitative evaluation of DIR algorithms.

Implementation of Modified Harris Corner Detector Algorithm -Including Free Parameters Based on FPGA

An efficient, pipelined Field Programmable Gate Arrays (FPGA) engineering of a modified Harris corner Detector is proposed. In laptop imaginative and prescient, the Harris nook encompass locator is one of the most fundamental strides in numerous precious applications, as an instance, three-D replica. in any case, inside the occasion that it's miles actualized in programming, the following code is not affordable to be achieved continuously by using minimum attempt versatile processors. device technique has been acquired for offloading the monotonous element extraction method into reason entryways with the purpose that the association is having minimal attempt to supply and low capability to paintings contrasted with its product accomplice. The framework is fabricated and attempted on a field programmable Gate Arrays(FPGA) level (Zed board). The assessments and demos exhibit that the speed and precision of the component indicator are enough for some proper applications. The results reveal an ideal concord between belongings utilization and timing execution, contrasted and previous.

Corner detection based on gradient ghost imaging

Gradient ghost imaging (GGI) is a new imaging method that can directly extract the edge of a target from the correlation of light intensity fluctuations. However, there are problems of poor image quality and practicability in GGI. The corner point is an important target feature and has important applications in the fields of image processing and machine vision. In this paper, we propose a corner detection scheme that combines a corner detection algorithm based on curvature scale space with a GGI system, which can directly extract corner points from the reconstruction results of ghost imaging. A simulation and experimental results show that our method can acquire precise and robust corner information of an unknown target in undersampled cases, which promotes the practical development of ghost imaging.

CCDA: a concise corner detection algorithm

In this article, the authors propose a concise corner detection algorithm, which is called CCDA. A cascade classifier concept is used to derive a corner detector, which can quickly discard the most non-corner pixels. The ruler of gradient direction is used to get the corner, which can avoid the influence of the light change. The method of second derivative non-maximum suppression is used to get the location of the corner and can get the exact corner point. As a result, CCDA is compare-tested with classical corner detection algorithms by using the same images which include synthetic corner patterns and real images. The result shows that CCDA has a similar speed to the FAST algorithm and better accuracy and robustness than the HARRIS algorithm.

Parameter Measurement of Biaxial Braided Composite Preform Based on Phase Congruency

Abstract In this paper, a new method based on phase congruency is proposed to measure pitch lengths and surface braiding angles of two-dimensional biaxial braided composite preforms. Lab space transform and BM3D (block-matching and 3D filter) are used first to preprocess the original acquired images. A corner detection algorithm based on phase congruency is then proposed to detect the corners of the preprocessed images. Pitch lengths and surface braiding angles are finally measured based on the detected corner maps. Experimental results show that our method achieves the automatic measurement of pitch lengths and the surface braiding angles of biaxial braided composite preforms with high accuracy.

Method for detecting the notch deviation based on corner point constraint

The notch detection plays a critical role in influencing the quality control of cut-piece. It is difficult to measure the deviation of notch due to the deformation of cut-piece, which in turn affects the subsequent sewing process and the quality of cut-piece. In this paper, a notch detection method based on corner point constraint is proposed to solve this problem. Firstly, all the corner points are extracted from the CAD by using the improved Harris corner detection algorithm. The triangle constraint and the intersection constraint method are used to eliminate extra corner points that are not in the triangle area, and then the notch is obtained. Secondly, the cut-piece and the CAD are superimposed by affine transformation, and the approximate notch area on the cut-piece is obtained in the neighbourhood of the notch on the CAD. Finally, the improved Harris corner detection algorithm is used to extract all the corner points of the notch area on the cut-piece, and then the notch is extracted after removing the extra corner points. The experimental results have shown a good consistency with the expected performance which confirms the effectiveness of the proposed method.

Multi-Threshold Corner Detection and Region Matching Algorithm Based on Texture Classification

In order to address the unreasonable distributed corners in single threshold Harris detection and expensive computation cost incurred from image region matching performed by normalized cross correlation (NCC) algorithm, multi-threshold corner detection and region matching algorithm based on texture classification are proposed. Firstly, the input image is split into sub-blocks which are classified into four different categories based on the specific texture: flat, weak, middle texture and strong regions. Subsequently, an algorithm is suggested to decide threshold values for different texture type, and interval calculation for the sub-blocks is performed to improve operation efficiency in the algorithm implementation. Finally, based on different texture characteristics, Census, interval-sampled NCC, and complete NCC are employed to perform image matching. As demonstrated by the experimental results, corner detection based on texture classification is capable to obtain a reasonable corner number as well as a more uniform spatial distribution, when compared to the traditional Harris algorithm. If combined with the interval classification, speedup for texture classification is approximately 30%. In addition, the matching algorithm based on texture classification is capable to improve the speed of 26.9%~29.9% while maintaining the comparable accuracy of NCC. In general, for better splicing quality, the overall stitching speed is increased by 14.1%~18.4%. Alternatively, for faster speed consideration, the weak texture region which accounts for a large proportion of an image and provides less effective information can be ignored, for which 23.9%~28.4% speedup can be achieved at the cost of a 1.9%~3.9% reduction in corner points. Therefore, the proposed algorithm is made potentially suited to uniformly distributed corner point calculation and high computation efficiency requirement scenarios.

Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background

The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.

5 Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background

The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.

Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background

The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.

Multilevel Wavelet Decomposition Based Harris Corner Detection Algorithm for Remote-sensing Image

Because of the interference of the noise and the subtle texture, the Harris algorithm will generate much more false corners. An improved Harris algorithm based on multilevel wavelet decomposition is proposed. By multilevel wavelet decomposition, the high frequency components of image are extracted. These high frequency components with different scales are parsed, and the noises and subtle textures are suppressed while the original image edge features are kept. The final experiment shows that the improved corner detection algorithm can effectively reduce the number of false corners.

Threshold-Guided Design and Optimization for Harris Corner Detector Architecture

High-speed corner detection is an essential step in many real-time computer vision applications, e.g., object recognition, motion analysis, and stereo matching. Hardware implementation of corner detection algorithms, such as the Harris corner detector (HCD) has become a viable solution for meeting real-time requirements of the applications. A major challenge lies in the design of power, energy and area efficient architectures that can be deployed in tightly constrained embedded systems while still meeting real-time requirements. In this paper, we proposed a bit-width optimization strategy for designing hardware-efficient HCD that exploits the thresholding step in the algorithm to determine interest points from the corner responses. The proposed strategy relies on the threshold as a guide to truncate the bit-widths of the operators at various stages of the HCD pipeline with only marginal loss of accuracy. Synthesis results based on 65-nm CMOS technology show that the proposed strategy leads to power-delay reduction of 35.2%, and area reduction of 35.4% over the baseline implementation. In addition, through careful retiming, the proposed implementation achieves over 2.2 times increase in maximum frequency while achieving an area reduction of 35.1% and power-delay reduction of 35.7% over the baseline implementation. Finally, we performed repeatability tests to show that the optimized HCD architecture achieves comparable accuracy with the baseline implementation (average decrease of repeatability is less than 0.6%).