Harris Corner Detection Algorithm Research Articles

Real Time FPGA Implementation of an Efficient High Speed Harris Corner Detection Algorithm Based on High-Level Synthesis

Computer vision systems use corner detection to identify features in an image. In applications such as motion detection, tracking, picture registration, and object recognition, corner detection is often one of the initial steps. In this paper, a real-time image processing system based on Harris corner detection was designed and implemented using Zynq architecture and model-based design tools. The system was based on a development board containing the Zynq-7000 chip, which consists of a combination of FPGA and microprocessor, and the image taken with a high-resolution camera was processed in real-time by applying color conversion and Harris corner detection. The filter hardware designs used in the system were made using the HDL Coder tool in Matlab/Simulink without writing HDL code. The hardware that receives images from the camera was designed on a model-based basis with the Xilinx Vivado 2020. The HDL code that was implemented on the Xilinx ZedBoard using Vivado software was then validated to ensure real-time operation with the incoming video stream. The results achieved exhibited superiority compared to prior implementations in terms of area efficiency (reduced number of gates on the target FPGA) and speed performance on an identical target card. Using the rapid prototyping approach, two alternative hardware accelerator designs were created using various high-level synthesis tools. This design used less than 50% of the host FPGA's logic resources and was at least 30% faster than current implementations.

Research of different feature detection and matching algorithms on panoramic image

Image stitching is the process of combining numerous photos to make a panorama. The technique of image stitching has rapidly advanced and grown to be a significant area of digital image processing. Many image stitching methods have been proposed and studied in prior study. In this paper, the image stitching process is implemented using different algorithms. For keypoints detection, the algorithms of Harris corner detection, SIFT(Scale-Invariant Feature Transform), SURF(Speeded Up Robust Feature) and ORB(Oriented FAST and Rotated BRIEF) algorithms are applied, then use different methods (i.e., Brute Force, etc.) for feature matching. The RANSAC(Random Sample Consensus) method is used to calculate a homography matrix from matched feature vectors and use it to warp the images. Image blending and cropping methods are proposed to enhance the image quality. Given groups of the self-captured images, experiments have been down to shown the performance of different techniques.

A Deep Learning-Enhanced Stereo Matching Method and Its Application to Bin Picking Problems Involving Tiny Cubic Workpieces

This paper proposes a stereo matching method enhanced by object detection and instance segmentation results obtained through the use of a deep convolutional neural network. Then, this method is applied to generate a picking plan to solve bin picking problems, that is, to automatically pick up objects with random poses in a stack using a robotic arm. The system configuration and bin picking process flow are suggested using the proposed method, and it is applied to bin picking problems, especially those involving tiny cubic workpieces. The picking plan is generated by applying the Harris corner detection algorithm to the point cloud in the generated three-dimensional map. In the experiments, two kinds of stacks consisting of cubic workpieces with an edge length of 10 mm or 5 mm are tested for bin picking. In the first bin picking problem, all workpieces are successfully picked up, whereas in the second, the depths of the workpieces are obtained, but the instance segmentation process is not completed. In future work, not only cubic workpieces but also other arbitrarily shaped workpieces should be recognized in various types of bin picking problems.

Color Image Steganography Using Gradient Selective Bezier Curves

Internet technology has revolutionized the landscape of communication technologies in the modern era. However, because the internet is open to the public, communication security cannot be guaranteed. As a result, data concealment approaches have been developed to ensure confidential information sharing. Various methods have emerged to achieve the goal of secure data communication via multimedia documents. This study proposes a method, which is both adaptable and imperceptible, for concealing a secret text in a color image. From an adaptivity perspective, image corners are detected using the Harris corner detection algorithm and utilized as anchor points for picking the optimal hiding regions of interest using Bezier curve interpolation. On the other hand, because human vision is less sensitive to aberrations in edge regions, imperceptibility is guaranteed by utilizing curves that cross through these regions. Experiments indicate that utilizing gradient selective Bezier curves for secret text concealment can keep the imperceptibility even when the payload capacity is increased.

Determining the size of the overlapping area for image stitching in dark-field detection

Dark-field microscopic imaging system has high spatial resolution, but small image field. When an optical surface with a large-diameter is tested, the image stitching is necessary to obtain the full-aperture detection results. In this paper, Harris corner detection algorithm is used to achieve the full aperture testing result by stitching multiple detection images. According to the Shannon sampling theorem, how the size of the overlapping area influences the stitching results is analysed in detail. Combined with the spatial scale of surface defects, a method for determining the size of the overlapping area is given. The standard scratch patterns are used to simulate the stitching process. On this basis, an actual stitching processing is carried out on the detection images of surface scratches of four different spatial scales. Both the simulation and experimental results show that it is reasonable to use the scale of the smallest defect to determine the number of sampling points in the stitching area.

Global Reconstruction Method of Maize Population at Seedling Stage Based on Kinect Sensor

Automatic plant phenotype measurement technology based on the rapid and accurate reconstruction of maize structures at the seedling stage is essential for the early variety selection, cultivation, and scientific management of maize. Manual measurement is time-consuming, laborious, and error-prone. The lack of mobility of large equipment in the field make the high-throughput detection of maize plant phenotypes challenging. Therefore, a global 3D reconstruction algorithm was proposed for the high-throughput detection of maize phenotypic traits. First, a self-propelled mobile platform was used to automatically collect three-dimensional point clouds of maize seedling populations from multiple measurement points and perspectives. Second, the Harris corner detection algorithm and singular value decomposition (SVD) were used for the pre-calibration single measurement point multi-view alignment matrix. Finally, the multi-view registration algorithm and iterative nearest point algorithm (ICP) were used for the global 3D reconstruction of the maize seedling population. The results showed that the R2 of the plant height and maximum width measured by the global 3D reconstruction of the seedling maize population were 0.98 and 0.99 with RMSE of 1.39 cm and 1.45 cm and mean absolute percentage errors (MAPEs) of 1.92% and 2.29%, respectively. For the standard sphere, the percentage of the Hausdorff distance set of reconstruction point clouds less than 0.5 cm was 55.26%, and the percentage was 76.88% for those less than 0.8 cm. The method proposed in this study provides a reference for the global reconstruction and phenotypic measurement of crop populations at the seedling stage, which aids in the early management of maize with precision and intelligence.

Corner Detection of the Computer VR Microscope Image Based on the 3D Reconstruction Algorithm.

In order to solve the problem of multisolution and ill-formedness of the 3D reconstruction method of a single image (purpose), the author proposes a microscope image segmentation algorithm based on the Harris multiscale corner detection. Separating complex engineering images into several simple basic geometric shapes, rebuild them separately to avoid the ill-conditioned solution problem of directly recovering depth information. In order to improve the registration accuracy of the corner-based image registration algorithm, the idea of multiresolution analysis was introduced into the classic Harris corner detection, and a gray intensity variation formula based on the wavelet transform was constructed, and a scale transformation characteristic was obtained so that the improved Harris corner detection algorithm is invariant to rotation, translation, and scale. Experimental results show that after reconstruction, the error between the length of the object measured based on the point cloud data and the actual length of the object is small, and both remain within the error range of 3 mm. The experiment verifies the fast, accurate, and stable characteristics of the improved algorithm.

Robust and Secure Zero-Watermarking Algorithm for Medical Images Based on Harris-SURF-DCT and Chaotic Map

To protect the patient information in medical images, this article proposes a robust watermarking algorithm for medical images based on Harris-SURF-DCT. First, the corners of the medical image are extracted using the Harris corner detection algorithm, and then, the previously extracted corners are described using the method of describing feature points in the SURF algorithm to generate the feature descriptor matrix. Then, the feature descriptor matrix is processed through the perceptual hash algorithm to obtain the feature vector of the medical image, which is a binary feature vector with a size of 32 bits. Secondly, to enhance the security of the watermark information, the logistic map algorithm is used to encrypt the watermark before embedding the watermark. Finally, with the help of cryptography knowledge, third party, and zero-watermarking technology, the algorithm can embed the watermark without modifying the medical image. When extracting the watermark, the algorithm can extract the watermark from the test image without the original image. In addition, the algorithm has strong robustness to conventional attacks and geometric attacks. Especially under geometric attacks, the algorithm performs better.

Improved Harris Corner Detection Algorithm Based on Canny Edge Detection and Gray Difference Preprocessing

Harris corner detection algorithm has been widely used in many computer vision allocations. However, it has low efficiency and accuracy, poor noise immunity and needs to set an artificial threshold. In this paper, an improved algorithm based on Canny edge detection and gray difference preprocessing is proposed. Firstly, Canny edge detection and gray difference preprocessing are used for corner prescreening to improve the detection efficiency, anti-noise, and rotation invariance. Secondly, non-maximum suppression is applied to the screened corners to reduce the number of false corners. Finally, the average of adjacent points method is used to solve the problem of corner cluster, and the detection results are compared the measurement accuracy is improved to sub-pixel level. Experimental results indicate that the proposed algorithm can accurately extract the corners in the image and remove the false corners and corner clusters. It achieves superior performance than existing methods.

Flame Detection Based on Deep Learning Using HSV Color Model and Corner Detection Algorithm

Recently, many image classification or object detection models that use deep learning techniques have been studied; however, in an actual performance evaluation, flame detection using these models may achieve low accuracy. Therefore, the flame detection method proposed in this study is image pre-processing with HSV color model conversion and the Harris corner detection algorithm. The application of the Harris corner detection method, which filters the output from the HSV color model, allows the corners to be detected around the flame owing to the rough texture characteristics of the flame image. These characteristics allow for the detection of a region of interest where multiple corners occur, and finally classify the flame status using deep learning-based convolutional neural network models. The flame detection of the proposed model in this study showed an accuracy of 97.5% and a precision of 97%.

Real time FPGA implementation of a high speed and area optimized Harris corner detection algorithm

Harris corner detection is an algorithm frequently used in image processing and computer vision applications to detect corners in an input image. In most modern applications of image processing, there is a need for real time implementation of algorithms such as Harris corner detection in hardware systems such as field-programmable gate arrays (FPGAs). FPGAs allow faster algorithmic throughput, which is required to match real time speeds or cases where there is a requirement to process faster data rates. High level synthesis tools offer higher abstraction level to designers with continued verification during the design flow and hence are getting popular with the design community. This paper proposes a high speed and area optimized implementation of a Harris corner detection algorithm. The proposed implementation was actualized using a novel high-level synthesis (HLS) design method based on application-specific bit widths for intermediate data nodes. Register transfer level (RTL) code was generated using MATLAB HDL coder for HLS. The generated hardware description language (HDL) code was implemented on Xilinx ZedBoard using Vivado software and verified for functionality in real time with input video stream. The obtained results are superior to those of previous implementations in terms of area(smaller gate count on target FPGA) and speed for the same target board.

Handwritten digit recognition based on corner detection and convolutional neural network

In the field of inspection and testing, it is necessary to manually fill a large number of test record forms, and the digital information accounts for a large proportion in the record forms. For the purpose of automatic and accurate recognition of handwritten form data, this paper proposes a handwritten digit recognition method based on Harris corner detection algorithm and convolutional neural network(CNN). The Harris corner detection algorithm is used to identify the positioning marks in the form image, which determine the possible fill-in positions of handwritten digits. Through a 14-layer CNN model, and using the ReLu function as the excitation function, the handwritten digit features of the target area in the image are recognized. The experimental results show that the average recognition accuracy rate of this method on the test set can reach 98.14%. So a technical solution for intelligent recognition of record forms is thus provided.

Stepwise local stitching ultrasound image algorithms based on adaptive iterative threshold Harris corner features.

Herein, a Harris corner detection algorithm is proposed based on the concepts of iterated threshold segmentation and adaptive iterative threshold (AIT–Harris), and a stepwise local stitching algorithm is used to obtain wide-field ultrasound (US) images.Cone-beam computer tomography (CBCT) and US images from 9 cervical cancer patients and 1 prostate cancer patient were examined. In the experiment, corner features were extracted based on the AIT–Harris, Harris, and Morave algorithms. Accordingly, wide-field ultrasonic images were obtained based on the extracted features after local stitching, and the corner matching rates of all tested algorithms were compared. The accuracies of the drawn contours of organs at risk (OARs) were compared based on the stitched ultrasonic images and CBCT.The corner matching rate of the Morave algorithm was compared with those obtained by the Harris and AIT–Harris algorithms, and paired sample t tests were conducted (t = 6.142, t = 31.859, P < .05). The results showed that the differences were statistically significant. The average Dice similarity coefficient between the automatically delineated bladder region based on wide-field US images and the manually delineated bladder region based on ground truth CBCT images was 0.924, and the average Jaccard coefficient was 0.894.The proposed algorithm improved the accuracy of corner detection, and the stitched wide-field US image could modify the delineation range of OARs in the pelvic cavity.

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.

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.

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.

Analysis of the ship target detection in high-resolution SAR images based on information theory and Harris corner detection

In order to make up the shortcomings of some existing ship target detection algorithms for high-resolution synthetic aperture radar (SAR) images, a ship target detection algorithm based on information theory and Harris corner detection for SAR images is proposed in this paper. Firstly, the SAR image is pretreated, and next, it is divided into superpixel patches by using the improved simple linear iterative clustering (SLIC) superpixel generation algorithm. Then, the self-information value of the superpixel patches is calculated, and the threshold T1 is set to select the candidate superpixel patches. And then, the extended neighborhood weighted information entropy growth rate threshold T2 is set to eliminate the false alarm candidate superpixel patches. Finally, the Harris corner detection algorithm is used to process the detection result and the number of the corner threshold T3 is set to filter out the false alarm patches, and the final SAR image target detection result is obtained. The effectiveness and superiority of the proposed algorithm are verified by comparing the proposed method with the results of constant false alarm rate (CFAR) detection algorithm combined with morphological processing algorithm and other ship target detection algorithms.

Harris Corner Detection Algorithm Optimization Based on OTSU

Harris Corner Detection Algorithm Optimization Based on OTSU

Harris Köşe Bulma Algoritmasının Hacimsel Görüntüler için Uygulanması

More effective detection of corner points in three dimensional (3-D) volumetric images can be possible through expansion of Harris corner detection algorithm, which run in two dimensional (2-D) images, into third dimension. In this study, the standard algorithm of Harris that detected corner points in 2-D slices and its 3-D version were implemented in the scale-space to determine the corner points of volumetric object images. The results obtained in sample object images with 2-D and 3-D methods that used different approaches for scale-space construction were qualitatively assessed.

Detection of honeycomb cell walls from measurement data based on Harris corner detection algorithm

A honeycomb core is a discontinuous material with a thin-wall structure—a characteristic that makes accurate surface measurement difficult. This paper presents a cell wall detection method based on the Harris corner detection algorithm using laser measurement data. The vertexes of honeycomb cores are recognized with two different methods: one method is the reduction of data density, and the other is the optimization of the threshold of the Harris corner detection algorithm. Each cell wall is then identified in accordance with the neighboring relationships of its vertexes. Experiments were carried out for different types and surface shapes of honeycomb cores, where the proposed method was proved effective in dealing with noise due to burrs and/or deformation of cell walls.