Harris Corner Algorithm Research Articles

Comparative Analysis of Image Stitching Algorithms for Bridge Inspection Imaging Systems

This paper discusses an innovative vehicle-mounted device designed for efficient inspection of bridge undersides, aiming to address the challenges of manual inspections, such as being labor-intensive and inefficient. The device uses high-precision cameras to capture detailed images of bridge bottoms, facilitating a thorough condition assessment. It evaluates three feature point extraction algorithms for image stitching—Scale-Invariant Feature Transform (SIFT), Harris corner detection, and Speeded Up Robust Features (SURF)—to create complete visual representations of bridge undersides. Field tests on a prefabricated I girder bridge demonstrated the device's effectiveness in gathering and stitching images, with the SIFT algorithm performing best for girder bottoms, Harris corner algorithm for web and flange surfaces, and SURF for rapid image processing. This research provides a foundation for the rapid identification of visible defects on bridge superstructures, significantly benefiting bridge maintenance and safety evaluations.

Dynamic 3D phase-shifting profilometry based ona corner optical flow algorithm.

Real-time 3D reconstruction has been applied in many fields, calling for many ongoing efforts to improve the speed and accuracy of the used algorithms. Phase shifting profilometry based on the Lucas-Kanade optical flow method is a fast and highly precise method to construct and display the three-dimensional shape of objects. However, in this method, a dense optical flow calculation is required for the modulation image corresponding to the acquired deformed fringe pattern, which consumes a lot of time and affects the real-time performance of 3D reconstruction and display. Therefore, this paper proposes a dynamic 3D phase shifting profilometry based on a corner optical flow algorithm to mitigate this issue. Therein, the Harris corner algorithm is utilized to locate the feature points of the measured object, so that the optical flow needs to calculate for only the feature points which, greatly reduces the amount of calculation time. Both our experiments and simulations show that our method improves the efficiency of pixel matching by four times and 3D reconstruction by two times.

Network Communication Platform Based on Artificial Intelligence

In order to improve the integration of digital prints, this paper proposes a new method of printmaking analog synthesis, that is, using interactive technology.. Firstly, Harris corner algorithm is used to collect and preprocess the adjacent feature points of digital printmaking image, and the texture features of digital printmaking image are extracted, so as to construct the texture information transmission model of digital printmaking image; Secondly, with the help of segmentation technology to process the digital print image, the dynamic feature segmentation is carried out, the local binary fitting method is used to enhance and repair the digital print image information, and the information fusion method based on interactive technology is used to complete the analog synthesis of digital print image; Finally, the simulation results show that the method has good performance of simulation synthesis and strong information fusion ability.

Bone fractures detection using support vector machine and error backpropagation neural network

Machine Learning (ML) methodologies have become a worthy choice for X-ray screening. X-ray imaging is a method used for the identification of bone fractures. But every so often, the dimensions and locations of a fracture are detected incorrectly. Therefore, this paper aims to design a system that would accurately detect and classify fractured and non-fractured bone images. This proposed system comprises four principal phases. The first is the image acquisition phase, in which few input images are acquired from the X-ray machine, and few input images are collected from the imaging centre. The second is a pre-processing phase to see their informative areas like location, edges and shape. So, the wavelet transform method helps to compress the images to store and remove noises. The third is the feature extracting phase; the Harris corner detection technique is used to locate the broken points as corner features and intensifies the quality of the X-ray images. Before applying the Harris corner algorithm, the image sharpening method was used. Then images are equipped to be fed into a fourth phase, i.e. classification phase; two techniques are used in this phase: Support Vector Machine (SVM) and Error Backpropagation Neural Network (EBP-NN). Classification by SVM and EBP-NN both methods are tested on various fractured and non-fractured bone images. Finally, found that classification done by the SVM technique is more accurate than EBP-NN. The classification by the SVM approach has higher efficiency and a higher classification rate of 94%, which is 2% more than the EBP-NN technique.

Satellite-Borne Optical Remote Sensing Image Registration Based on Point Features.

Since technologies in image fusion, image splicing, and target recognition have developed rapidly, as the basis of many image applications, the performance of image registration directly affects subsequent work. In this work, for rich features of satellite-borne optical imagery such as panchromatic and multispectral images, the Harris corner algorithm is combined with the scale invariant feature transform (SIFT) operator for feature point extraction. Our rough matching strategy uses the K-D (K-Dimensional) tree combined with the BBF (Best Bin First) method, and the similarity measure is the nearest neighbor/the second-nearest neighbor ratio. Finally, a triangle-area representation (TAR) algorithm is utilized to eliminate false matches in order to ensure registration accuracy. The performance of the proposed algorithm is compared with existing popular algorithms. The experimental results indicate that for visible light and multi-spectral satellite remote sensing images of different sizes and different sources, the proposed algorithm in this work is excellent in accuracy and efficiency.

Detecting Features via Modified Harris Corners and Matching Them through SIFT

Interpreting images spatially is a daunting task which is achieved by detecting corners and features. The most important task of detecting features is achieved by Harris Corner Algorithm. The algorithm is not robust to different scale of the same image. The algorithm may detect corner but when the image is zoomed in, the corner may appear as ridges. We use the corners detected from Harris Corner algorithm and treat these as key points to pass into Scale Invariant Feature Transform (SIFT) algorithm. The SIFT algorithm extracts descriptor vector of dimension 128 X 1 from these corners and can be used to find similarity between different images. This process is quite robust to noise, intensity, scale and occlusion and is used for matching images from a database of descriptors. We have investigated both the algorithms in this paper and made a modified version of Harris Corner algorithm by performing different kind of thresholding, both of them gave a little different result.

An improved corner detection algorithm used in video statistics

In order to address the difficult problem to determine the number of populations, this paper improves the algorithm based on the Harris point detection algorithm, and the number of people is returned through the first-order linear regression model. First of all, according to the shortcomings of Harris corner algorithm in population statistics, an adaptive gray difference idea is proposed, and the concept of integral image is introduced to overcome its defects in noise immunity and real-time operation. Secondly, in view of the large error generated in the process of population statistics in the first-order static model, a dynamic linear model regression method is proposed. In this method, it is believed that there is certain proportionality coefficient between each frame of corner points and the number of people with the change of time, and this coefficient has certain correlation with the angle points in the previous frame and current frame. At the same time, in order to eliminate the number of redundant corners generated in the corner statistics process, the frame difference method is used to filter the stationary point. Finally, the number of people is returned through first-order linear model.

Recognition of Birds in Blurred and Illumination Images by Local Features

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

Application of an Adaptive Harris Corner Algorithm for Inspection of Pressure Marks on LCD Panel

Application of an Adaptive Harris Corner Algorithm for Inspection of Pressure Marks on LCD Panel

Application of Image Stitching in the Scene Investigation of Traffic Accident

Image stitching technology is to stitch some picture into a whole seamless view with no loss of resolution. In order to meet the random objective conditions and the characteristics of high real-time demand in traffic accident scene image stitching, an improved Harris corner algorithm has been proposed in this paper. The algorithm reduces the impact from parameter setting itself on extracting corners. And can complete image registration accurately, by using a smooth transition of fusion algorithm, the algorithm achieve a seamless image.

License Plate Detection Using Harris Corner and Character Segmentation by Integrated Approach from an Image

This paper addresses License Plate localization with the integrated segmentation approach. As the significance of open travel framework builds an Automatic License Plate Recognition has ended up being a critical exploration subject. ALPR furnished with numerous keen observation frameworks like, street activity administration, security administration, programmed toll gathering framework, and so on. Various systems have been offered for license plate recognition, every bearing its own particular points of interest and hindrances. The crucial step in ALPR system is the precise confinement of number plate, Segmentation, Recognition. Harris corner algorithm is proposed in this paper which end up being robust in changing motion and illuminated lightning conditions. While the precision of License Plate restriction is fed forward to the Segmentation stage. The Segmentation is accomplished by a method of connected component analysis consolidated with Pixel count, Aspect ratio and Height of characters. Toward the end, the simulated results are shown with conclusion and future work.