Harris Corner Features Research Articles

Application research of interior space design elements based on image processing

In order to quantitatively analyze the application performance of elements in interior space design, a detection method of texture visual features of elements distribution images in interior space design based on image processing and texture edge contour features detection is proposed. A multi-resolution visual information collection model for detecting the texture visual characteristics of element distribution images in indoor space design is constructed, and a visual characteristic identification model for element application configuration in indoor space design is established. Multi-texture grid block area combination design method is adopted to realize the fusion of element application configuration visual images in indoor space design. Through the edge feature detection method of texture grid area, Establish a visual parameter identification model of element application configuration in interior space design, extract the visual information feature quantity of element application configuration in interior space design, adopt Harris corner feature matching method, realize distributed detection of texture edge contour features of element application configuration visual features in interior space design, and fuse detection of high-resolution texture information through combined filtering and collaborative filtering. The collection and optimization of visual features of element application configuration in interior space design are realized, and the distributed detection algorithm of texture edge contour features is adopted to improve the convergence level of detection of texture visual features of element distribution images in interior space design, and the application of elements in interior space design based on image processing is realized. The simulation results show that this method has good resolution and strong visual expression ability in extracting the texture visual features of element distribution images in interior space design, which improves the quantitative analysis ability of elements in interior space design.

On-Earth Observation of Low Earth Orbit Targets through Phase Disturbances by Atmospheric Turbulence

This paper addresses the challenges of ground-based observation of Low Earth Orbit (LEO) targets using on-Earth telescopes and proposes solutions for improving image quality and tracking fast-moving objects under atmospheric turbulence conditions. The study investigates major challenges, including atmospheric turbulence-induced aberrations, blurring, telescope platform vibrations, and spatial variations caused by the target motion. A scenario simulation is conducted considering practical limitations, such as feasible time slots for monitoring and the number of available frames within the Field of View (FoV). The paper proposes a novel method for detecting LEO targets using Harris corner features and matching adjacent images upon the corrected frames by Adaptive Optics (AO), with a 38% reduction in the Mean Squared Error (MSE) achieved for certain frames within the isoplanatic angle. Additionally, a refinement strategy for deblurring images is proposed, combining the post-processing with Singular Value Decomposition (SVD) filtering; with a proper filtering factor (β=0.1), an almost complete collection of 14 corner nodes can be detected. The feasibility of continuously tracking objects with uncontrolled attitudes, such as space debris, is successfully demonstrated with continuous monitoring of certain features. The proposed methods offer promising solutions for ground-based observation of LEO targets, providing insights for further research and application in this field.

Research on Image Fusion in UAV Visual Navigation

The accuracy of multi-sensor image fusion is the key to improve the accuracy of UAV visual navigation. In this paper, a two-step registration method combining region registration and feature registration is proposed. First, rough image registration is carried out based on improved Hough transform and Fourier-Mellin transform, and image precise registration is implemented based on Harris corner feature and cross-correlation function. Finally, wavelet transform is applied to image fusion and image edge enhancement to realize the coarse to accurate registration of SAR images.

Signature Verification Using The K-Nearest Neighbor (KNN) Algorithm and Using the Harris Corner Detector Feature Extraction Method

The security of the transaction process is very important in this day and age. Signatures can be used as a means of guaranteeing the security of a transaction other than fingerprints. However, the threat of signature forgery for those who use signatures as security is still very high and frequent. In this research, we will verify the authenticity of a signature and test it using the K-Nearest Neighbour (KNN) algorithm and the Harris Corner feature extraction method. There are two kinds of distance calculations that will be used in the K-NN algorithm, namely by calculating the distance from Euclidean Distance and Manhattan Distance. The k value at KNN taken is at k = 1, k = 3, and k = 5.

Cognitive IoT Vision System Using Weighted Guided Harris Corner Feature Detector for Visually Impaired People

India has an estimated 12 million visually impaired people and is home to the world’s largest number in any country. Smart walking stick devices use various technologies including machine vision and different sensors for improving the safe movement of visually impaired persons. In machine vision, accurately recognizing an object that is near to them is still a challenging task. This paper provides a system to enable safe navigation and guidance for visually impaired people by implementing an object recognition module in the smart walking stick that uses a local feature extraction method to recognize an object under different image transformations. To provide stability and robustness, the Weighted Guided Harris Corner Feature Detector (WGHCFD) method is proposed to extract feature points from the image. WGHCFD discriminates image features competently and is suitable for different real-world conditions. The WGHCFD method evaluates the most popular Oxford benchmark datasets, and it achieves greater repeatability and matching score than existing feature detectors. In addition, the proposed WGHCFD method is tested with a smart stick and achieves 99.8% recognition rate under different transformation conditions for the safe navigation of visually impaired people.

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.

Improved Accuracy of Suspicious Activity Detection in Surveillance Video

Suspicious activity detection from surveillance video is the main objective of the work presented in this paper. The method developed consist of various stages of suspicious frame detection, and verifying the frame for suspicious activity related analysis of human movements within obtained set of suspicious frames. The method consist of GLCM feature extraction which constitutes the features such as energy, prominence, contrast, entropy, homogeneity type of features and matching using Euclidian distance along with descriptor features obtained by using Harris corner features and cosine similarity index estimation. The successful suspicious activity detection rate is analyzed which shows better performance and time saving method while analyzing large surveillance video dataset.

A New Recognition and Pose Estimation of Tiny Electronic Parts Using Principal Component Analysis and Harris Corner Features

The center coordinates and rotated angle of the electronic part must be precisely estimated in case of palletizing the parts by picking and placing the parts to output tray by a robot, especially, the tiny parts like surface mounting device (SMD). A machine vision algorithm must be used in recognizing the type as well as estimating the pose of the parts. In this paper, we propose not only a recognition algorithm of electronic part using Principal Component Analysis [PCA] combined with Harris corner feature (HCF) and least mean square error (LMSE) for the matching of the part with data base, but also a precise pose estimation of the recognized part by using the extracted four corners from HCF combined with lines from Hough transformation (LHT). Applying the proposed algorithm to the test setup of five kinds of SMDs, we verified the usefulness of the proposed algorithm in accurate pose estimation as well as in recognition of type of the parts even in environment with illumination changes.

Detecting anomalous crowd scenes by oriented Tracklets’ approach in active contour region

Video imagery based crowd analysis has become a topic of great interest for public safety at the venues of mass gathering events. This paper presents a novel approach to detect anomalous scene in high density crowded places. We propose an oriented tracklets approach in active contour region and measure the entropy and temporal occupancy deviation of oriented tracklets over the frames. To this end, an oriented trajectory algorithm is designed to extract tracklets of moving crowd. For trajectory extraction, spatio-temporal interest points are detected by adopting Harris corner features. The detected interest points are tracked over the frames within the optimized region. An active contour segmentation approach is applied to optimize the tracking region as a moving crowd is not distributed in entire frame region. The flow direction of each oriented tracklet is distributed into histogram bins at a specified interval, which defines the flow of collective motion pattern. A real-time scene updating procedure is also followed to adapt the changes of crowd scenes. Further, an entropy of histogram of oriented tracklets is computed based on the probability of occurrence of the tracklets. It has been shown that entropy of flow direction changes markedly in the unusual state of affairs. A simulation on a large number of the anomalous scene has been exercised to see the characteristics of an entropy. Also, temporal occupancy deviation is computed which measures the area occupied by the extracted tracklets of the crowd during a certain interval of time. If entropy and temporal occupancy deviation increase beyond a certain threshold, an alert is issued to detect anomaly to prevent potentially dangerous crowd-related disasters. Experiments conducted on three publicly available benchmark crowd datasets such as UMN, UCF Web, and Violent Flows, obtained interesting and promising results. We also evaluated some manually collected challenging real-world crowd video sequences. We compared the proposed approach with various state-of-the-art methods, and achieve remarkable accuracy while maintaining the lower computational complexity.

Real-time object tracking system based on field-programmable gate array and convolution neural network

Vision-based object tracking has lots of applications in robotics, like surveillance, navigation, motion capturing, and so on. However, the existing object tracking systems still suffer from the challenging problem of high computation consumption in the image processing algorithms. The problem can prevent current systems from being used in many robotic applications which have limitations of payload and power, for example, micro air vehicles. In these applications, the central processing unit- or graphics processing unit-based computers are not good choices due to the high weight and power consumption. To address the problem, this article proposed a real-time object tracking system based on field-programmable gate array, convolution neural network, and visual servo technology. The time-consuming image processing algorithms, such as distortion correction, color space convertor, and Sobel edge, Harris corner features detector, and convolution neural network were redesigned using the programmable gates in field-programmable gate array. Based on the field-programmable gate array-based image processing, an image-based visual servo controller was designed to drive a two degree of freedom manipulator to track the target in real time. Finally, experiments on the proposed system were performed to illustrate the effectiveness of the real-time object tracking system.

Visual positioning of rectangular lead components based on Harris corners and Zernike moments

With the increasing necessities for reliable printed circuit board (PCB) product, there has been a considerable demand for high speed and high precision vision positioning system. To locate a rectangular lead component with high accuracy and reliability, a new visual positioning method was introduced. Considering the limitations of Ghosal sub-pixel edge detection algorithm, an improved algorithm was proposed, in which Harris corner features were used to coarsely detect the edge points and Zernike moments were adopted to accurately detect the edge points. Besides, two formulas were developed to determine the edge intersections whose sub-pixel coordinates were calculated with bilinear interpolation and conjugate gradient method. The last experimental results show that the proposed method can detect the deflection and offset, and the detection errors are less than 0.04° and 0.02 pixels.

Novel Recognition Method of Blast Furnace Dust Composition by Multifeature Analysis Based on Comprehensive Image-Processing Techniques

The traditional artificial recognition methods for the blast furnace dust composition have several disadvantages, including a great deal of information to dispose, complex operation, and low working efficiency. In this article, a multifeature analysis method based on comprehensive image-processing techniques was proposed to automatically recognize the blast furnace dust composition. First, the artificial recognition and feature analysis, which included image preprocessing, Harris corner feature, Canny edge feature, and Ruffle feature analysis, was designed to build the template image, so that any unknown dust digital image could be tested. Second, the composition of coke, microvariation pulverized coal, vitric, ash, and iron from dust would be distinguished according to their different range of values based on the multifeature analysis. The method is valid for recognizing the blast furnace dust composition automatically, and it is fast and has a high recognition accuracy.

Performance Improvement in Vslam Using Stabilized Feature Points

Simultaneous localization and mapping (SLAM) is the main prerequisite for the autonomy of a mobile robot. In this paper, we present a novel method that enhances the consistency of the map using stabilized corner features. The proposed method integrates template matching based video stabilization and Harris corner detector. Extracting Harris corner features from stabilized video consistently increases the accuracy of the localization. Data coming from a video camera and odometry are fused in an Extended Kalman Filter (EKF) to determine the pose of the robot and build the map of the environment. Simulation results validate the performance improvement obtained by the proposed technique.