Corner Detection Algorithm Research Articles

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.

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.

Harris Corner Detection Algorithm Optimization Based on OTSU

Harris Corner Detection Algorithm Optimization Based on OTSU

A Spiking Neural Model of HT3D for Corner Detection

Obtaining good quality image features is of remarkable importance for most computer vision tasks. It has been demonstrated that the first layers of the human visual cortex are devoted to feature detection. The need for these features has made line, segment, and corner detection one of the most studied topics in computer vision. HT3D is a recent variant of the Hough transform for the combined detection of corners and line segments in images. It uses a 3D parameter space that enables the detection of segments instead of whole lines. This space also encloses canonical configurations of image corners, transforming corner detection into a pattern search problem. Spiking neural networks (SNN) have previously been proposed for multiple image processing tasks, including corner and line detection using the Hough transform. Following these ideas, this paper presents and describes in detail a model to implement HT3D as a Spiking Neural Network for corner detection. The results obtained from a thorough testing of its implementation using real images evince the correctness of the Spiking Neural Network HT3D implementation. Such results are comparable to those obtained with the regular HT3D implementation, which are in turn superior to other corner detection algorithms.

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.

Novel Image Segmentation Algorithm Based on Automatic GVF Snake Model

To address issues that GVF Snake model needs to initialize the contour manually so that image segmentation cannot be automatically handled, the effect of segmentation is also related to the initial contour, and its efficiency and accuracy of the model are not ideal, a novel algorithm for image segmentation based on automatic GVF Snake model is proposed in this paper. We introduce the concepts of flat redundant point and fitting outsourcing polygon. In our algorithm the discrete feature edge points set of target image is first obtained by SUSAN corner detection algorithm. Secondly, finding the fitting outsourcing polygon of discrete edge points set, Finally, the GVF Snake curve is evolved by using the fitting outsourcing polygon as the initial contour curve to complete the image segmentation. It is not only proved that the fitting outsourcing polygon as the initial contour curve can converge to the real target image edge by the algorithm in theory, but the experimental results also show that the proposed algorithm can effectively improve the segmentation accuracy, greatly reduce the number of iterations of the model, and improve the efficiency of the algorithm.

Seperating Touching Particles: A Concavity-Based Method Using the Area Ratio of a Circular Mask

To address the difficulty of separating touching particles in an image, a method based on the area ratio of a circular mask is proposed in this article, and a framework consisting of image segmentation, concave point detection, point-pair matching, and the shape estimation of particles is implemented. A given color image is first preprocessed to obtain a binary image, then a corner detection algorithm is adopted to obtain a rough estimation of potential concave points, following which, the area ratio method is used to refine segmentation points qualified. The segmentation points obtained are matched to each pair by gray projection according to a certain evaluation criteria. Finally, each pair of points and one of their adjacent pixels on the border are used to estimate the status of each particle before adhesion, and the contour of each particle is generated using the curve fitting method. Experimental results show that the proposed method is consistent with human visual perception and can be used in the practice.

A New FPGA Architecture of FAST and BRIEF Algorithm for On-Board Corner Detection and Matching.

Although some researchers have proposed the Field Programmable Gate Array (FPGA) architectures of Feature From Accelerated Segment Test (FAST) and Binary Robust Independent Elementary Features (BRIEF) algorithm, there is no consideration of image data storage in these traditional architectures that will result in no image data that can be reused by the follow-up algorithms. This paper proposes a new FPGA architecture that considers the reuse of sub-image data. In the proposed architecture, a remainder-based method is firstly designed for reading the sub-image, a FAST detector and a BRIEF descriptor are combined for corner detection and matching. Six pairs of satellite images with different textures, which are located in the Mentougou district, Beijing, China, are used to evaluate the performance of the proposed architecture. The Modelsim simulation results found that: (i) the proposed architecture is effective for sub-image reading from DDR3 at a minimum cost; (ii) the FPGA implementation is corrected and efficient for corner detection and matching, such as the average value of matching rate of natural areas and artificial areas are approximately 67% and 83%, respectively, which are close to PC’s and the processing speed by FPGA is approximately 31 and 2.5 times faster than those by PC processing and by GPU processing, respectively.

RST invariant robust video watermarking algorithm using quaternion curvelet transform

Digital images are widely distributed today over the internet and through other mediums. There are powerful digital image processing tools which have made perfect image duplication a trivial procedure. Therefore, security, copyright protection, and integrity verification of digital video have become an urgent issue in the digital world, which can be achieved by means of a technique called digital watermarking. The issues of watermarking are to achieve imperceptibility, robustness, payload and security simultaneously. This paper presents a new Quaternion Curvelet Transform (QCT) based video watermarking technique for embedding a video on another video in a secure and optimized way. However, many of the existing techniques are unable to handle the problem of rotation, translation and scaling attacks on watermarked video. This study presents a novel embedding approach where in the quaternion transform followed by the traditional Curvelet transform is able to overcome the above disadvantages. In this paper, we first represent the color cover video frames in the quaternion form and subsequently generate Quaternion Curvelet Transform (QCT) coefficients for each such quaternion frames. Second, in order to withstand the desynchronization causes due to the geometrical attacks, the Harris corner detection algorithm is used to determine the invariant feature points on the QCT coefficients, which is followed by the calculation of energy for each block centered on those feature points. Third, in order to maintain the trade-off among the watermarking characteristics, the optimized location for embedding the watermark frames is determined using the cuckoo search optimization algorithm. Finally, to attain the authenticity, an authentication key generation procedure is employed using the owner’s biometric image and the watermarked frames. The experimental results prove that the proposed method is promising in terms of robustness, imperceptibility and security to most notable image processing attacks, geometrical attacks, and video processing attacks among the various conventional watermarking algorithms.

Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

In order to improve the visual effect of the around view monitor (AVM), we propose a novel ring fusion method to reduce the brightness difference among fisheye images and achieve a smooth transition around stitching seam. Firstly, an integrated corner detection is proposed to automatically detect corner points for image registration. Then, we use equalization processing to reduce the brightness among images. And we match the color of images according to the ring fusion method. Finally, we use distance weight to blend images around stitching seam. Through this algorithm, we have made a Matlab toolbox for image blending. 100% of the required corner is accurately and fully automatically detected. The transition around the stitching seam is very smooth, with no obvious stitching trace.

Harris corner detection on a NUMA manycore

Corner detection is a key kernel for many image processing procedures including pattern recognition and motion detection. The latter, for instance, mainly relies on the corner points for which spatial analyses are performed, typically on (probably live) videos or temporal flows of images. Thus, highly efficient corner detection is essential to meet the real-time requirement of associated applications. In this paper, we consider the corner detection algorithm proposed by Harris, whose the main work-flow is a composition of basic operators represented by their approximations using 3 × 3 matrices. The corresponding data access patterns follow a stencil model, which is known to require careful memory organization and management. Cache misses and other additional hindering factors with NUMA architectures need to be skillfully addressed in order to reach an efficient scalable implementation. In addition, with an increasingly wide vector registers, an efficient SIMD version should be designed and explicitly implemented. In this paper, we study a direct and explicit implementation of common and novel optimization strategies, and provide a NUMA-aware parallelization. Experimental results on a dual-socket INTEL Broadwell-E/EP show a noticeably good scalability performance.

Robust corner detection using altitude to chord ratio accumulation

As one of the most significant image local features, corner is widely utilized in many computer vision applications. A number of contour-based corner detection algorithms have been proposed over the last decades, among which the chord-to-point distance accumulation (CPDA) corner detector is reported to produce robust performance in corner detection, especially compared with curvature scale-space (CSS) based corner detectors, which are sensitive to local variation and noise on the contour. In this paper, we investigate the CPDA algorithm in terms of its limitations, and then propose the altitude-to-chord ratio accumulation (ACRA) corner detector based on CPDA approach. Altitude-to-chord ratio is insensitive to the selection of chord length compared with chord-to-point distance, which allows us utilize a single chord instead of the three chords used in CPDA algorithm. Besides, we replace the maximum normalization used in CPDA algorithm with the linear normalization to avoid the uneven data projection. Numerical experiments demonstrate that the proposed ACRA corner detection algorithm outperforms the CPDA approach and other seven state-of-the-art methods in terms of the repeatability and localization error evaluation metrics.

A Corner Detection Algorithm Using the Star-based Grayscale Projection

A Corner Detection Algorithm Using the Star-based Grayscale Projection

Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images

Nowadays, computer aided diagnosis (CAD) system become popular because it improves the interpretation of the medical images compared to the early diagnosis of the various diseases for the doctors and the medical expert specialists. Similarly, bone fracture is a common problem due to pressure, accident and osteoporosis. Moreover, bone is rigid portion and supports the whole body. Therefore, the bone fracture is taken account of the important problem in recent year. Bone fracture detection using computer vision is getting more and more important in CAD system because it can help to reduce workload of the doctor by screening out the easy case. In this paper, lower leg bone (Tibia) fracture types recognition is developed using various image processing techniques. The purpose of this work is to detect fracture or non-fracture and classify type of fracture of the lower leg bone (tibia) in x-ray image. The tibia bone fracture detection system is developed with three main steps. They are preprocessing, feature extraction and classification to classify types of fracture and locate fracture locations. In preprocessing, Unshrap Masking (USM), which is the sharpening technique, is applied to enhance the image and highlight the edges in the image. The sharpened image is then processed by Harris corner detection algorithm to extract corner feature points for feature extraction. And then, two classification approaches are chosen to detect fracture or non-fracture and classify fracture types. For fracture or not classification, simple Decision Tree (DT) is employed and K-Nearest Neighbour (KNN) is used for classifying fracture types. In this work, Normal, Transverse, Oblique and Comminute are defined as the four fracture types. Moreover, fracture locations are pointed out by the produced Harris corner points. Finally, the outputs of the system are evaluated by two performance assessment methods. The first one is performance evaluation for fracture or non-fracture (normal) conditions using four possible outcomes such as TP, TN, FP and FN. The second one is to analysis for accuracy of each fracture type within error conditions using the Kappa assessment method. The programming software used to implement the system is MATLAB with wide range of image processing tools environment. The system produces 82% accuracy for classification fracture types.

Optic disc localization algorithm based on improved corner detection

In the computer-aided diagnosis of retinal images, the location of the optic disc plays an important supporting role in the location of the macula, the segmentation of the blood vessel and the determination of the location of the lesions. In order to avoid the failure of the location of optic discs due to the misjudgment and segmentation of the blood vessels and the changes of brightness caused by the lesions, this paper proposes an algorithm of optic disc localization based on an improved corner detection algorithm by using the feature that the distribution of vessels in the optic disc area is complicated and the corner distribution is the densest. The localization of the optic disc location by using this method in the 3 databases shows that this method can effectively locate the optic disc and has higher accuracy and real-time performance. This method provides a new idea for the localization of the optic disc.

Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

A triangle mesh-based corner detection algorithm for catadioptric images

ABSTRACTAs with conventional images corner detection is an important aspect of many computer vision problems involving catadioptric images. However, classical image processing algorithms are no longer appropriate for catadioptric images due to nonuniform resolution and distortions of catadioptric images. In this paper, we propose a novel approach to corner detection for catadioptric images based on triangle mesh. First, we transform catadioptric images to spherical images by combining an improved projection model for central catadioptric cameras with triangle mesh for a unit sphere. Spherical images yield a spatially uniform resolution domain for processing catadioptric images. Then, based on the topology of a triangle mesh, variations of light intensity with respect to directions for each image patch are measured to detect corners. The proposed algorithm addresses problems of catadioptric image processing caused by non-uniform resolution and distortions of these images. Experimental results showed that comparing to widely used methods, the triangle mesh-based corner detection algorithm can achieve higher repeatability rate relative to different imaging condition changes.

DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

Abstract. In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Automatic single-view monocular camera calibration-based object manipulation using novel dexterous multi-fingered delta robot

The approximation of 3D geometry through single image is a particular instance of 3D reconstruction from several images. The advance information or user input should be provided to retrieve or conclude depth information. This research presents a novel automatic method to enumerated 3D affine measurements from a single perspective image. The least geometric information has been resolute through the image of the scene. The vanishing line and a vanishing point are two required information to reconstruct from an image of a scene. The affine scene structure can be reconstructed through the image of a scene. The proposed approach has many advantages; there is no need of the camera’s intrinsic matrix and the explicit correlation among camera and scene (pose), no need for selecting V x , V y , V z points, novel dexterous robot architecture for manipulation. In this paper, the following approaches have been implemented: (1) the three sets of vanishing points in X, Y, and Z axis; (2) the vanishing lines of the image; (3) distance among planes that parallel to the reference plane; (4) image wrapping; (5) corner detection (algorithm has been implemented in order to make the process automatic). The indigenous data set has been taken for the experiment. The results are compared with Zhang- and ArUco-based calibration. This novel approach has been used to perform tracking and manipulation of an object in real-time environment.