Corner Detection Research Papers - Academia.edu (original) (raw)

Detecting points of interest is one important issues in image processing systems and these points could be uses to eliminates the information of the images to minimum number to be used in recognition or data reduction ..etc. in this paper we produce a method to detect corners in images depending on topological and tint information , The gradient of intensity is calculated in two steps one for grayscale to detect curvatures and other for binary to detect corners in curvatures . The method was quite accurate , efficiency and fast , but rather fair with noisy images

- by
- •
- Corner Detection

We present the design and implementation of a real-time computer vision system for a rotorcraft unmanned aerial vehicle to land onto a known landing target. This vision system consists of customized software and off-the-shelf hardware which perform image processing, segmentation, feature point extraction, camera pan/tilt control, and motion estimation. We introduce the design of a landing target which significantly simplifies the computer vision tasks such as corner detection and correspondence matching. Customized algorithms are developed to allow for realtime computation at a frame rate of 30 Hz. Such algorithms include certain linear and nonlinear optimization schemes for model-based camera pose estimation. We present results from an actual flight test which show the vision-based state estimates are accurate to within 5 cm in each axis of translation, and 5 degrees in each axis of rotation, making vision a viable sensor to be placed in the control loop of a hierarchical flight management system.

Corner detection is widely used in image processing and machine vision. Hence, different corner detectors are proposed. But the performance of such corner detectors is sensitive to round effect and curve shape of the edges. In this paper we propose a novel corner detector that either over come corner detection problems and produces some information about detected corners that is very useful in segmentation and object recognition.

- by Ali Asghar Pour Haji Kazem
- •
- Computer Vision, Image Processing, Machine Vision, Image segmentation

Corners are visually distinguishable, well localized, and more robust than other image feature-points like wavelet-maxima points. The contour-based multi-scale corner detectors, commonly known as curvature scale-space (CSS) detectors, first obtain planar curves using some edge detector and then search for the curvature-maxima points along those curves in the scale-space. The existing CSS-based detectors suffer from two main problems. First, their curvature estimation technique considers a very small neighborhood which makes the corner detection technique sensitive to the local variations and the noise on the curve. Second, they require appropriate curve smoothingscale selection which is difficult in practice. Any successful application of a corner detector requires a sophisticated matching technique. Existing corner matching techniques are computationally intensive and may not be able to obtain all the repeated corners between different versions (transformed and signal processed) of the same image. This thesis first presents an improved contour-based multi-scale corner detector that parameterizes the extracted curves using the affine-length. This improved detector shows better performance than the existing CSS detectors in geometric transformed images. Then a new multi-scale corner detector is presented that uses the chord-to-point distance accumulation technique of discrete curvature estimation. The proposed new detector considers a large neighborhood and does not require the appropriate smoothing-scale selection, and thus overcomes the main problems associated with the existing CSS detectors. Finally, a new geometric point matching technique is proposed that obtains all the repeated corners within the allowed localization error. The proposed corner detectors and matching technique in transformed image identification and copyright protection.

Energy saving is of important concern in case of both domestic and industrial buildings. There are various automated systems for power control and energy saving. Most of those methods are based on Wireless Sensor Networks (WSNs). These systems has drawback of high false alarms since they are depending on sensor values for generating output. This paper explains a vision based system which works on video surveillances now widely used in buildings. The system will continuously check whether any human is present in the room, if not it will automatically switch off the electrical appliances in the room. The entire project consists of two units. DSP unit for human presence detection via head tracking, Embedded unit for controlling the appliances.

- by NISHAD TN
- •
- Embedded Systems, Digital Image Processing, Motion Detection, Power Quality Monitoring

Perspective distortion occurs due to the perspective projection of 3D scene on a 2D surface. Correcting the
distortion of a single image without losing any desired information is one of the challenging task in the
field of Computer Vision. We consider the problem of estimating perspective distortion from a single still
image of an unstructured environment and to make perspective correction which is both quantitatively
accurate as well as visually pleasing. Corners are detected based on the orientation of the image. A
method based on plane homography and transformation is used to make perspective correction. The
algorithm infers frontier information directly from the images, without any reference objects or prior
knowledge of the camera parameters. The frontiers are detected using geometric context based
segmentation. The goal of this paper is to present a framework providing fully automatic and fast
perspective correction.

Credit card fraud occurs when user provides their information to the unknown persons or stolen by the unknown persons, that information can be used for unauthorized online purchase and some other situation. Data mining techniques are employed to study the patterns and characteristics of normal and abnormal transactions based on normalized and anomalies data. In order to detect fraud activities and feature patterns associated with financial activities, we are proposing novel fraud detection framework, Codetect. Wide variety of applications is associated with anomaly detection such as fraud detection and network intrusion detection which referees to pattern finding problems in data.

This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. Details of the new feature detectors and of the new noise reduction method are described, along with test results.

- by Stephen Smith
- •
- Computer Vision, Image Processing, Noise reduction, Corner Detection
- by Han Wang
- •
- Computational Complexity, Motion estimation, Edge Detection, Feature Detection

A real time automatic optical inspection (AOI) system for printed circuit board (PCB) drill bit and the inspection methods are presented. After a brief comparison between traditional inspection methods and the AOI method, the outline of the AOI system configuration and inspection methods are described. Subsequently, the illumination method is discussed due to the special characteristics of drill bit blade for obtaining a clear and less distortion image. Based on the shape features of drill bit blade, inspection algorithms including edge extraction, edge subpixel location, line and curve fitting, corner detection and defect detection are investigated to enable inspection of 100% such drill bits including dimensional properties and surface defects for the drill bit blade. The test result demonstrates that the effectiveness of the developed inspection algorithms and the AOI method can meet the inspection requirements of PCB drill bits. Furthermore, AOI method makes the inspection for each drill bit available and increases the quality and the reliability of PCB drill bits

- by Aoy AuTomatize
- •
- Printed Circuit Board, Real Time Systems, Feature Extraction, Shape
- by Asif Masood
- •
- Algorithms, Corner Detection, Human Vision System

Corner detection is a low-level feature detection operator that is of great use in image processing applications, for example, optical flow and structure from motion by image correspondence. The detection of corners is a computationally intensive operation. Past implementations of corner detection techniques have been restricted to software. In this paper we propose an efficient very large-scale integration (VLSI) architecture

- by Rajiv Mehrotra
- •
- Cognitive Science, Image Processing, Machine Vision, Image segmentation
- by Cihan Topal
- •
- Computational Geometry, Image segmentation, Edge Detection, Profitability

A growing demand for traffic data concerning traffic flow and automatic vehicle identification, led researchers around the world to adopt advanced electronic and computer vision technologies to monitor and control traffic. Increasing levels of road traffic ask for real time analysis of a moving car in order to extract important data, in this case the vehicle registration number. This paper

- by Nadeem Daudpota
- •
- Computer Vision, Image Processing, Image, Traffic Management

Detecting points of interest is one important issues in image processing systems and these points could be uses to eliminates the information of the images to minimum number to be used in recognition or data reduction ..etc. in this paper we produce a method to detect corners in images depending on topological and tint information , The gradient of intensity is calculated in two steps one for grayscale to detect curvatures and other for binary to detect corners in curvatures. The method was quite accurate , efficiency and fast , but rather fair with noisy images

- by JASH MATHEW
- •
- Corner Detection
- by Muhammad Sarfraz
- •
- Algorithms, Image Processing, Computational Geometry, Digital Images
- by Nguyen Cuong
- •
- Computational Geometry, Image segmentation, Edge Detection, Feature Extraction
- by Domenico Tegolo
- •
- Cognitive Science, Edge Detection, Autocorrelation, Corner Detection

A novel corner detection algorithm is presented which can be used to camera calibration methods where square corners are used as control points. Corners are detected with sub-pixel accuracy, using a segmentation method for separation of each square, based on seeds. These are pixels with a predefined color or gray value. An 11x11 proper developed template, including pixels of the

- by Theodore Pachidis
- •
- Camera Calibration, Corner Detection, Robot Manipulator

In computer vision, the corners of an object play an important role in shape representation and analysis. In this paper, we describe a new approach to corner detection in a digital image based on the assumption that corners are image points with high information content, and hence corners in an image exist in the regions having considerably high intensity variations.

- by Ambar Dutta
- •
- Computer Vision, Edge Detection, Feature Extraction, Information Content
- by Milan Malkiya
- •
- Image Processing, Embedded Systems, Image recognition, Mobile phone
- by mehdi dadashi haji
- •
- Document processing, Corner Detection
- by Robert Laganiere
- •
- Corner Detection
- by Brian Lovell
- •
- Pattern Recognition, Localization, Performance Evaluation, Edge Detection
- by Laszlo Mero
- •
- Information Systems, Image Processing, TV, Automation

This paper presents a Web based system for capturing outlines of 2D shapes using Matlab Web Server. From a simple user interface in HTML, any Web user can upload his data and view the results. Cubi c Bezier curve design is used to capture... more

- by Muhammad Sarfraz
- •
- User Interface, Bezier Curve, Human Interaction, Web Based System
- by Mita Nasipuri and +1
- •
- Pattern Recognition, Neural Network, Edit Distance, Corner Detection

This paper proposes a new invariant corner detection algorithm using steerable filters and Harris corner detection. The steerable filters have better orientation selectivity and multi-orientation image decomposition that provide a useful front-end for image-processing and computer vision applications. Corners, in image analysis, are important features for image registration, stereo matching, motion tracking and object recognition. These corners are referred as interest points or key points for image registration. In this paper, we compare the performance of SUSAN, Harris, and propose corner detectors in terms of consistency.

Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and kmean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms.

There are many applications, such as image copyright protection, where transformed images of a given test image need to be identified. The solution to this identification problem consists of two main stages. In stage one, certain representative features, such as corners, are detected in all images. In stage two, the representative features of the test image and the stored images are compared to identify the transformed images for the test image. Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. However, the arc-length used to parameterize the planar curves by the existing CSS detectors is not invariant to geometric transformations such as scaling. As a solution to stage one, this paper presents an improved CSS corner detector using the affine-length parameterization which is relatively invariant to affine transformations. We then present an improved corner matching technique as a solution to the stage two. Finally, we apply the proposed corner detection and matching techniques to identify the transformed images for a given image and report the promising results.

- by Mohammad Awrangjeb
- •
- Image Processing, Curvature, Corner Detection, Curvature Scale Space

Point-based registration is one of the most popular registration methods in practice. During registration, we use external fiducial markers that are rigidly attached through the skin to the skull. Determining the coordinates of the fiducials without human selection is crucial for a fully automatic point-based image registration. In this paper, an image processing technique for automatic fiducial detection is presented.

- by Ishwar Sethi
- •
- Brain Imaging, Image Registration, Object Detection, Corner Detection
- by A. Varkonyi-koczy
- •
- Computer Science, Computer Vision, Image Processing, Pattern Recognition
- by Leonardo Trujillo
- •
- Computer Vision, Genetic Programming, Optimization Problem, Domain Knowledge
- by Lucas Teixeira
- •
- Computer Vision, Feature Selection, Real Time, Parallel Computer

In this paper, an effective method for human eye tracking and also decreasing the current challenges and problems in its algorithms, possibly as real time and for unconstrained environments has been proposed. In this method, firstly face has been detected and segmented from the remaining parts to make the searching area in tracking stage, narrower and processing speed higher. Then eye area is determined and eye pupils are detected in the specified area. In the proposed method, to support tracking in eye occlusion state, corner detection has been additionally used. Experimental results show the potential of this method for real time eye tracking in unconstrained environments with existence of complex background, head and face rotation, beard, makeup, eye glasses and veil, even while the eyes are closed. The correct recognition rate of the proposed method is about 91.9%.

- by Mahmoud Mahlouji
- •
- Eye tracking, Face Detection, Kalman Filter, Eye Detection
- by Mahmoud Belhocine
- •
- Computational Modeling, Augmented Reality, Image, Computer Model
- by Asif Masood
- •
- Computational Complexity, Complexity Analysis, Curve fitting, Corner Detection
- by Aaron Wolin
- •
- OPERATING SYSTEM, Handwriting Recognition, Sketch Recognition, Corner Detection

... Restrictions apply. Page 6. Liu , HC and MD Srinath., (1990). Corner detection from chain code. ... Pattern Recognition Letters, 13, 849-856. Sankar PV and CV Sharma, (1978). A parallel procedure for the detection of dominant points... more

- by P. Nagabhushan
- •
- Corner Detection
- by A. Varkonyi-koczy and +1
- •
- Soft Computing, Neural Network, Edge Detection, Intelligent System
- by Marco Mammarella and +2
- •
- Cognitive Science, Machine Vision, Feature Extraction, Pose Estimation

In this paper, we present method that detects useful
feature points based on hardware architecture. We
propose hardware architecture that uses the
algorithm of FAST-n[1]. Feature point detection
process needs extensive computing power and
processing time. Therefore, we build hardware
structure for real-time processing. The structure of
the hardware is as follows. After loading the images
in parallel, finding feature point candidates and
selecting valid feature point modules operate
simultaneously and independently using pipeline
structure to reduce processing time. Proposed
hardware architecture will operate in about 20,000
cycles in case of 320 x 240 resolution image. If our
hardware structure is used for 1080p, the
performance of processing will be about 70fps.

- by SDIWC Organization
- •
- Computer Science, Edge Detection, FAST, Feature Point
- by IOSR Journals
- •
- Image Processing, Image segmentation, Edge Detection, Corner Detection
- by Antonio Guiducci
- •
- Cognitive Science, Artificial Intelligence, Pattern Recognition, Image Analysis
- by Björn Giesler
- •
- Computer Vision, Image Reconstruction, Edge Detection, 3-D Reconstruction

Camera calibration is a central topic in computer vision, since it is the first and fundamental step for image rectification, D modelling and reconstruction. Good results can be obtained using very well known camera calibration algorithms like the ones presented by Zhang or Tsai; both of them need an accurate initialization procedure that requires to determine the corner positions of a calibration pattern (e.g. a chessboard) with very high precision. In this paper we propose an efficient algorithm which determines the chessboard corners with subpixel precision; moreover it does not make any assumption on the scale and orientation of the chessboard, and works under very different illumination conditions. The method first localizes the chessboard in the image, then it determines the size of its squared elements, and finally it looks for the corners by means of a simple statistical model. The results presented show the accuracy and the robustness of the method.