Journal articles on the topic 'Harris corner detection'

Conventional Harris corner detector is a desirable detector but it requires significantly more computation time. For MIC detector proposed by Trajkovic, the minimal computational demands of its operator make it well-suited for real-time applications, however the Trajkovic’s operator responses too readily to certain diagonal edges. For this reason, the paper proposed a new corner detection algorithm. The new corner detection algorithm adopted multigrid algorithm and preprocessed the lower resolution revision of the original image to obtain the potential corners, subsequently used autocorrelation matrix to get the corner response function for the corresponding points of the potential corner. The test results indicate the new corner detection algorithm can decrease edge responses and the number of textural corners effectively. Furthermore, it can satisfy the demands of real-time applications.

An adaptive Harris corner detection algorithm based on the iterative threshold is proposed for the problem that the corner detection algorithm must be given a proper threshold when the corner detection algorithm is extracted. In order to avoid the phenomenon of clustering and restrain the pseudo corner, this algorithm realizes the adaptive threshold selection by iteration instead of the threshold value of the Harris corner detection algorithm. Simulation results show that the proposed method achieves good results in terms of threshold setting and feature extraction.

In this paper, the comparation and analysis of the conner detection algorithm used in the three-dimensional reconstruction were made .Also the improvement and optimization to the parameters of harris algorithm were done. It is concluded that the hariis algorithm improved had a better effect ,simpler operation and better stability in the measurement of material stack volume measurement through the experimental results.

There is no parametric formulation of corner, so the conventional Hough transform cannot be employed to detect corners directly. A random corner detection method is developed in this paper based on a new concept "accumulative intersection space" under Monte Carlo scheme. This method transforms the corner detection in the image space into local maxima localization in the accumulative intersection space where the intersections are accumulated by random computations. The proposed algorithm has been demonstrated by both theory and experiments. The proposed algorithm is isotropic, robust to image rotation, insensitive to noise and false corners on diagonal edges. Unlike the other existing contour based corner detection methods, our algorithm can effectively avoid the influence of the edge detectors, such as rounding corners or line interceptions. Extensive comparisons among our approach and the other detectors including Harris operator, Fei Shen and Han Wang detector, Han Wang and Brady detector, Foveated Visual Search method and SIFT feature, have shown the effectiveness of our method.

Obtaining the depth image is the foundation of the research on other direction of Kinect. When to obtain the depth image information in a Kinect, it can t obtain the depth image accurately if there is a shelter in front of the body, and then the recognition and the restoration of the skeleton will become very difficult. This paper uses the method of Harris corner detection to detect shelter corner which is used to detect the range of shelter and restore the depth image information. But different from the traditional way, this paper uses the depth data values in the corner detection instead of the gray date values. After detecting corner, a series of actions of depth value substitution and smoothing will be done to repair occluded depth image.

This paper proposes a novel inner and outer eye corners detection method, which inosculates corner, regional texture and gray information, called CTGF algorithm. It utilizes corner detector to determine the candidate points of eye corners, such as Harris. Next, the region texture information is obtained through polar coordinate integral, in order to locate the exactly positions of eye corners among the candidate points. The CTGF algorithm provides a precise and reliable facial feature for many computer vision applications and the robustness and accuracy are demonstrated in experiments.

As a first step for image processing operations, detection of corners is a vital procedure where it can be applied for many applications as feature matching, image registration, image mosaicking, image fusion, and change detection. Image registration can be defined as process of getting the misalignment of pixel's position between two or more images. In this paper, a modified corner detector named Synthetic Aperture Radar-Phase Congruency Harris (SAR-PCH) based on a combination between both phase congruency, named later PC, and Harris corner detector is proposed where PC image can supply fundamental and significative features although the complex changes of intensities. Also, the proposed approach overcomes the Harris limitation concerning the noise since the Harris is more sensitive to the noise. The performance was similitude with Shi-Tomasi, FAST, and Harris corner detectors where experiments are conducted first with simulated images and second with real ones. Mean square error (MSE) and peak signal-to-noise ratio (PSNR) are used for the simile. Experimental results, carried out in a standard computer, verify its effectiveness where it utilizes the privileges of image constitutional depicting, allowing extraction of the most powerful key points since it preserves robustness of co-registration process using image frequency properties which are not variant to illumination. Reasonable results compared to the state of art method as Shi-Tomasi, FAST, and Harris algorithms were achieved on the expense of high computational processing time that can be recovered using hardware having high capabilities.

According to the lack of Harris corner detection algorithm in corner detection, positioning, detection speed and anti-noise ,this paper improves Harris operator to make up for the bad stability of original operator, so as to realize accurate inspection, Strong anti-noise, fast, good stability and the exact match of image.

Camera calibration is one of the key technologies of Computer Vision. This paper presented a microscope camera calibration method based on grid sub-pixel corner detection for the particular application area of microscopic measurement. First of all, the actual corner coordinates information of the grid was obtained through the improved Harris corner detection method. Then, considering the distribution law of corner coordinates in the microscopic image, the paper obtained the sub-pixel corner coordinates by combining the quadratic surface and linear fitting. Finally, through the established non-linear camera model, the average error between fore-projection and re-projection grid corner coordinates was obtained by re-projecting the grid corner coordinates. Experimental results show that the sub-pixel corner detection algorithm is accurate and the final calibration error is 0.4637 pixels. Compared with improved Harris corner detection, the accuracy increased by about 52%, which is applicable to microscopic camera calibration.

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 detectors, which depend on strong invariance and a local autocorrelation function, display poor detection performance for infrared (IR) images with low contrast and nonobvious edges. In addition, feature points detected by Harris corner detectors are clustered due to the numerous nonlocal maxima. This paper proposes a modified Harris corner detector that includes two unique steps for processing IR images in order to overcome the aforementioned problems. Image contrast enhancement based on a generalized form of histogram equalization (HE) combined with adjusting the intensity resolution causes false contours on IR images to acquire obvious edges. Adaptive nonmaximal suppression based on eliminating neighboring pixels avoids the clustered features. Preliminary results show that the proposed method can solve the clustering problem and successfully identify the representative feature points of IR breast images.

It is important for fire detectors to operate quickly in the event of a fire, but existing conventional fire detectors sometimes do not work properly or there are problems where non-fire or false reporting occurs frequently. Therefore, in this study, HSV color conversion and Harris Corner Detection were used in the image pre-processing step to reduce the incidence of false detections. In addition, among the detected corners, the vicinity of the corner point facing the upper direction was extracted as a region of interest (ROI), and the fire was determined using a convolutional neural network (CNN). These methods were designed to detect the appearance of flames based on top-pointing properties, which resulted in higher accuracy and higher precision than when input images were still used in conventional object detection algorithms. This also reduced the false detection rate for non-fires, enabling high-precision fire detection.

The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.

The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.

Comprehensive analysis of the tower in the image feature information with Harris corner imprecise for complex image, we propose an improved algorithm, which use of the improved Harris detection method to calculate the number of gray similar to the target pixel 8 field, analyze the gray level distribution of pixels within the local range, and then use the lag suppression method to set threshold , if corner is the objective function greater than the set threshold, the change point was identified as the final corner. The experimental results proved to be effective to determine the image of the tower area.

Abstract This paper presents a new method of detecting vehicles by using a simple and effective algorithm. The features of a vehicle are the most important aspects in detection of vehicles. The corner points are considered for the proposed algorithm. A large number of points are densely packed within the area of a vehicle, and the points are calculated by using the Harris corner detector. Making use of the fact that they are densely packed, grouping of these points is carried out. This grouping indicates that the group of corners belongs to each vehicle, and such groupings play a vital role in the algorithm. Once grouping is done, the next step is to eliminate the background noise. The Lucas-Kande algorithm is used to track the extracted corner points. Each corner point of the vehicle is tracked to make the output stable and reliable. The proposed algorithm is new, detect vehicles in multiple conditions, and also works for complex environments.

Door is a very significant element as it enables a person to enter a house or room. Though identifying doorway is an easy task for a regular person, for robots or visually impaired people it is a challenging task. To overcome this challenge, we have proposed a door detection method. Our proposed method is based on Prewitt edge detection method and Harris corner detector. Here, we are using a number of predefined rules to detect the doorframe correctly. To establish the robustness of our proposed method, we have formed a substantial dataset of scene images that are captured in various unfamiliar environments. Our experimental results validate that our proposed method is robust against changes in viewpoint, shapes, occlusions, illumination, colors, sizes, orientations, and textures of the door. The experimental results show that our proposed method reaches 87.45% accuracy as well as achieves lower false positive rate and lower computational time.

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

This paper presents a novel sub-pixel corner detection algorithm for camera calibration. In order to achieve high accuracy and robust performance, the pixel level candidate regions are firstly identified by Harris detector. Within these regions, the center of gravity (COG) method is used to gain sub-pixel corner detection. Instead of using the intensity value of the regions, we propose to use corner response function (CRF) as the distribution of the weights of COG. The results of camera calibration experiments show that the proposed algorithm is more accurate and robust than traditional COG sub-pixel corner detection methods.

The Harris algorithm via fractional order derivative (the adaptive fractional differentiation Harris corner detection algorithm), which adaptively adjusts the fractal dimension parameter, has been investigated for an analysis of image processing relevant to surface roughness by vision measurements. The comparative experiments indicate that the algorithm allows the edge information in the high frequency areas to be enhanced, thus overcoming shortcomings. The algorithm permits real-time measurements of surface roughness to be performed with high precision, superior to the conventional Harris algorithm.

In order to effectively inhibit the image dithering caused by wind-induced vibration in the security monitoring system, it calls for the extraction and match of the feature points of the sequential frames. Harris corner detection algorithm is a widely-employed characteristics extraction algorithm in the image processing. In the security monitoring field, images and videos photographed are characterized by large scale, high pixel and low contrast degree. The classical algorithm often fails to effectively obtain the feature points while handling the images and videos of the kind. Concerning the above problems, this paper puts forward an improved self-adaptive corner detection algorithm. Firstly, this paper employs the self-adaptive gray threshold comparative results of the of every point with the surrounding eight neighborhood points to select the preselected points of part of the corners. Following that, this paper classifies the preselected points into three types according to certain rules and the value of the already selected self-adaptive gray threshold. At last, according to the classification results, this paper uses different corners to test function threshold and the preselected points as well to eliminate the peripheral points and the pseudo-corners so as to gain the genuine corners. After verifying the above improved algorithm in the practical scenario in the security monitoring, the results of this paper prove its effectiveness, feasibility and its advantages in terms of robustness.

<p>Vision system applied in electrical power generated mobile robot to provide a comfortable ride while providing comfort to tourist to interact with visitors. The camera is placed in front of the mobile robot to snap the images along in pathways. The system can recognized the sign which are right, left and up by using Harris corner algorithms and will be display in Graphical User Interface (GUI). A sign can be determined from the vertex coordinates according to the degree to distinguish the direction of the sign. The system will be tested in term of percentage of success in Harris point detection and availability to detect sign with different range. The result show the even though not all Harris point in an image can be detected but most of the images possible to recognise it sign direction.</p>

Image classification and visualization is a challenging task in hyper spectral imaging system. To overcome thisissue, here the proposed algorithm incorporates normalized correlation into active corner point of an image representation structure to perform hasty recognition by matching algorithm. Matching algorithms can be of two major categories, based on correlation and based on its features based on correlation and on its feature detection. Proposed algorithms often ignore issues related to scale and orientation and also those to be determined during the localization step. The task of localization involves finding the right region within the search image and passing this region to the verification process. A Harris corner detector is an advancedapproach to detect and extract a huge number of corner points in the input image. We integrate all the extracted corner points into a possible task to locate candidate regions in input image. In terms of detection and classification the proposed method has got better result.

Malaria is a curative disease, with therapeutics available for patients, such as drugs that can prevent future malaria infections in countries vulnerable to malaria. Though, there is no effective malaria vaccine until now, although it is an interesting research area in medicine. Local descriptors of blood smear image are exploited in this paper to solve parasitized malaria infection detection problem. Swarm intelligence is used to separate the red blood cells from the background of the blood slide image in adaptive manner. After that, the effective corner points are detected and localized using Harris corner detection method. Two types of local descriptors are generated from the local regions of the effective corners which are Gabor based features and color based features. The extracted features are finally fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed and different combinations of feature types are attempted. The achieved results showed that when using combined vectors of local descriptors, the system gives the desired accuracy which is 100%. The achieved result demonstrates the effectiveness of using local descriptors in solving malaria infection detection problem.

This paper presents a new automatic lung segmentation method. Harris corner detection algorithm is used to solve the problem of separating the left lung from the right one, and contourlet transform and mathematical morphology hybrid algorithm are used to solve the problem that the nodules at lung edge is easy to be missed. Through the simulation results of multiple lung CT images, compared with the common algorithms, the results show that the average sensitivity and average accuracy become much better.

An improved corner detection algorithm for medical image based on USAN is proposed in this paper. The algorithm firstly defines the concept of end line which through the core of the circular template, and analyses the number of end lines in the corner point, thus obtains an initial response function of the corner. Then puts forward a non-maxima suppression method by defining the concept of uniformity coefficient of the non-end line. Finally, compared with existing algorithms, experiments indicates that the improved algorithm has the fast and accurate advantages. I.IntroductionCorner detection based on grayscale images has been widely used in medical image registration. Medical image registration techniques can be divided into two categories, which is based on pixels (voxels) and the feature-based methods. Feature-based registration method is matched by extracting the structural features of different images (the common features including points, contours, curves and surfaces, etc.) , in which the registration based on feature points is the most widely applied method[1]. Corner points as the feature of image has rich information content and a little calculation, matching simple and rotation, translation, scaling invariant properties. In recent years, some improved methods for Harris algorithm[2,3] and USAN (Univalue Segment Assimilating Nucleus, with the value of the shrinking core) algorithm[4] method have been applied in image registration. In 2002, Zhou Peng, etc. developed a new corner detection method based on the model proposed in the USAN[5].Experimental results show that image registration based on the method without restrictions on the rotation of the image, it improves the registration accuracy and has less computation. In 2006, Rae etc. built a new multi-scale Harris corner detection algorithm[6]. The new corner-detection method can obtain corner points at different scales corner, improved the detection performance of detection operators, advanced the registration accuracy of corner-based image registration algorithm. In 2007, Huashun Gang, etc. absorbed and improved the SUSAN , proposed a dense image matching algorithm[7], using Sobel operator and the feature similarity to initially match the corner on the left and right image, achieved dense matching of all the pixels of two images which without calibration. At present, image corner detection based on gray, in particular the method based on template made great development. However, the accuracy of these methods are mostly poor, algorithm complexity, time-consuming, so have some limits in real-time applications. Based on the USAN, a new improved algorithm was proposed and has been applied to medical image corner detection, the new algorithm in terms of speed and accuracy has a larger increase.II.Susan Corner Detection PrincipleSUSAN algorithm [4] defines a circular template, as shown in Fig. 1, the center of circular template is called the core, if some pixel's brightness of the circular template is the same or similar to the core, the region composed with these pixels will be defined USAN region ,at the corner point and non-corner point, USAN region area is different, and the USAN area of the corner point is the smallest.Fig. 1 The circular template of SUSAN algorithmDoing the specific defection, a circular template with 37 pixel will be used to scan the entire image, and to calculate the gray intensity’s minus between each pixel and the core of the template, if the absolute value is less than the given threshold t ,which will be considered to belong to USAN ,the commonly used similarity comparison function is as follows: (1)in this formula,is the discriminate result，is the gray value of the core of circular template， is the gray value of the arbitrary pixel in addition to the core of the template.Apply the formula (1) to each pixel, then the size of the USAN area can be expressed as: (2)In this formula, is the circular template whose core is [8].After getting the USAN area of all the pixels, then according to the following corner response function to generate the initial corner response: (3)In this formula, g is the geometric threshold value of the noise suppression, which decides the maximum value of output corners’ USAN area.SUSAN algorithms do not need the beforehand edge detection, which avoid the calculation of the gradient and not depend on the results of image segmentation, with integral characteristics, good anti-noise performance, and is not influenced by the type of the corner point, so it is widely used in image processing. However, a fixed threshold value will also make the positioning accuracy is not satisfactory, easy to produce pseudo-response, or easy to lose the true corner point, the process of integration has also led to more time-consuming.III.Description of the Improved AlgorithmA. Corner Detection Based On Non-end LineUse the circular template in SUSAN algorithm ,in the circular template start a axle-box whose origin is the core, if the pixels at the axis positive direction are all located in the USAN outside, there is the pixels within the USAN in the negative direction, state the core as the end point at the direction of the axis ,state the axis as the end line of the core, otherwise state it as the non-end line, as shown in Fig. 2,is the end point at the direction of ,is the end line. Due to that there are no pixels at the negative direction of within the USAN, so is not the end point at the direction of ,is non-end line, as the same, is not the end point at the direction of , , , is non-end line ,is the end point at the direction of , but is not the end point at the direction of , is the end line, is the non-end line.By analysing of the fig.2, we can get the following laws: there are numerous end lines and non-end lines inner the circular template whose core is the corner point; there are numerous end lines inner the circular template whose core is the edge point; but there is only one non-end line, which is the axis down the direction of USAN area edge; there are numerous non-end lines, but there is no end line inner the circular template whose core is inner point. That is to say, the corner point has numerous end lines and non-end lines; the edge point has numerous end points, but there is only one non-end line; the inner point has numerous non-end line, but no end lines.Fig. 2 The end line and non-end line of the circular templateFig. 3 The corner inspection based on the number of the end linesAccording to the above rules, you can filter out some of the non-corner points by the way of judging the number of the end lines and non-end lines inner the circular mask, and generate an initial response of the corner points. In Fig. 3(a), eight straight lines which pass through the circle are distributed in the circular mask, it can be respectively stated as , it is the USAN area shown by the shaded area, Stated from the Fig., the number of the end lines and non-end lines are respectively five and three, according the above rules, we can judge O is the corner point . In actual detections, considering of a circular template including 37 pixels, shown in Fig.3(b), each edge pixel and the pixel on the symmetry of the core constitute a line straight passing through the core. for example, the two pixels numbered g constitute the straight line g-g, the same as Fig.3(a), all pixels were composed of eight straight lines. Using a circular template to scan the entire image, to calculate the number of the end lines and non-end lines in each template to produce a candidate corner points.In Fig. 3 (a) , the line constituted by two symmetrical edge pixels is (), the collection of the end lines within the round templates is and the collection of the non-end lines is , then the judging formula of the end line is: (4)In this formula, 、 respectively stands for the similarity values of the two symmetrical edge pixels with the core ,and is the non-end line rejection threshold. For easy to calculate, the definition of the degree of the end line is: (5)Then the number of end line within a circular template is: (6)So the initial response function of the corner point is: (7)In this formula, is the geometric threshold for restraining edge points.Only the relationship between the edge pixel and the core is considered in the introduction of the new algorithms, which will generate some pseudo-response at the corner points, a number of suppression methods should be adopted to filter, but based on that the calculation amount can be greatly reduced because of the edge points’ initial response, which makes it more applicable for real-time applications.B. Non-maxima Suppression Based on Non-end Line’s uniformity coefficientAs a result of adapting the similarity between the edge pixels and core to generate the initial response of the corner points, pseudo-response will be produced in a certain range near the corner, as shown in Fig. 4, in the circular template 1, A corner points can be got by, , in the circular template 2, B corner points can be got by, , apparently B is a pseudo-response. The following non-maxima suppression method based on the non-end line’s uniformity can be adopted to filter the false responses.Fig. 4 The uniformity degree of the non-end lineAccording to Section 3.1, non-end line has two forms, the one is the direction line which is not passing through the USAN area (assuming that a small neighbor area near the core of the USAN area is negligible), the other is the direction line which is passing through the USAN area but the two intersection with the circular template is located outside USAN area. If a non-end lines do not pass through USAN region, we call it is uniform, or else call it a non-uniform, and according to the length of the segment inner USAN area to define the degree of non-uniform, the longer is, the greater the degree of non-uniform is, shown in Fig. 4, the degree of non-uniform 、 is greater than 、(, is uniform), it is easy to prove, the degree of non-uniform at the corner point is the greatest, but the degree of non-uniform at the non-corner point is the smallest, so according to this feature, pseudo-response suppression can be operated.While actually use it, first use the judging formula of the end lines to get the non-end lines, then through calculating the length of the line segment in the USAN region to get the degree of non-uniform, suppose the degree of non-uniform of the non-end line as ,so (8) is the non-end line which is passing through the core of the circular template, thus, the degree of non-uniform of all the non-end lines that have passed through is :(9)represent all the non-end lines that passes through the core of the circular template. Finally, the non-maxima suppression function is as follows: (10) in this formula, is the geometric value of pseudo-response suppression, which determines the sharp degree of the output corner points.IV. Experimental VerificationTo test the effectiveness of the new algorithm, this paper compared the improved algorithm with the present algorithm in accuracy and computational complexity .First, through adopting artificial typical corner-point image to test, shown as Fig. 5, (a) is the initial image, (b)~(d) is the effective image which respectively adopted Harris algorithm, SUSAN algorithm and the improved algorithm, through analysing, Harris algorithm has positioning bias in some corner points, there is also a small amount of pseudo-response, while SUSAN and the improved algorithm has greater effect. (a) Initial image (b) Harris (c) SUSAN (d) Improved algorithmFig. 5 The detection results in artificial corner detection of the improved algorithm and the initial algorithmFig. 6 is the detection test results of the three kinds of algorithm in medical image, you can see, Harris has stronger ability to process actual image processing ,but has a large number of pseudo-response points and there are few undetected corner. SUSAN has not very good processing effect to process actual image and has a lot of pseudo-response points. The new algorithm also has a little pseudo-response points, but on the whole there is a better detection result than the SUSAN algorithm .TableⅠ shows the average detection rate of the three algorithms in a variety samples. (a) Initial image (b) Harris (c) SUSAN (d) Improved algorithmFig. 6 The test results of the improved algorithm and the initial algorithm in medical imageTABLEⅠTHE AVERAGE DETECTION RATE OF THE THERE ALGORITHMWe can know from the former analysis of the algorithm, the improved algorithm do the complex calculations only on the most likely place for the corner points, but do little calculations on the easily judge where is the non-corner points, and the new algorithm is not based on a very abstract theory, which makes that the improved algorithm simple and easy to understand, with low computational complexity. The author adapted a variety samples to add up the detection time of the three algorithm using own computer, shown in the TableⅡ, we can know, the detection time of the improved algorithm is significantly lower than other algorithm.TABLEⅡ THE AVERAGE DETECTION TIME OF THE THERE ALGORITHMV.ConclusionsIn order to improve detection speed, the improved algorithm uses a progressive layer by layer mechanism, the basic idea is: first, through the most simple calculation to exclude certain non-corner points, followed the possible areas for the corner point will be implement the more sophisticated detection algorithm, due to the proportion of the corner point in image is small, this mechanism greatly increased the detection rate. For the pre-processing of the exclusion of the non-corner point, this paper proposed a method based on the smallest circular template with small amount of calculation, and can retain all the characteristics of corner points. In the corner detection algorithm, using the number characteristics of the corner points’ end line, and according to the non-end lines’ uniformity coefficient to perform non-maxima suppression, experiments show that the improved algorithm is effective in the speed and accuracy of calculation is better than the SUSAN method .ReferencesVanden P, Evert Jan D P, Viergever M, ”Medical image matching-a review with classification,” IEEE Engineering in Medicine and Biology, vol.12, no.1, pp. 26-39, January 1993.Jianhui Hou, Yi Lin, ” Harris checkerboard corner detection algorithm with self-adapting,”Computer Engineering and Design, vol.30, no.20, pp.4741 -4743, October 2009Harris C, Stephens M. A Combined Corner and Edge Detector. In Proceedings of the 4thAlvey Vision Conference, pp. 147-151,1988.Smith S M, Brady M. SUSAN, ”A new approach to low level image processing,” International Journal of Computer Vision, vol.23, no.1, pp.45-48, January 1997.Peng Zhou, Yong Tan, Shoushi Xu, ”A new image registration algorithm based on the corner detection ,”College journal of China University of Science and Technology, vol. 32, no. 4, pp. 455-461, August 2002. Bo Li, Dan Yang, Xiaohong Zhang,”A new image registration algorithm based on Harris multi-scale corner detection , ”Computer Engineering and Applications, no. 35, pp. 37-40, December 2006.Gang Huashun,Yi Zeng ,”A new image dense registration algorithm based on corner detection ,” Computer Engineering and Design, vol. 28, no.5, pp. 1092-1095, March 2007.Haixia Guo, Kai Xie.,” An improved corner detection algorithm based on USAN,”Computer Engineering, vol. 33, no. 22, pp. 232-234, November 2007.

A yawning detection method which can be used in drivers’ fatigue monitoring is proposed. To adapt to the variance in different mouth shapes and sizes, it based on mouth inner contour corner detection and curve fitting. First, the Harris corner detection algorithm was used to detect inner mouth feature points. Second, we established the open mouths’ mathematical model by curve fitting those points, calculated the degree of mouth openness using the mouth model, and generated the real-time M-curve. Third, the duration of big openness in successive images is divided into levels for further judgment. The validation results show that the method can obtain more precise mouth parameters and distinguish yawn from complex mouth activities. So the method achieves a higher level of accuracy.

In order to solve the inaccuracy and slowness of four-rotor aircraft pose estimation, a fast method is proposed. It uses FPGA+DSP as the hardware processing platform to reduce the overhead time and adopts the theory of Harris corner detection to gain the corner points’ coordinate and gray values. Then the pose of four-rotor aircraft can be calculated. The result of experiment shows that the method could estimate the pose of four-rotor aircraft accurately.

The applications of the remote image surveillance in the unattended substation were limited by the narrow bandwidth of wireless communication network. A novel method is proposed to adapt the wireless environment. The technique, implemented to obtain the digital instrument’s reading which is set as the ROI(region of interest) automatically, utilizes Harris corner detection. Then the ROI encoding in JPEG2000 technology is used in the image of the region of interest compressed at a higher quality than the rest of the image. The instrument’s reading information of unattended substation can be transmitted to dispatching center using wireless network effectively. Results show that the proposed method is feasible in terms of image quality and compression, adapting the narrow bandwidth, and improving the image of the real-time transmission.

Proposed matching method of bag’s images based on straight line features control in this paper. At first, it established a mathematical model of straight line features control theory; Secondly, it collected bags images by use of machine vision systems. It applied the contrast, image binarization, the first order derivative edge detection, dilation and erosion, image thinning, noise remove and other image processing technology and got the edge of the target object image. Next it used the Harris corner detection algorithm to get corners of edge image which is needed by straight linear control theory and achieve the features matching of two bag’s edge images. Finally, it processed vertical, rotating, tilt, moving and light changing’s bags’ images when putting bags stacked and side by side, the results show that the proposed matching method has strong robustness.