Study of Pose Detection Algorithm for Specific Object Based on Monocular Vision

2014 ◽  
Vol 651-653 ◽  
pp. 517-523 ◽  
Author(s):  
Ling Yun Liu ◽  
Min Luo ◽  
Yue Min Wu
Keyword(s):  
Hausdorff Distance ◽  
Image Plane ◽  
Mathematic Model ◽  
Detection Algorithm ◽  
Monocular Vision ◽  
Threshold Method ◽  
Virtual Camera ◽  
Pose Detection ◽  
Specific Object ◽  
Vision Detection

This paper brings forward a monocular vision detection algorithm in allusion to the specific object’s pose based on Hausdorff Distance. At first, according to the mathematic model which has been established, the 2D template sequence is generated by projecting the specific object in different poses into the image plane of a virtual camera. Then, in order to predigest calculations and accelerate the matching speed during the image matching, the algorithm adopts the local-mean Hausdorff Distance as matching estimate and adopts the search strategy based on hiberarchy which reduces the searching rang by the threshold method before accurate match. In the end of this paper, the experiment that measures the poses of the clamp via the different clamp images respectively is given to testify the validity and speediness of this algorithm.

scholarly journals AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 771-777 ◽  
Author(s):  
Guoqing Zhou ◽  
Xiang Zhou ◽  
Tao Yue ◽  
Yilong Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Threshold Method ◽  
Dsp Implementation ◽  
Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

A Novel Method for Tramcar Anti-Collision Warning System Based on Monocular Vision

2012 ◽  
Vol 430-432 ◽  
pp. 1871-1876
Author(s):  
Hui Bo Bi ◽  
Xiao Dong Xian ◽  
Li Juan Huang
Keyword(s):  
Software Design ◽  
Early Warning System ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Monocular Vision ◽  
System Construction ◽  
Collision Warning ◽  
Lane Width ◽  
Novel Method

For the problem of tramcar collision accident in coal mine underground, a monocular vision-based tramcar anti-collision warning system based on ARM and FPGA was designed and implemented. In this paper, we present an improved fast lane detection algorithm based on Hough transform. Besides, a new distance measurement and early-warning system based on the invariance of the lane width is proposed. System construction, hardware architecture and software design are given in detail. The experiment results show that the precision and speed of the system can satisfy the application requirement.

scholarly journals Pothole Classification Model Using Edge Detection in Road Image

2020 ◽  
Vol 10 (19) ◽  
pp. 6662
Author(s):  
Ji-Won Baek ◽  
Kyungyong Chung
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Image Data ◽  
Structural Similarity ◽  
Detection Algorithm ◽  
Classification Model ◽  
Specific Object ◽  
Pixel Value ◽  
Distortion Rate

Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

Lane Marking Detection Based on Segments with Upper and Lower Structure

2019 ◽  
Vol 34 (02) ◽  
pp. 2055005
Author(s):  
Junjie Huang ◽  
Zhiling Wang ◽  
Huawei Liang ◽  
Linglong Lin ◽  
Biao Yu ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Urban Traffic ◽  
Middle Level ◽  
Threshold Method ◽  
Line Segments ◽  
Lane Marking ◽  
Lane Departure ◽  
Lower Structure ◽  
Multiple Disturbance ◽  
High Level

An effective and accurate lane marking detection algorithm is a fundamental element of the intelligent vehicle system and the advanced driver assistant system, which can provide important information to ensure the vehicle runs in the lane or warn the driver in case of lane departure. However, in the complex urban environment, lane markings are always affected by illumination, shadow, rut, water, other vehicles, abandoned old lane markings and non-lane markings, etc. Meanwhile, the lane markings are weak caused by hard use over time. The dash and curve lane marking detection is also a challenge. In this paper, a new lane marking detection algorithm for urban traffic is proposed. In the low-level phase, an iterative adaptive threshold method is used for image segmentation, which is especially suitable for the blurred and weakened lane markings caused by low illumination or wear. In the middle-level phase, the algorithm clusters the candidate pixels into line segments, and the upper and lower structure is used to cluster the line segments into candidate lanes, which is more suitable for curve and dashed lane markings. In the high-level phase, we compute the highest scores to get the two optimal lane markings. The optimal strategy can exclude interference similar to lane markings. We test our algorithm on Future Challenge TSD-Lane dataset and KITTI UM dataset. The results show our algorithm can effectively detect lane markings under multiple disturbance, occlusions and sharp curves.

scholarly journals A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots

Sensors ◽  
2016 ◽  
Vol 16 (3) ◽  
pp. 311 ◽  
Author(s):  
Tae-Jae Lee ◽  
Dong-Hoon Yi ◽  
Dong-Il Cho
Keyword(s):  
Autonomous Robots ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Vision Sensor

A Monocular Vision Measurement Algorithm Based on the Underwater Robot

2014 ◽  
Vol 532 ◽  
pp. 165-169 ◽  
Author(s):  
Tao Liu ◽  
Lei Wan ◽  
Xing Wei Liang
Keyword(s):  
Measurement System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Mathematic Model ◽  
Monocular Vision ◽  
Underwater Robot ◽  
Visual Measurement ◽  
Vision Measurement ◽  
Measurement Algorithm

A monocular vision measurement system was proposed based on the underwater robot, which used a single camera as visual sensor and used inertial navigation system to measure position and attitude. In this paper, the processing of underwater target images and the parameter calibration algorithm for visual measurement system were discussed, and then the visual measurement mathematic model of underwater robot was derived using the pose information of the inertial navigation system. Vision measuring principle validation tests for underwater robot have been carried out to detect underwater targets. The experiments verify that the monocular vision measurement algorithm proposed for underwater robot is effective with a preferably accuracy.

A Method of Determining the Pose of Planar Target in Vision Detection System

2013 ◽  
Vol 659 ◽  
pp. 156-161
Author(s):  
Sheng Nan Zhang ◽  
Bo Shi Wang ◽  
Wei Qi Yuan
Keyword(s):  
Detection System ◽  
Relative Displacement ◽  
Monocular Vision ◽  
Depth Information ◽  
Planar Target ◽  
Vision Detection ◽  
Ccd Image ◽  
The Relationship ◽  
Curve Equation ◽  
Independent Parameters

Planar target is a location reference that is widely used in vision detection system. Based on monocular vision and circular markers on the planar target, a new method of determining the pose of target spatial plane is proposed. Since CCD image of a circle is an ellipse ,determining the pose of spatial plane may be transformed into determining the pose of ellipse. Ellipse curve equation of circle’s image is deduced and the independent parameters of ellipse are obtained, the relationship between spatial plane and ellipse parameters is established, thus the pose of spatial plane can be determined. By calculating the relative displacement with the standard plane that its depth information is known, the depth information from the center of ellipse image to the camera coordinate system is obtained. The algorithm is simple and fast, and it can be widely used in monocular vision detection.

Multi-Frame Image Fusion Method Combining Spatial-Temporal Saliency Detection and NSCT

2011 ◽  
Vol 403-408 ◽  
pp. 1927-1932
Author(s):  
Hai Peng ◽  
Hua Jun Feng ◽  
Ju Feng Zhao ◽  
Zhi Hai Xu ◽  
Qi Li ◽  
...  
Keyword(s):  
Image Fusion ◽  
Feature Detection ◽  
Saliency Detection ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Image Sequences ◽  
Fusion Method ◽  
Threshold Method ◽  
Salient Features ◽  
Image Fusion Method

We propose a new image fusion method to fuse the frames of infrared and visual image sequences more effectively. In our method, we introduce an improved salient feature detection algorithm to achieve the saliency map of the original frames. This improved method can detect not only spatially but also temporally salient features using dynamic information of inter-frames. Images are then segmented into target regions and background regions based on saliency distribution. We formulate fusion rules for different regions using a double threshold method and finally fuse the image frames in NSCT multi-scale domain. Comparison of different methods shows that our result is a more effective one to stress salient features of target regions and maintain details of background regions from the original image sequences.

Sign in / Sign up

Export Citation Format

Share Document