Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
The observation, decomposition and record of motion are usually accomplished\nthrough artificial means during the process of motion analysis. This method not only\nhas a heavy workload, its efficiency is also very low. To solve this problem, this paper\nproposes a novel method to segment and recognize continuous human motion\nautomatically based on machine vision for mechanical assembly operation. First, the\ncontent-based dynamic key frame extraction technology was utilized to extract key\nframes from video stream, and then automatic segmentation of action was implemented.\nFurther, the SIFT feature points of the region of interest (ROIs) were extracted,\non the basis of which the characteristic vector of the key frame was derived. The\nfeature vector can be used not only to represent the characteristic of motion, but also\nto describe the connection between motion and environment. Finally, the classifier is\nconstructed based on support vector machine (SVM) to classify feature vectors, and\nthe type of therblig is identified according to the classification results. Our approach\nenables robust therblig recognition in challenging situations (such as changing of\nlight intensity, dynamic backgrounds) and allows automatic segmentation of motion\nsequences. Experimental results demonstrate that our approach achieves recognition\nrates of 96.00 % on sample video which captured on the assembly line....
The growth in production of Android devices has resulted in greater functionalities as well as lower costs. This has made previously more expensive systems such as night vision\n afordable for more businesses and end users. We designed and implemented robust and low cost night vision systems based on red-green-blue (RGB) colour histogram for a static camera as well as a camera on an unmanned aerial vehicle (UAV), using OpenCV library on Intel compatible notebook computers, running Ubuntu Linux operating system, with less than 8GB of RAM. They were tested against human intruders under low light conditions (indoor, outdoor, night\n time) and were shown to have successfully detected the intruders....
Most of raw materials of small hardware processing for plate scraps, and it�s realized through the\nmanual operation of ordinary punch, which way has the low production efficiency and the high labor intensity.\nIn order to improve the automation level of production, developing and designing of a visual processing system\nfor punch press manipulator which based on the MFC tools of Visual Studio software platform. Through the\nimage acquisition and image processing, get the information about the board to be processed, such as shape,\nlength, the center of gravity position and pose, and providing relevant parameters for positioning gripping and\nplacing into the punch table positioning of the feeding manipulator and automatic programming of punching\nmachine, so as to realize the automatic operation about press feeding and processing....
Low visibility is one of the reasons for rear accident at night. In this paper, we propose a method to detect the leading vehicle based\nonmultisensor to decrease rear accidents at night.Then, we use image enhancement algorithm to improve the human vision. First,\nby millimeter wave radar to get the world coordinate of the preceding vehicles and establish the transformation of the relationship\nbetween the world coordinate and image pixels coordinate, we can convert the world coordinates of the radar target to image\ncoordinate in order to form the region of interesting image. And then, by using the image processing method, we can reduce\ninterference from the outside environment. Depending on D-S evidence theory, we can achieve a general value of reliability to test\nvehicles of interest. The experimental results show that the method can effectively eliminate the influence of illumination condition\nat night, accurately detect leading vehicles, and determine their location and accurate positioning. In order to improve nighttime\ndriving, the driver shortage vision, reduce rear-end accident. Enhancing nighttime color image by three algorithms, a comparative\nstudy and evaluation by three algorithms are presented. The evaluation demonstrates that results after image enhancement satisfy\nthe human visual habits....
This paper presents a method for\nsynthesizing a stroboscopic image of a moving sports\nplayer from a hand-held camera sequence. This method\nhas three steps: synthesis of background image,\nsynthesis of stroboscopic image, and removal of player�s\nshadow. In synthesis of background image step, all\ninput frames masked a bounding box of the player are\nstitched together to generate a background image. The\nplayer is extracted by an HOG-based people detector.\nIn synthesis of stroboscopic image step, the background\nimage, the input frame, and a mask of the player\nsynthesize a stroboscopic image. In removal of shadow\nstep, we remove the player�s shadow which negatively\naffects an analysis by using mean-shift. In our previous\nwork, synthesis of background image has been timeconsuming.\nIn this paper, by using the bounding box\nof the player detected by HOG and by subtracting the\nimages for synthesizing a mask, computational speed\nand accuracy can be improved. These have contributed\ngreatly to the improvement from the previous method.\nThese are main improvements and novelty points from\nour previous method. In experiments, we confirmed\nthe effectiveness of the proposed method, measured\nthe player�s speed and stride length, and made a\nfootprint image. The image sequence was captured\nunder a simple condition that no other people were in\nthe background and the person controlling the video\ncamera was standing still, such like a motion parallax\nwas not occurred. In addition, we applied the synthesis\nmethod to various scenes to confirm its versatility....
Loading....