Current Issue : October - December Volume : 2014 Issue Number : 4 Articles : 5 Articles
Robotic machines are now of great interest for process automation. Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control and robot guidance in industry. Machine vision is concerned with the theory behind artificial systems that extract information from images and sequence of images. The purpose of this system is to relieve human inspectors of the tedious and inefficient task of looking of these defects. Here we first compared a standard image of steel coating with defective image, using subtraction algorithm that can highlight the main problem-regions. Our aim was to detect typical defect in bridge images like rust, hole, crack and fatigue....
As microfluidics has been applied extensively in many cell and biochemical applications, monitoring the related processes is\nan important requirement. In this work, we design and fabricate a high-throughput microfluidic device which contains 32\nmicrochambers to perform automated parallel microfluidic operations and monitoring on an automated stage of a microscope.\nImages are captured atmultiple spots on the device during the operations formonitoring samples in microchambers in parallel; yet\nthe device positions may vary at different time points throughout operations as the device moves back and forth on a motorized\nmicroscopic stage. Here, we report an image-based positioning strategy to realign the chamber position before every recording\nof microscopic image. We fabricate alignment marks at defined locations next to the chambers in the microfluidic device as\nreference positions. We also develop image processing algorithms to recognize the chamber positions in real-time, followed by\nrealigning the chambers to their preset positions in the captured images.We perform experiments to validate and characterize the\ndevice functionality and the automated realignment operation. Together, this microfluidic realignment strategy can be a platform\ntechnology to achieve precise positioning of multiple chambers for general microfluidic applications requiring long-term parallel\nmonitoring of cell and biochemical activities....
There is an increasing demand for automatic online detection system and computer vision plays a prominent role in this growing\nfield. In this paper, the automatic real-time detection system of the clamps based on machine vision is designed. It hardware is\ncomposed of a specific light source, a laser sensor, an industrial camera, a computer, and a rejecting mechanism. The camera\nstarts to capture an image of the clamp once triggered by the laser sensor. The image is then sent to the computer for defective\njudgment and location through gigabit Ethernet (GigE), after which the result will be sent to rejecting mechanism through RS485\nand the unqualified ones will be removed. Experiments on real-world images demonstrate that the pulse coupled neural network\ncan extract the defect region and judge defect. It can recognize any defect greater than 10 pixels under the speed of 2.8 clamps\nper second. Segmentations of various clamp images are implemented with the proposed approach and the experimental results\ndemonstrate its reliability and validity....
Fabric defect inspection is necessary and essential for quality control in the textile\nindustry. Traditionally, fabric inspection to assure textile quality is done by humans,\nhowever, in the past years, researchers have paid attention to PC-based automatic inspection\nsystems to improve the detection efficiency. This paper proposes a novel automatic\ninspection scheme for the warp knitting machine using smart visual sensors. The proposed\nsystem consists of multiple smart visual sensors and a controller. Each sensor can scan\n800 mm width of web, and can work independently. The following are considered in dealing\nwith broken-end defects caused by a single yarn: first, a smart visual sensor is composed of a\npowerful DSP processor and a 2-megapixel high definition image sensor. Second, a wavelet\ntransform is used to decompose fabric images, and an improved direct thresholding method\nbased on high frequency coefficients is proposed. Third, a proper template is chosen in a\nmathematical morphology filter to remove noise. Fourth, a defect detection algorithm is\noptimized to meet real-time demands. The proposed scheme has been running for six months\non a warp knitting machine in a textile factory. The actual operation shows that the system is\neffective, and its detection rate reaches 98%....
Removing noise without producing image distortion is the challenging goal for any image denoising filter. Thus, the different\namounts of residual noise and unwanted blur should be evaluated to analyze the actual performance of a denoising process. In\nthis paper a novel full-reference method for measuring such features in color images is presented. The proposed approach is based\non the decomposition of the normalized color difference (NCD) into three components that separately take into account different\nclasses of filtering errors such as the inaccuracy in filtering noise pulses, the inaccuracy in reducing Gaussian noise, and the amount\nof collateral distortion. Computer simulations show that the proposed method offers significant advantages over other measures of\nfiltering performance in the literature, including the recently proposed vector techniques....
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