A Review : Classification of Food Grains and Quality Prediction

This review paper discuss about recent techniques and methods used for grain classification and grading. Grains are important source of nutrients and they play important role in healthy diet. The production of grains across worldwide each year is in terms of hundreds of millions. The common method to classify these hugely produced grains is manual which is mind-numbing and not accurate. So the automated system is required which can classify the verities and predict the quality (i.e. grade A, grade B) of grain fast and accurate. As machine learning had done most of the difficult things easy, many machine learning algorithms can be used which can easily classify and predict the quality of grains. The system uses colour and geometrical features like size and area of grains as attributes for classification and quality prediction. Here, several image procession methods and machine learning algorithms are reviewed.

Anti-Collision System on Embedded Visual Distance Measurement Based on ARM

In the actual operation by crane operator, there will appear situation that operator cannot make timely judgment on distance between crane and adjacent obstacle and cause collision accident. For this problem, this paper establishes anti-collision system on distance measurement focuses on ARM 11, on the basis based on machine vision and image procession, it considers effect of corresponding point match and extraction deviation of single characteristics point on measurement result, adopts one kind of method of distance measurement by monocular vision based on single characteristic point. The test result indicates that this system can effectively measure distance with obstacle, it has accurate measurement, high reliability and it can meet real-time requirement.

The Video Device to Image the Side of Deep Pipe

The imaging of the side of deep pipe is key problem of the detection implement in industry. With the help of tailor-made lamp-house, the side image of the deep pipe is detected by the CMOS chip. The CMOS chip is a little view field and large focal length, and it could convey the side information to the processor-SQ606. The image procession and photo and video files manipulation are fulfilled. With the telescopic barrels, the image of the side of deep pipe is satisfied.

A Design of Path Planning System for UP6 Industrial Robot Based on Machine Vision

This paper introduces operational path planning system of visual industrial robot, which could be applied to arc welding, cutting and gluing. It depicts a calibration method of camera coordinate. It gains environmental messages via the method of image acquisition. It uses the method of image procession to process the signals acquired. It fulfills robotic real-time control and offline programming. Practice has proved that this system has high repeatability precision, which can meet the challenge of arc welding, cutting and gluing, and also meet the needs of production line.

Based on Image Procession to Measure Flame Emission Characteristic and Radioactive Properties

radioactive properties are hard to be given exactly by the existing methods. In this paper, multiple color image detectors were used to capture approximately red, green, and blue monochromatic radioactive intensity images in the visible wavelength region, and the flame emissive and the radioactive properties of the particulate media in three pulverized-coal-fired boiler furnaces were got from the flame images.

Study of Precise Measurement Algorithm Based on Machine Vision

An precise measurement algorithm based on machine is presented, first, salt and pepper noise has been pushed into original image, then the image with noise was under a median filtering，output image is well preprocessed, and distortion in image has been corrected by Camera Distortion Correction. The corrected image was detected by Canny operator on its edge then the image’s edge was extracted by the maximum variance method on certain threshold, and the output image is showed in gray level, then the gray level was under interpolation computation by bilinear interpolation function to make its edge be detected at sub-pixel level. In this paper, the combination of image procession and camera calibration is practically and effectively fulfilled the precision requirement of the system.