Automated Machine Vision System Research Articles

Detection of Foreign Materials in Wheat Kernels Using Regional Colour Descriptors

Present paper reports the development of an automated machine vision system for detection of foreign materials in wheat kernels using regional color descriptors. The said system was executed in the form of an integrated flowing pipeline after having proper choice of different possible alternatives at different stages of image processing. A new type of surface colour descriptor is also proposed in this work to define wheat kernel uniquely. The fifteen-element colour descriptor is executed after having thorough comparison of six different colour spaces, each having 72 separate quantifiable components. The fifteen elements of the proposed colour-descriptor, extracted from each segmented region of the sample image, are concatenated in the form of an input to the neural classifier. The neural classifier is trained with Levenberg-Marquardt (LM) learning algorithm to achieve extremely fast convergence. The recognition rate of the executed classifier is found to be more than 99.2% for detection of impurity in unconnected wheat kernels. The results of present investigations are quite promising. The proposed pipeline has potential future in the field of machine vision based quality inspection of wheat and other cereal grains.

Design and implementation of a smart system for water management of lilium flower using image processing

Agriculture is one of the largest and most important water consuming sector in the world. More than 80% of water resources are wasted in this sector because of the lack of advanced technology. Therefore, by saving water and controlling the growth conditions of the plant simultaneously, a low level of water consumption can be achieved. In this research, an automated machine vision system was designed and developed to determine the water requirement of the Lilium plant. Therefore, the average values of each RGB, Lab and HSV channel of leaves and stem along with curvature, length and orientation of stem were extracted. After that, features were analyzed using Duncan's multiple range test and the linguistic hedges feature selection algorithm combined with adaptive neuro-fuzzy classifier. According to the results, Hs, curvature, stem angle, HL, BL and LL were the most significant features and the classifier accuracy for four plant conditions (100% of field capacity (FC), 80% of FC, 60% of FC and 40% of FC) were 84.84, 84.21, 86.95 and 80%, respectively. The results showed that the proposed system has the ability to determine the levels of water stress and provide the amount of water requirement.

Automated Machine Vision System for Liquid Particle Inspection of Pharmaceutical Injection

The particle matter inspection for pharmaceutical injection is inevitable in the field of pharmaceutical manufacturing, as it has the direct impact on the quality of the drugs. It is a challenge to inspect the contaminated injection online using an imaging system. This paper introduces a novel and effective inspection machine consisting of three modules, a mechanical system with 120 carousel grips, an image acquisition system with multihigh resolution cameras and a multilight sources station, and a distributed industrial electrical computer control system. Particle visual inspection machine first acquires image sequence using the high-speed image acquisition system. The image capture process at each camera module is alternately synchronized with different LED illumination techniques (light transmission method and light reflection method), enabling independent capture of particle images from the same container. Then, a set of novel algorithms for image registration and fast segmentation are proposed to minimize false rejections even in sensitive conditions, which enable the identification of all the tiny potential defects. Finally, a particle tracking and classification algorithm based on an adaptive local weighted-collaborative sparse model is also presented. The experiments demonstrate that the proposed inspection system can effectively detect the particles in the pharmaceutical infusion solution online, and achieve a performance rate of above 97% average accuracy.

Automated Machine Vision System for Inspecting Cutting Quality of Cubic Zirconia

In this paper, we present an automated system for the visual inspection of cubic zirconia (CZ) cut quality. In particular, we inspect the cut quality from pavilion facets of the CZ. For the hardware, the system includes a computerized-control mechanical part that performs both the task of feeding the CZ to the inspection station and the task of separating the gemstone according to the inspection result. In terms of software, we propose an image processing algorithm that consists of two major steps. For the first step, pavilion facets are extracted from the CZ image acquired from the pavilion side. In particular, we resort to the idea of 1-D edge detection in conjunction with random sample consensus line fitting for the pavilion facet extraction. For the second step, a set of measures derived from the extracted facet structure are calculated and are used for cut quality judgment as either accept or reject. The metrological analysis of the system is also investigated. We perform an experiment to inspect 1756 object images consisting of both good and bad samples. The performance of our system yields to about 5.21% of false reject rate and 0% of false acceptance rate. The system can inspect CZ with a rate of 1 sample/s.

Classification of crops and weeds from digital images: A support vector machine approach

In most agricultural systems, one of the major concerns is to reduce the growth of weeds. In most cases, removal of the weed population in agricultural fields involves the application of chemical herbicides, which has had successes in increasing both crop productivity and quality. However, concerns regarding the environmental and economic impacts of excessive herbicide applications have prompted increasing interests in seeking alternative weed control approaches. An automated machine vision system that can distinguish crops and weeds in digital images can be a potentially cost-effective alternative to reduce the excessive use of herbicides. In other words, instead of applying herbicides uniformly on the field, a real-time system can be used by identifying and spraying only the weeds. This paper investigates the use of a machine-learning algorithm called support vector machine (SVM) for the effective classification of crops and weeds in digital images. Our objective is to evaluate if a satisfactory classification rate can be obtained when SVM is used as the classification model in an automated weed control system. In our experiments, a total of fourteen features that characterize crops and weeds in images were tested to find the optimal combination of features that provides the highest classification rate. Analysis of the results reveals that SVM achieves above 97% accuracy over a set of 224 test images. Importantly, there is no misclassification of crops as weeds and vice versa.

An Automated Machine Vision System for Classification of Seeds Using Color Features

An Automated Machine Vision System for Classification of Seeds Using Color Features

High-accuracy automatic machine vision based calibration of micrometers

The calibration of simple handheld instruments is often more expensive than the price of a new device. Therefore, the amount of manual labour is kept at a minimum in order to keep the price of calibration at a tolerable level. This also means that only a few points of e.g. a length scale can be checked. By using automatic machine vision based systems, the calibration of measurement instruments can be done faster and more thoroughly. In order to study the possibilities of machine vision automation for volume calibration tasks a set-up for micrometer calibration was constructed at Centre for Metrology and Accreditation (MIKES). With the developed automated machine vision system it is possible to check hundreds of points on the scale of a micrometer, giving new insight into error sources of the micrometer screw. The attained uncertainty is at the same level as calibration with gauge blocks according to ISO 3611.

Discrimination and classification of fresh-cut starfruits ( Averrhoa carambola L.) using automated machine vision system

Software for detecting the quality features of golden delicious starfruits of Averrhoa carambola L. genus were developed for automated inspection system using machine vision technology. The features considered were colour and shape. The use of artificial classifiers such as linear discrimination analysis and multi-layer perceptron neural network as a tool to detect starfruit maturities such as unripe, underripe, ripe and overripe in HSI colour space were investigated. The colour spectra of matured and unmatured fruits were characterised using all colour features ranging from hue 10 to hue 74, and, using principal hues generated by Wilks-lambda analysis. Experiments performed on 200 independent starfruit samples revealed that linear discriminant analysis after Wilks-lambda analysis was more precise in classification than direct application of linear discriminant analysis. However, the classification accuracy of multi-layer preceptron remained relatively the same before and after feature reduction. Overall, the average correct classification for linear discriminant analysis and multi-layer preceptron was 95.3% and 90.5% respectively during testing stage. Meanwhile the use of Fourier transform was investigated for shape discrimination. This algorithm produced 100% success rate in detecting starfruits by three shape categories: well-formed, slightly deformed and seriously deformed. Both colour and shape analysis was easily affected by the variation of lighting levels, and this contributed to the major classification error.

Development of machine vision system and dimensional analysis of the automobile front-chassis-module

In the present research work, an automated machine vision system and a new algorithm to interpret the inspection data has been developed. In the past, the control of tolerance of front-chassis-module was done manually. In the present work a machine vision system and required algorithm was developed to carryout dimensional evaluation automatically. The present system is used to verify whether the automobile front-chassis-module is within the tolerance limit or not. The directional ability parameters related with front-chassis-module such as camber, caster, toe and king-pin angle are also determined using the present algorithm. The above mentioned parameters are evaluated by the pose of interlinks in the assembly of an automobile front-chassis-module. The location of ball-joint center is important factor to determine these parameters. A method to determine the location of ball-joint center using geometric features is also suggested in this paper. In the present work a 3-D best fitting method is used for determining the relationship between nominal design coordinate system and the corresponding feature coordinate system.

DETECTION OF SPLENOMEGALY IN POULTRY CARCASSES BY UV AND COLOR IMAGING

Splenomegaly (spleen enlargement) is one indication that processed poultry is not acceptable for humanconsumption because of diseases such as tumors or septicemia. This study explored the possibility of detectingsplenomegaly with a computer imaging system that will assist human inspectors in food safety inspections. Images ofinternal viscera from 45-day-old commercial turkeys were taken with fluorescent and UV lighting systems. Imageprocessing algorithms using the linear transformation, morphological filtering, and statistical classification weredeveloped to distinguish the spleen from its background surroundings and then to detect abnormalities. Experimentalresults demonstrated that the imaging method could effectively distinguish the spleen from other organs and intestines.Based on a total of 57 turkey sample images, correct classification rates of 92% and 95% in detection of spleenabnormality were obtained using a self test set and an independent test set, respectively. The methods indicated thefeasibility of using automated machine vision systems in the future to inspect internal organs and check thewholesomeness of poultry carcasses.

AUTOMATED SORTING OF PISTACHIO NUTS WITH CLOSED SHELLS

An automated machine vision system was developed to identify and remove pistachio nuts with closed shells from processing streams. The system includes a novel material handling system to feed nuts to linescan cameras without tumbling. The camera output signals are input to digital signal processing boards which extract image features characteristic of closed and open shell pistachios. The classification accuracy of this machine vision system for separating open shell from closed shell nuts is approximately 95%. The system has a throughput rate of approximately 40 nuts per second and cost of about $15,000 per channel. This throughput and cost is comparable to color sorters used to remove other pistachio defects. Currently, closed shell pistachio nuts are removed by mechanical devices. These devices also have a classification accuracy of about 95% but damage kernels in open shell pistachios by “pricking” them with a needle. The needle hole can give the appearance of an insect tunnel and cause rejection by the consumer. The newly developed machine vision system is a non-invasive inspection method.