Machine Vision Intelligence Research Articles

Застосування технології глибинного навчання для побудови інтелектуальних автономних машин

One of the most common tasks that arise in building intelligent machine vision systems for intellectually autonomous machines is the problems of classification and regression. Classification problems are used for the reflexive action of autonomous machines. Prediction tasks can be used to build machine vision systems to provide intelligent autonomous machines with environmental knowledge, which in turn is important for planned predictable movements. Defining a class of task instances is an important procedure for the effective design of deep learning systems. In this context, the possibility of using a multilayered neural network as a regressor to construct elementary functional mappings is explored for further prediction. The study outlines the peculiarities of functioning and configuration of a specialized robotics system, considered in this paper as an intelligent autonomous machine or physical agent, generates a set of data points for elementary functions, analytical modeling and modeling of training systems. Input graph was constructed, neural network architecture was defined, gradient descent algorithm was implemented, and output schedules were finally constructed: learning process, results prediction and comparative graph of predicted results superimposed on the input graph. As a result of the study, an assessment of the machine's intellectual ability to predict was made.

Pixel-wise Colorimetric Characterization based on U-Net Convolutional Network

Abstract In this article, we present a U-Net convolutional network for solving insufficient data problems of color patches in colorimetric characterization. The U-Net network uses data augmentation annotated over 6,885,222 colors, 32,027,200 color patches, and 2,098 billion pixels directly from only eight standard colorimetric images of ISO 12640 (CIELAB/SCID). By applying the U-Net network trained on big augmented data, the pixel-wise colorimetric characterization is implemented from digitalized red, green, blue image samples to ISO 12640 (CIELAB/SCID) CIELAB standard colorimetric images. The performance efficiency of the U-Net network is superior to that of the convolutional neural network on both training and validating epochs. Moreover, pixel-wise color colorimetric characterization is achieved using the intelligent machine vision of U-Net integrated with a data augmentation technique to overcome the drawback of complex color patches and labor-intensive tasks. This study might improve colorimetric characterization technology with a resolution of 2560-by-2048 for over 4 million pixels. The results reveal that U-net with pixel-wise regression enhances the precise colors of images, taking detail and realism to a new level.

Estimation of Wind Turbine Angular Velocity Remotely Found on Video Mining and Convolutional Neural Network

Today, energy issues are more important than ever. Because of the importance of environmental concerns, clean and renewable energies such as wind power have been most welcomed globally, especially in developing countries. Worldwide development of these technologies leads to the use of intelligent systems for monitoring and maintenance purposes. Besides, deep learning as a new area of machine learning is sharply developing. Its strong performance in computer vision problems has conducted us to provide a high accuracy intelligent machine vision system based on deep learning to estimate the wind turbine angular velocity, remotely. This velocity along with other information such as pitch angle and yaw angle can be used to estimate the wind farm energy production. For this purpose, we have used SSD (Single Shot Multi-Box Detector) object detection algorithm and some specific classification methods based on DenseNet, SqueezeNet, ResNet50, and InceptionV3 models. The results indicate that the proposed system can estimate rotational speed with about 99.05 % accuracy.

Interactive design of intelligent machine vision based on human–computer interaction mode

The intelligent machine vision technology based on man-machine interaction mode has the advantages of weak intrusion, low adhesion and no device binding. With the development and progress of science and technology, the intelligent machine vision technology has become one of the most important directions in the field of human–computer interaction. Compared with the traditional interactive mode, the intelligent machine vision interaction technology is quite convenient, and the existence of these problems can also influence the vision of the intelligent machine to a certain extent. And the wide application of the sense interaction. At present, the operation precision is the main basis of the machine vision interaction design. Therefore, this paper studies the precision of the visual interaction of the intelligent machine. In this paper, three point-to-click experiments will be carried out on the basis of the Fitts' Law. The accuracy of the machine vision algorithm is experimentally studied from the operating direction, the mapping scale equation and the machine vision algorithm, and a reasonable suggestion is made for the intelligent machine vision interactive design of the human interaction mode.

An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

Scientific issues that need to be addressed for a successful read-across prediction

This work aims to explore the robot automatic navigation model under computer intelligent algorithms and machine vision, so that mobile robots can better serve all walks of life. In view of the current situation of high cost and poor work flexibility of intelligent robots, this work innovatively researches and improves the image processing algorithm and control algorithm. In the navigation line edge detection stage, aiming at the low efficiency of the traditional ant colony algorithm, the Canny algorithm is combined to improve it, and a Canny-based ant colony algorithm is proposed to detect the trajectory edge. In addition, the Single Shot MultiBox Detector (SSD) algorithm is adopted to detect obstacles in the navigation trajectory of the robot. The performance is analyzed through simulation. The results show that the navigation accuracy of the Canny-based ant colony algorithm proposed in this work is basically stable at 89.62%, and its running time is the shortest. Further analysis of the proposed SSD neural network through comparison with other neural networks suggests that its feature recognition accuracy reaches 92.90%. The accuracy is at least 3.74% higher versus other neural network algorithms, the running time is stable at about 37.99 s, and the packet loss rate is close to 0. Therefore, the constructed mobile robot automatic navigation model can achieve high recognition accuracy under the premise of ensuring error. Moreover, the data transmission effect is ideal. It can provide experimental basis for the later promotion and adoption of mobile robots in various fields.

Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process

Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.

Employing Type-2 Fuzzy Logic Systems in the Efforts to Realize Ambient Intelligent Environments [Application Notes

Ambient Intelligence (AmI) is an emerging vision that aims to realize intelligent environments which are sensitive and responsive to the users' needs and behaviors. This paper presents an insight on the benefits that type-2 Fuzzy Logic Systems (FLSs) can offer towards the efforts to realize Ambient Intelligent Environments (AIEs). We will introduce research results from the Scaleup project showing different type-2 FLSs based applications in AIEs. Such applications include intelligent machine vision systems, blending real and virtual realities over dispersed geographical areas and allowing natural communication between the AIE and humans.

Applications of Intelligent Machine Vision in Plant Factory

Intelligent machine vision has been widely used in plant factory for many purposes. There are two aims in this study i.e. the first is improving the performance of intelligent machine vision for precision irrigation system using optimized feature selection technique and the second is developing intelligent machine vision for precision artificial lighting system using Light Emitting Diode (LED). The proposed feature selection technique used in the first aim is Neural-Discrete Hungry Roach Infestation Optimization (N-DHRIO) algorithm. The intelligent machine vision for precision irrigation system and the precision LED lighting system have successfully been developed, and it shows effective to control moisture content and light intensity of the plant precisely. In large scale plant factory, those systems can optimize plant growth and reduce the water consumption and energy costs.

From Pixels to Region: A Salient Region Detection Algorithm for Location-Quantification Image

Image saliency detection has become increasingly important with the development of intelligent identification and machine vision technology. This process is essential for many image processing algorithms such as image retrieval, image segmentation, image recognition, and adaptive image compression. We propose a salient region detection algorithm for full-resolution images. This algorithm analyzes the randomness and correlation of image pixels and pixel-to-region saliency computation mechanism. The algorithm first obtains points with more saliency probability by using the improved smallest univalue segment assimilating nucleus operator. It then reconstructs the entire saliency region detection by taking these points as reference and combining them with image spatial color distribution, as well as regional and global contrasts. The results for subjective and objective image saliency detection show that the proposed algorithm exhibits outstanding performance in terms of technology indices such as precision and recall rates.

Target Tracking Feature Selection Algorithm Based on Adaboost

With the development of image processing technology and popularization of computer technology, intelligent machine vision technology has a wide range of application in the medical, military, industrial and other fields. Target tracking feature selection algorithm is one of research focuses in the machine intelligent vision technology. Therefore, to design the target tracking feature selection algorithm with high accuracy and good stability is extremely necessary. This paper presents a target tracking feature selection algorithm based on Adaboost. It includes Adaboost algorithm’s principle and Adaboost algorithm's application in video object tracking. Experimental results show that the proposed algorithm has the characteristics of real-time, accuracy and stability. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3688

A machine learning based intelligent vision system for autonomous object detection and recognition

Existing object recognition techniques often rely on human labeled data conducting to severe limitations to design a fully autonomous machine vision system. In this work, we present an intelligent machine vision system able to learn autonomously individual objects present in real environment. This system relies on salient object detection. In its design, we were inspired by early processing stages of human visual system. In this context we suggest a novel fast algorithm for visually salient object detection, robust to real-world illumination conditions. Then we use it to extract salient objects which can be efficiently used for training the machine learning-based object detection and recognition unit of the proposed system. We provide results of our salient object detection algorithm on MSRA Salient Object Database benchmark comparing its quality with other state-of-the-art approaches. The proposed system has been implemented on a humanoid robot, increasing its autonomy in learning and interaction with humans. We report and discuss the obtained results, validating the proposed concepts.

Self-organizing maps for texture classification

A further investigation of our intelligent machine vision system for pattern recognition and texture image classification is discussed in this paper. A data set of 335 texture images is to be classified into several classes, based on their texture similarities, while no a priori human vision expert knowledge about the classes is available. Hence, unsupervised learning and self-organizing maps (SOM) neural networks are used for solving the classification problem. Nevertheless, in some of the experiments, a supervised texture analysis method is also considered for comparison purposes. Four major experiments are conducted: in the first one, classifiers are trained using all the extracted features without any statistical preprocessing; in the second simulation, the available features are normalized before being fed to a classifier; in the third experiment, the trained classifiers use linear transformations of the original features, received after preprocessing with principal component analysis; and in the last one, transforms of the features obtained after applying linear discriminant analysis are used. During the simulation, each test is performed 50 times implementing the proposed algorithm. Results from the employed unsupervised learning, after training, testing, and validation of the SOMs, are analyzed and critically compared with results from other authors.

Intelligent Visual Recognition and Classification of Cork Tiles With Neural Networks

An intelligent machine vision system is investigated and used for pattern recognition and classification of seven different types of cork tiles. The system includes image acquisition with a charge-coupled device (CCD) camera, texture feature generation (co-occurrence matrices and Laws' masks), analysis and processing of the feature vectors [linear discriminant analysis (LDA) and principal component analysis (PCA)], and cork tiles classification with feedforward neural networks (NN), employing our GLP(tau) S (genetic low-discrepancy search) hybrid global optimization method. In addition, the same NN are trained with backpropagation (BP) and the obtained results are compared with the ones from GLP(tau) S . The NN generalization abilities are discussed and assessed with respect to the NN architectures and the texture feature sets. The reported results are very encouraging with testing rate reaching up to 95%.

An autonomous phase-boundary detection technique for colloidal hard sphere suspension experiments

Colloidal suspensions of monodisperse spheres are used as physical models of thermodynamic phase transitions and as precursors to photonic band gap materials. Current techniques for identifying the phase boundaries involve manually identifying the phase transitions, which is very tedious and time-consuming. In addition, current image analysis techniques are not able to distinguish between densely packed phases within conventional microscope images, which are mainly characterized by degrees of randomness or order with similar grayscale value properties. We have developed an intelligent machine vision technique that automatically identifies colloidal phase boundaries. The technique utilizes intelligent image processing algorithms that accurately identify and track phase changes vertically or horizontally for a sequence of colloidal hard sphere suspension images. This technique is readily adaptable to any imaging application wherein regions of interest are distinguished from the background by differing patterns of motion over time.

Multi-Spectrum Image Sensor for Detecting Crop Status by Robot Tractor

Sensors are an essential part of intelligent agricultural machinery. Machine vision, in particular, can supply information about current crop status, including maturity and weed infestations. The information gathered through machine vision and other sensors such as GPS can be used to create field management schedules for chemical application, cultivation and harvest. The purpose of the study is to develop an intelligent machine vision system for an agricultural mobile robot A multi-spectral imaging system was developed to remotely obtain crop status on a field.

Crop Status Sensing Based on Machine Vision for Precision Farming

Sensors are an essential part of intelligent agricultural machinery. Machine vision, in particular, can supply information about current crop status, including maturity and weed infestations. The information gathered through machine vision and other sensors such as GPS can be used to create field management schedules for chemical application, cultivation and harvest. The purpose of the study is to develop an intelligent machine vision system for an agricultural mobile robot A mUlti-spectral imaging system was developed to remotely obtain crop status on a field. The developed multi-spectral imaging system (MSIS) consists of an imaging sensor, an ilumination sensor, a differential GPS, and a portable computer. The imaging sensor was a custom-developed 3-CCD camera, which contains t~lfee separate optical paths and CCD image plane. Special optical filters were installed over the sensors providing tllfee video channels of Green (G), Red (R), and near infrared (NIR). The field experiment of MSIS was conducted using cornfield. The r2 for estimation of the crop height showed 0.73. And, the r2 for estimation of the SPAD value showed 0.92 by using both reflectance and leaf area infonnation. As a result, the developed vision system enables a robot to recognize the crop status and efficiently conduct field operation through the timely information. The outputs include crop stress maps with nitrogen deficiency indexes on the field which is available on building a database for precise field management.

Environmental control strategies based on plant responses using intelligent machine vision technique

The finite element method was utilized in image feature extraction. Non-linear relationships between plant moisture content and the finite element image features were identified by using a neural network in order to predict water status of plant from digital image of a wilting plant. In this paper, bio-response feedback control strategies for developing an intelligent water management system in plant production were discussed. A non-invasive sensing technique for monitoring plant moisture conditions were proposed.