Limited receptive area neural classifier for texture recognition of metal surfaces

The Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtainedIFIP International Conference on Artificial Intelligence in Theory and Practice - Machine VisionRed de Universidades con Carreras en Informática (RedUNCI

Limited receptive area neural classifier for texture recognition of metal surfaces

The Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtainedIFIP International Conference on Artificial Intelligence in Theory and Practice - Machine VisionRed de Universidades con Carreras en Informática (RedUNCI

Image Recognition Systems with Permutative Coding

A feature extractor and neural classifier for image recognition system are proposed. They are based on the permutative coding technique which continues our investigations on neural networks. It permits us to obtain sufficiently general description of the image to be recognized. Different types of images were used to test the proposed image recognition system. It was tested on the handwritten digit recognition problem, the face recognition problem and the shape of microobjects recognition problem. The results of testing are very promising. The error rate for the MNIST database is 0.44% and for the ORL database is 0.1%

Micromechanics as a testbed for evaluation of artificial Intelligence methods in manufacturing

The purpose is to provide a brief description of low cost microequipment prototypes and some AI methods that can be evaluated with such prototypes. Results: several neural network algorithms were proposed to improve automation systems in manufacturing processes. These algorithms were tested with specific micromechanical equipment, similar to conventional mechanical equipment, but of much smaller sizes and therefore of lower cost.Методы искусственного интеллекта (ИИ) могут использоваться для улучшения систем автоматизации в производственных процессах. Однако применение этих методов в промышленности не получило широкого распространения из-за высокой стоимости экспериментов с системами ИИ в обычных производственных системах. Для снижения стоимости экспериментов в этой области нами разработано специальное микромеханическое оборудование, аналогичное обычному механическому оборудованию, но гораздо меньших размеров и, следовательно, более низкой стоимости. Это оборудование может быть использовано для оценки различных методов ИИ простым и недорогим способом. Методы, которые показывают хорошие результаты, могут быть переданы в промышленность путем соответствующего масштабирования. Кратко описаны прототипы микрооборудования, имеющих низкую стоимость, и некоторых методов ИИ, которые могут быть оценены с такими прототипами.Методи штучного інтелекту (ШІ) можуть використовуватися для поліпшення систем автоматизації у виробничих процесах. Однак застосування цих методів у промисловості не набуло широкого поширення через високу вартість експериментів з системами ШІ у звичайних виробничих системах. Для зниження вартості експериментів у цій галузі нами розроблено спеціальне мікромеханічне обладнання, аналогічне звичайному механічному обладнанню, але набагато менших розмірів і, отже, більш низької вартості. Це обладнання може бути використано для оцінки різних методів ШІ простим і недорогим способом. Методи, які показують хороші результати, можуть бути передані в промисловість шляхом відповідного масштабування. Ця Коротко описано прототипи мікрооборудованія, що мають низьку вартість, та деяких методів ШІ, які можуть бути оцінені з такими прототипами

Limited receptive area neural classifier for texture recognition of metal surfaces

The Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtainedIFIP International Conference on Artificial Intelligence in Theory and Practice - Machine VisionRed de Universidades con Carreras en Informática (RedUNCI

Micromechanics as a testbed for artificial intelligence methods evaluation

Some of the artificial intelligence (AI) methods could be used to improve the performance of automation systems in manufacturing processes. However, the application of these methods in the industry is not widespread because of the high cost of the experiments with the AI systems applied to the conventional manufacturing systems. To reduce the cost of such experiments, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a lot smaller overall sizes and therefore of lower cost. This equipment can be used for evaluation of different AI methods in an easy and inexpensive way. The methods that show good results can be transferred to the industry through appropriate scaling. This paper contains brief description of low cost microequipment prototypes and some AI methods that can be evaluated with mentioned prototypes.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Permutation Coding Technique for Image Recognition Systems

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1