To article proposes an approach to improve the quality of data used in various processes based on machine vision systems. The paper proposes a combined approach to applying the method of multi-criteria processing based on the use of a combined criterion in order to implement an edge detector, smoothing and separation areas of the background / object in the image. The application of the method allows eliminating the noise caused by external factors (such as dust and water suspension on the lens or space). The generated data make it possible to form an adaptive criterion for changing the correction parameters for a non-linear change in color balance in areas of increased detail or selected masks of changes blocks. The proposed algorithms make it possible to increase the visibility of small elements, reduce the noise component, while maintaining the boundaries of objects, increase the accuracy of selecting the boundaries of objects and the visual quality of data. As test data used to evaluate the effectiveness, nature data and expert evaluation results for test images obtained by a machine vision system with a sensor with a resolution of 1024x768 (8-bit, color image, visible range) are used. Images of simple shapes are used as analyzed objects.
In the paper we propose approach for lossless image compression. Proposed method is based on separate processing of two image components: structure and texture. In the subsequent step separated components are compressed by standard RLE/LZW coding. We have performed a comparative analysis with existing techniques using standard test images. Our approach have shown promising results.
Content–based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo–collection management systems, web–scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image–retrieval technique. It’s the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel–based image representation to hash–value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine–tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data– dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state–of–the–art methods.
This article discusses features of the parallel hashing for the designing of the frame filtering tables in distributed computing systems. The proposed method of filtering tables design can reduce the time of frame processing by network bridges and switches and provide a low probability of filtering table overflowing. The optimal number of parallel tables was determined for a given amount of memory for table design.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.