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Artificial intelligence is widely used in image processing. Neural networks (NN) were successful used for solving complicated issues due to their capacity of generalization and learning from examples. In this paper some aspects of image compression using artificial neural networks are discussed. The network is used in the feedback loop of the visual servoing system, which aims to control a wheeled...
This paper presents a novel frame-pair based method for visual object tracking. Instead of adopting two-stream Convolutional Neural Networks (CNNs) to represent each frame, we stack frame pairs as the input, resulting in a single-stream CNN tracker with much fewer parameters. The proposed tracker can learn generic motion patterns of objects with much less annotated videos than previous methods. Besides,...
Visual tracking is a significant but challenging field in computer vision. Although considerable progress has been made in recent years, robust tracking in complicated scenes remains an open problem. Trackers get confused easily when similar objects appear or heavy clutter occurs due to indistinguishable features. In this work, a more effective feature extraction method based on convolutional neural...
We present an approach for unsupervised computation of local shape descriptors, which relies on the use of linear autoencoders for characterizing local regions of complex shapes. The proposed approach responds to the need for a robust scheme to index binary images using local descriptors, which arises when only few examples of the complete images are available for training, thus making inaccurate...
Visual prosthesis holds hope of vision restoration for millions with retinal degenerative diseases. Machine learning techniques such as artificial neural networks could help in improving prosthetic devices as they could learn how the brain encodes information and imitate that code. This paper introduces an autoencoder-based approach for tuning thalamic visual prostheses. The objective of the proposed...
Along with the development of social network, more and more people know the world by reading news. The problem about what kind of emotion is inspired when people read news is very worthy of discussion. This paper will mix Deep Belief Networks (DBN) model and Support Vector Machine (SVM) to a hybrid neural network model by using the Contrast Divergence (CD) algorithm to estimate the weights when training...
A compact device for training of artificial neural network on the basis of knowledge of an expert is described in the article. The device implements the proposed original heuristic method of artificial neural network training for solution of the problems of classification on the basis of weakly formalized expert knowledges and its algorithms that use visual (cognitive) images of possible situations...
Due to the large amounts of Multimedia data on the Internet, Multimedia mining has become a very active area of research. Multimedia mining is a form of data mining. Data mining uses algorithms to segment data to identify useful patterns and to make predictions. Despite the successes in many areas, data mining remains a challenging task. In the past, multimedia mining was one of the fields where the...
The automatic meteor detection solution presented in this paper uses a self-organizing map to analyze radio spectrogram data and detect the meteor samples found within. This artificial neural network is trained using data samples extracted from spectrograms of radio recordings using a rectangular sliding window. Several tests were run to find the optimal neural network topology and duration of training...
The paper describes the use of associative models for integrating different sensors. Integrated associative structures are outlined and related to previous approaches; the enhanced robustness resulting from the integration of Associative Memories (AMs) and Neural Networks (NNs) is shown. Discussion then focuses on how different information sources can cooperate on associative visual recognition. Experimental...
A novel automatic meteor detection approach, using a self-organizing map type of artificial neural network is presented in this paper. The proposed solution aims at analyzing a given dataset and extracting the meteor signals present in it. For this, a self-organizing map is trained using radio recordings. This neural network will then be used on a test dataset to check the performances of the proposed...
As an important component of the future HumanComputer Interface, Automatic Speech Recognition is designed for the purpose of realizing identification recognition and natural language comprehension by means of human voice. Speech recognition technology has acquired significant achievements with some successful popularity and applications. IBM's ViaVoice system, for instance, has good performances when...
We demonstrate that visual (geometric) patterns can be robustly recognized by an artificial retina composed of a chaotic sensitive system where the coding of the patterns is by attractor features and an artificial neural network is used to classify the attractors. This opens the door to sensorial systems that mimic the biological ones. The specificity of solutions of chaotic systems to their parameters...
Visual navigation is an important research field in robotics because of the low cost and the high performance that is usually achieved by visual navigation systems. Pixel classification as a road pixel or a non-road pixel is a task that can be well performed by Artificial Neural Networks. In the case of real-time instances of the image classification problem, as when applied to autonomous vehicles...
The development of autonomous vehicles is a highly relevant research topic in mobile robotics. Road recognition using visual information is an important capability for autonomous navigation in urban environments. Over the last three decades, a large number of visual road recognition approaches have been appeared in the literature. This paper proposes a novel visual road detection system based on multiple...
In this paper, an efficient technique is proposed for the precise segmentation of normal and pathological tissues in the MRI brain images. The proposed segmentation technique initially performs classification process by utilizing FFBNN. Dual FFBNN networks are used in the classification process. The inputs for these networks are the features that are extracted in two ways from the MRI brain images...
Because the complication of subway engineering and uncertainty of influencing factors, traditional prediction methods of strata deformation by subway engineering are far from accuracy. In this paper, Visual C++6.0 is adopted to combine FLAC3D based on finite difference method with artificial neural network, to construct the artificial analysis method connecting normal analysis with back analysis....
The design of visual robotic behaviors constitutes a substantial challenge. It requires to draw meaningful relationships and constraints between the acquired visual perception and the geometry of the environment both empirically and programmatically. This contribution proposes a novel robot learning framework to classify and acquire scenario specific autonomous behaviors through demonstration. During...
In the 1980s and at the turn of last century, severe global waves of sovereign defaults occurred in less developed countries. To date, the forecasting and monitoring results of debt crises are still at a preliminary stage, while the issue is at present highly topical. This paper explores whether the application of the Self-organizing map (SOM), a neural network-based visualization tool, facilitates...
Recent evidence in neuroscience support the theory that prediction of spatial and temporal patterns in the brain plays a key role in human actions and perception. Inspired by these findings, a system that discriminates laughter from speech by modeling the spatial and temporal relationship between audio and visual features is presented. The underlying assumption is that this relationship is different...
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