In many autonomous mobile applications, robots must be capable of analyzing motion of moving objects in their environment. Duringmovement of robot the quality of images is affected by quakes of camera which cause high errors in image processing outputs. In thispaper, we propose a novel method to effectively overcome this problem using Neural Networks and Kalman Filtering theory. Thistechnique u...

A research has been carried out finalised to the definition of a methodology useful to address the task of detection and tracking of moving targets. Algorithms performing this task for real time monitoring and surveillance purposes have been developed. Due to deformations occurring in the appearance of the target in real time video sequences, a Hierarchical Neural Network (HNN) has been used fo...

The visual recognition of human hand pointing gestures from stereo pairs of video camera images provides a very intuitive kind of man-machine interface. We show that a modular, neural network based system can solve this task in a realistic laboratory environment. Several neural networks account for image segmentation, estimation of hand location , estimation of 3D-pointing direction, and necess...

When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the specialized domain. If the target domain covers a smaller visual space than the source domain used for pre-training (e.g. ImageNet), the fine-tuned network is likely to be overparameterized. However, applying network prun...

The emerging recent large scale visual recognition methods, and in particular the deep Convolutional Neural Networks (CNN), are promising to revolutionize many computer vision based artificial intelligent applications, such as autonomous driving and online image retrieval systems. One of the main challenges in large scale visual recognition is the complexity of the corresponding algorithms. Thi...

Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more effici...

Artificial Neural Networks (ANN) are a type of computer programming that is designed to mimic the process of human cognition found the natural neural network – the human brain. Artificial Neural Networks have been successfully applied to tasks involving image compression, classification, pattern matching, and change detection. This presentation summarizes the current research of the author rela...

This paper shows how to solve the recognition of ultrasound brain-stem images. Our work is based on PCA method that is very useful and known method for image processing. We will demonstrate a solving with C# application, Gnumeric spreadsheet as a standard solution. Also we will use an artificial neural networks (ANN) for this problem and we will compare a results. The ANN are generally very usa...

This paper investigates the analysis of microscopic particles generated by wear mechanisms using image processing techniques. Particles are classified using their visual and morphological attributes to predict wear failure in engines and other machinery. The paper describes the stages of identification processing involved including a neural network system to classify wear articles in terms of t...

Current design trends with respect to the manufacture of human-like robotic platforms are constantly growing; day to day manufacturers generate new platforms in order to improve the integration of these technological tools into our daily activities. This paper proposes a methodology associated with pattern recognition by using Adaptive Resonance Theory (ART) Neural Networks and image processing...