Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

SEAMLESS JOINING OF TILES OF VARYING RESOLUTIONS FOR ONLINE 3D TERRAIN VISUALIZATION BY DWT DOMAIN SMOOTHING

A novel context-based DWT domain smoothing strategy is being proposed for seamless tessellation of texture as well as the digital elevation model (DEM). In the use case, in perspective, the tessellation is heterogeneous in resolution with three different tile qualities or levels of detail (LOD), at a given instant, depending on the viewpoint distance, the time of rendering and hardware resources. The LOD is dependent on the multiresolution characteristic of wavelets from the now widely accepted JPEG2000 codec. Three different joining functions have been employed to create subband-sized masks for each of the tile subbands selected for smoothing. The results, obtained for a practical example, have been interesting in the sense that it eliminated the popping artifacts to a considerable extent without affecting the field of view at a given instant. 1

Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment

To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural network into the EKF estimation procedure, the quality of performance can be improved

Prediction of Robot Execution Failures Using Neural Networks

In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution

11th International Coral Reef Symposium Abstracts

https://nsuworks.nova.edu/occ_icrs/1001/thumbnail.jp