Readers may make verbatim copies of this document for noncommercial purposes by any means, provided that the above copyright notice appears on all copies. The research reported in this paper was supported by a grant from the National Science Foundation to the University of Wisconsin–Madison (EHR 0227016) for a Mathematics and Science Partnership project called System-wide Change for All Learner...

this research was performed to evaluate the potentials of landsat mss data for map-ping land features in arid zones of southeastern esfahan, iran. databases of the area were formed using all available relevant maps and reports which were supported by fieldwork. a supervised image classification approach was used and thirty-two training areas were applied. separability of the spectral classes wa...

In this paper, we propose a large-scale image annotation system for the ImageCLEF 2014 Scalable Concept Image Annotation task. The annotation task, of this year, concentrated on developing annotation algorithms that rely only on data obtained automatically from the web. Since the sophisticated SVM based annotation techniques had been widely applied in the task last year (ImageCLEF 2013), for th...

One of the most fundamental features of digital image and the basic steps in image processing, analysis, pattern recognition and computer vision is the edge of an image where the preciseness and reliability of its results will affect directly on the comprehension machine system made objective world. Several edge detectors have been developed in the past decades, although no single edge detector...

The recent developments of training image-based geostatistics have allowed the creation of stochastic models with greater realism than before. These algorithms aim at reproducing spatial patterns, such as connectivity, that are depicted in a training image. A training image contains the possible spatial configurations for any given geological object and relationships between objects. However, a...

CLASSIFICATION Hossam Osman and Steven D. Blostein Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, Canada, K7L 3N6 ABSTRACT This paper proposes a variation of the standard backpropagation (BP) training algorithm for the particular application of multiresolution image classi cation. The proposed variation is that, during the backward phase of training, h...

The abundant information available in hyperspectral image has provided important opportunities for land-cover classification and recognition. However, “Curse of dimensionality” and small training sample set are two difficulties which hinder the improvement of computational efficiency and classification precision. In this paper, we present a co-training based method on hyperspectral image classi...

In this paper, we propose XGPT, a new method of Cross-modal Generative Pre-Training for Image Captioning that is designed to pre-train text-to-image caption generators through four novel generation tasks, including Adversarial (AIC), Image-conditioned Masked Language Modeling (IMLM), Denoising Autoencoding (IDA), and Text-conditioned Feature Generation (TIFG). As result, the pre-trained XGPT ca...

A large amount of data can partly assure good fitting quality for the trained neural networks. When quantity experimental or on-site monitoring is commonly insufficient and difficult to control in engineering practice, numerical simulations provide a controlled high data. Once networks are by such data, they be used predicting properties/responses objects instantly, saving further computing eff...