Search results for "Age class"
showing 10 items of 133 documents
Simplified spiking neural network architecture and STDP learning algorithm applied to image classification
2015
Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced computation complexity. SNN have been successfully used for image classification. They provide a model for the mammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical models exist, but their computational complexity makes them ill-suited for hardware implementation. In this paper, a novel, simplified and computationally efficient model of spike response model (SRM) neuron with spike-time dependent plasticity (STDP) lear…
The pathology of bladder cancer: An update on selected issues
2006
OBJECTIVE: To achieve a closer relationship between urologists and pathologists and to use a common language and identical objectives in the pathology of bladder cancer. METHODS AND RESULTS: Special emphasis was given to an analysis of the new World Health Organization (WHO) grading system, to the interpretation of the last Tumour-Nodes-Metastasis staging rules, and to identifying new markers of prognostic significance in clinical practice. A consensus was achieved on the main points. CONCLUSIONS: The 2004 WHO grading system must become acceptable to clinicians, perhaps by a minimal modification of the present terminology. Simple transurethral resection-biopsy should be expressed in terms o…
Optimal band selection for future satellite sensor dedicated to soil science
2009
Hyperspectral imaging systems could be used for identifying the different soil types from the satellites. However, detecting the reflectance of the soils in all the wavelengths involves the use of a large number of sensors with high accuracy and also creates a problem in transmitting the data to earth stations for processing. The current sensors can reach a bandwidth of 20 nm and hence, the reflectance obtained using the sensors are the integration of reflectance obtained in each of the wavelength present in the spectral band. Moreover, not all spectral bands contribute equally to classification and hence, identifying the bands necessary to have a good classification is necessary to reduce …
Semi-supervised Hyperspectral Image Classification with Graphs
2006
This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to exploit the spatial/contextual information in the im- ages through composite kernels. The proposed method produces smoother classifications with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Good accuracy in high dimensional spaces and low number of labeled samples (ill-posed situations) are produced as compared to standard inductive support vector machines.
Including invariances in SVM remote sensing image classification
2012
This paper introduces a simple method to include invariances in support vector machine (SVM) for remote sensing image classification. We rely on the concept of virtual support vectors, by which the SVM is trained with both the selected support vectors and synthetic examples encoding the invariance of interest. The algorithm is very simple and effective, as demonstrated in two particularly interesting examples: invariance to the presence of shadows and to rotations in patchbased image segmentation. The improved accuracy (around +6% both in OA and Cohen's κ statistic), along with the simplicity of the approach encourage its use and extension to encode other invariances and other remote sensin…
Syllabus Design for Pre-service English Language Teachers in Spain
2018
This paper describes the steps involved in designing educational programs for pre-service English language teachers in Secondary education within the Spanish context, following the current educational law (LOMCE). Considering the century-long search for the best method within TEFL (Teaching English as a Foreign Language), and the continuous reforms of educational laws in Spain, pre-service teachers must learn the competences and skills essential for designing of their own teaching program. The series of steps described here will enable teachers to show accountability to educational authorities and increase their self-confidence, subsequently improving their craft in the language classroom.
Time series of Cosmo-SkyMed data for landcover classification and surface parameter retrieval over agricultural sites
2012
This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop class…
Global Upscaling of the MODIS Land Cover with Google Earth Engine and Landsat Data
2021
Image classification has become one of the most common applications in remote sensing yielding to the creation of a variety of operational thematic maps at multiple spatio-temporal scales. The information contained in these maps summarizes key characteristics related with the physical environment and provides fundamental information of the Earth for vegetation monitoring or land use status over time. However, high spatial resolution land cover maps are usually only produced for specific small regions or in an image tile. We present a general methodology to obtain a high spatial resolution land cover maps using Landsat spectral information, the powerful Google Earth Engine platform, and oper…
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
2020
The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource allocation principles to extract common patterns in the data, rather than relying on minimizing output error, which is prone to overfitting. Unlike the intertwined nature of pattern representation in neural networks, a TM decomposes problems into self-contained patterns, represented as conjunctive clauses. The clause outputs, in turn, are combined into a classification decision through summation and thresholding, akin to a logistic regression function, however, with binary weights and a unit step output function. …
Putting the user into the active learning loop : Towards realistic but efficient photointerpretation
2012
In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according to their uncertainty, and the confidence of the user in labeling, which is related both to the homogeneity of the pixel context and to the knowledge of the user of the scene. In this paper, we propose a two-steps procedure based on a filtering scheme to learn the confidence of the user in labeling. This way, candidate training pixels are ranked according both to their uncertainty and to the chances of being labeled correctly by the user. In…