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To successfully move a robot into the building, the elevator button and elevator floor number detection and recognition can play an important role. It can help a robot move in the building, just as it also can help a visually impaired person who wants to move another floor in the building. Due to vision-based approach, the difference in lighting condition and the complex background are the main obstacles...
Every organism emits energy around it which comprises UV-radiation, EM-radiation, infrared and thermal radiation. This energy around human body represents health condition of the subject under study. These energy fields are called as aura of the body under consideration. Several types of equipments are there to capture such energy. Kirlian camera captures the distribution of energy radiation around...
The main objective of the spatial image classification is to extract information classes from a multiband raster spatial image. The network structure and number of inputs are the key factors in deciding the performance and accuracy of the traditional pixel based image classification techniques like Support Vector Machines (SVM), Artificial Neural Networks (ANN), Fuzzy logic, Decision Trees (DT) and...
This paper presents the results of the ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers). This work aims at providing a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition. At this competition, we proposed two independent classification...
This study proposes and evaluates the application of two classifiers: decision tree (DT) and neural network (NN) to discriminate three region types: cancer (CC), lymphocyte (LC), and stromal (SC) in the breast cancer cell images. The feature extraction from area based texture information of BCCI is studied to compare results from the segmented cells. A combination between texture features based on...
Image classification is one of the most multifaceted disciplines in image processing. There are quite a few approaches to categorize images and they offer good classification outcome but they not be up to snuff to provide acceptable classification upshots when the image comprises blurry content. The two chief techniques for image classification are supervised and unsupervised classification. Mutually...
BP neural network is an important and efficient method in machine learning. But there are some drawbacks lying in its local minimum and slow convergence speed. To solve these problems and enhance the performance of BP network, an optimized BP neural network by genetic algorithm is proposed in this paper. Firstly, we design a fitness function based on genetic algorithm for the view of obtaining the...
Artificial Intelligence development is stepping into a new era due to the recent exciting achievements from neural network and statistical machine learning research communities. Statistic neural-computing based machine learning has been deemed as one of promising roads towards realizing the ideal of Artificial Intelligence promoted since last century. Learning is the key in making progress. Statistic...
A novel polarimetric synthetic aperture radar (PolSAR) image classification method based on Deep Belief Networks (DBNs) is proposed in this paper. First, the coherency matrix data are converted to a 9-dimentional data. Second, many patches are randomly selected from each dimension in the 9-dimentional data, and many filters can be obtained from a Restricted Boltzmann Machine (RBM) trained by using...
In recent years, Image classification has been a growing research area in the computer vision field. Thus, many approaches were proposed in literature. Moreover, many content-based image classification approaches are widely used in developing applications and techniques for many areas such as remote-sensing and content-based image retrieval. In this study, we introduce a new technique for content-based...
This paper focus on two phenomena that "same spectrum with different objects" and "same object with different spectra" in multispectral remote sensing image, and propose a stepwise refinement classification method based on multi-sensitive strategies. It's a top-down, gradually refinement hierarchical way of classification which combines with advantages of both supervised classification...
Features derived from Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run-Length (GLRL) matrix are widely used for image characterization based on texture analysis. In this paper, we propose the application of suitably selected texture discriminating features for classification of oral cancer lesions in digital camera images into six groups. Backpropagation based Artificial Neural Network (BPANN)...
This paper presents a color image classification method using rank based ensemble classifier. In this paper, we use color histogram in different color spaces and Gabor wavelet to extract color and texture features respectively. These features are classified by two classifiers: Nearest Neighbor (NN) and Multi Layer Perceptron (MLP). In the proposed approach, each set of features are classified by each...
With the rising of internet photos-sharing web sites, the rich aware text information surrounding images on the sites are proved helpful to improve the image classification. This paper presents a novel nested deep learning model called Nested Deep Belief Network(NDBN) for tag-aware image classification. A multi-layer structure of Deep Belief Network(DBN) is established to learn a unified representation...
The novel instruments of the COSMO-SkyMed (CSK) Earth Observation programme, offer an opportunity to explore at various resolutions the information content of X-band signal backscattered with different polarizations. In spite of their potential to render additional information about an area of interest, speckle noise and artifacts make X-band acquisitions difficult to interpret. This is a motivating...
The research presented in this paper was aimed to develop a recognition system for microscopic images of breast tissues samples. The system should classify breast tissues as malignant or not, or identifying their malignancy types. In this paper, multi-scale fractal dimension concept was used to extract a set of textural features in order to perform texture analysis for breast tissues samples. The...
This paper presents a support vector machine (SVM), The color images and text to effectively classify the large image database. In order to evaluate the performance of this method, it will be this way for the artificial neural network, this method proved to be superior to other methods.
The new generation of spaceborne instruments, capable of capturing a large amount of very-high resolution images within a short revisit time, is allowing remote sensing researchers and final users to receive huge amounts of data in rather short times. Such a scenario makes it mandatory the development of techniques, as much as possible automatic, for the understanding and the effective exploitation...
This paper describes a network-theoretic approach for clustering pixels in a remot-sensing image and has received relatively satisfactory results. This approach is divided into two stages—the local classification stage and the global clustering stage. During local classification, the original image is partitioned into small blocks which are converted into local network graphs respectively. Then, the...
The launch of last-generation satellites (COSMO-SkyMed and TerraSAR-X), equipped with X-band sensors acquiring images with a very high spatial resolution, has opened up new challenges in the field of SAR image processing for remote sensing applications. In this work, a set of Spotlight and Stripmap COSMO-Skymed images taken the Tor Vergata-Frascati test site was considered to investigate on the potential...
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