Search results for "Image classification"
showing 10 items of 114 documents
Incorporating in vivo and ex vivo NMR sources of information for modeling robust brain tumor classifiers
2010
The purpose of this paper is to investigate the potential and limitations of using multimodal sources of information coming from in vivo NMR and ex vivo NMR data for detecting brain tumors. Supervised pattern recognition methods, whose performance directly depends on the prior available observations used in building them, are proposed. We show that high resolution magic angle spinning (HR-MAS) data act as complementary information for classifying magnetic resonance spectroscopic imaging (MRSI) data. In particularly, when considering rare brain tumors, since it is unlikely to acquire sufficient cases to define their metabolite profiles using only in vivo NMR information, HR-MAS can support t…
Classification of cat ganglion retinal cells and implications for shape-function relationship
2002
This article presents a quantitative approach to ganglion cell classification by considering combinations of several geometrical features including fractal dimension, symmetry, diameter, eccentricity and convex hull. Special attention is given to moment and symmetry-based features. Several combinations of such features are fed to two clustering methods (Ward's hierarchical scheme and K-Means) and the respectively obtained classifications are compared. The results indicate the superiority of some features, also suggesting possible biological implications.
Study and Evaluation of Pre-trained CNN Networks for Cultural Heritage Image Classification
2021
The classification of digital images is an essential task during the restoration and preservation of cultural heritage (CH). In computer vision, cultural heritage classification relies on the classification of asset images regarding a certain task such as type, artist, genre, style identification, etc. CH classification is challenging as various CH asset images have similar colors, textures, and shapes. In this chapter, the aim is to study and evaluate the use of pre-trained deep convolutional neural networks such as VGG16, VGG-19, ResNet50, and Inception-V3 for cultural heritage images classification using transfer learning techniques. The main idea is to start with CNN models previously t…
Machine learning in remote sensing data processing
2009
Remote sensing data processing deals with real-life applications with great societal values. For instance urban monitoring, fire detection or flood prediction from remotely sensed multispectral or radar images have a great impact on economical and environmental issues. To treat efficiently the acquired data and provide accurate products, remote sensing has evolved into a multidisciplinary field, where machine learning and signal processing algorithms play an important role nowadays. This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis.
Dimensionality Reduction Techniques: An Operational Comparison On Multispectral Satellite Images Using Unsupervised Clustering
2006
Multispectral satellite imagery provides us with useful but redundant datasets. Using Dimensionality Reduction (DR) algorithms, these datasets can be made easier to explore and to use. We present in this study an objective comparison of five DR methods, by evaluating their capacity to provide a usable input to the K-means clustering algorithm. We also suggest a method to automatically find a suitable number of classes K, using objective "cluster validity indexes" over a range of values for K. Ten Landsat images have been processed, yielding a classification rate in the 70-80% range. Our results also show that classical linear methods, though slightly outperformed by more recent nonlinear al…
FABC: Retinal Vessel Segmentation Using AdaBoost
2010
This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as we…
Depth Map Generation by Image Classification
2004
This paper presents a novel and fully automatic technique to estimate depth information from a single input image. The proposed method is based on a new image classification technique able to classify digital images (also in Bayer pattern format) as indoor, outdoor with geometric elements or outdoor without geometric elements. Using the information collected in the classification step a suitable depth map is estimated. The proposed technique is fully unsupervised and is able to generate depth map from a single view of the scene, requiring low computational resources.
Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval
2017
Remote sensing data analysis is knowing an unprecedented upswing fostered by the activities of the public and private sectors of geospatial and environmental data analysis. Modern imaging sensors offer the necessary spatial and spectral information to tackle a wide range problems through Earth Observation, such as land cover and use updating, urban dynamics, or vegetation and crop monitoring. In the upcoming years even richer information will be available: more sophisticated hyperspectral sensors with high spectral resolution, multispectral sensors with sub-metric spatial detail or drones that can be deployed in very short time lapses. Besides such opportunities, these new and wealthy infor…
Mammogram Segmentation by Contour Searching and Mass Lesions Classification with Neural Network
2006
The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting masses in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration (Medical Applications on a Grid Infrastructure Connection). A reduction of the whole image's area under investigation is achieved through a segmentation process, by means of a ROI Hunter algorithm, without loss of meaningful information. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters.…
Remote Sensing Image Classification with Large Scale Gaussian Processes
2017
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for…