Search results for "image processing"
showing 10 items of 3285 documents
Development of an Automatic Pollen Classification System Using Shape, Texture and Aperture Features
2015
International audience; Automatic detection and classification of pollen species has value for use inside of palynologic allergen studies. Traditional labeling of different pollen species requires an expert biologist to classify particles by sight, and is therefore time-consuming and expensive. Here, an automatic process is developed which segments the particle contour and uses the extracted features for the classification process. We consider shape features, texture features and aperture features and analyze which are useful. The texture features analyzed include: Gabor Filters, Fast Fourier Transform, Local Binary Patterns, Histogram of Oriented Gradients, and Haralick features. We have s…
New techniques for visualization of losses due to image compression in grayscale medical still images
2003
To evaluate the visual influence of irreversible compression on medical images, changes of the images have to be visualized. The authors have explored alternative techniques to be used instead of the usual side-by-side comparison, where the information contained in both images is perceived in a single image, preserving the context between compression errors and image structures. Thus fast and easy comparison can be done. These techniques make use of the human ability to perceive information also in the dimensions of color, space, and time. A study was performed with JPEG-compressed coronary angiographic images. Changes in the resulting images for six compression factors from 7 to 30 were sc…
Graph Clustering with Local Density-Cut
2018
In this paper, we introduce a new graph clustering algorithm, called Dcut. The basic idea is to envision the graph clustering as a local density-cut problem. To identify meaningful communities in a graph, a density-connected tree is first constructed in a local fashion. Building upon the local intuitive density-connected tree, Dcut allows partitioning a graph into multiple densely tight-knit clusters effectively and efficiently. We have demonstrated that our method has several attractive benefits: (a) Dcut provides an intuitive criterion to evaluate the goodness of a graph clustering in a more precise way; (b) Building upon the density-connected tree, Dcut allows identifying high-quality cl…
Learning to Rank Images for Complex Queries in Concept-based Search
2018
Concept-based image search is an emerging search paradigm that utilizes a set of concepts as intermediate semantic descriptors of images to bridge the semantic gap. Typically, a user query is rather complex and cannot be well described using a single concept. However, it is less effective to tackle such complex queries by simply aggregating the individual search results for the constituent concepts. In this paper, we propose to introduce the learning to rank techniques to concept-based image search for complex queries. With freely available social tagged images, we first build concept detectors by jointly leveraging the heterogeneous visual features. Then, to formulate the image relevance, …
Movie Script Similarity Using Multilayer Network Portrait Divergence
2020
International audience; This paper addresses the question of movie similarity through multilayer graph similarity measures. Recent work has shown how to construct multilayer networks using movie scripts, and how they capture different aspects of the stories. Based on this modeling, we propose to rely on the multilayer structure and compute different similarities, so we may compare movies, not from their visual content, summary, or actors, but actually from their own storyboard. We propose to do so using “portrait divergence”, which has been recently introduced to compute graph distances from summarizing graph characteristics. We illustrate our approach on the series of six Star Wars movies.
Global RDF Vector Space Embeddings
2017
Vector space embeddings have been shown to perform well when using RDF data in data mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local information, i.e., they rely on local sequences generated for nodes in the RDF graph. For word embeddings, global techniques, such as GloVe, have been proposed as an alternative. In this paper, we show how the idea of global embeddings can be transferred to RDF embeddings, and show that the results are competitive with traditional local techniques like RDF2Vec.
Verification of linear hybrid systems with large discrete state spaces using counterexample-guided abstraction refinement
2017
Abstract We present a counterexample-guided abstraction refinement ( CEGAR) approach for the verification of safety properties of linear hybrid automata with large discrete state spaces, such as naturally arising when incorporating health state monitoring and degradation levels into the controller design. Such models can – in contrast to purely functional controller models – not be analyzed with hybrid verification engines relying on explicit representations of modes, but require fully symbolic representations for both the continuous and discrete part of the state space. The presented abstraction methods directly work on a symbolic representation of arbitrary non-convex combinations of line…
A study on graph representations for genetic programming
2020
Graph representations promise several desirable properties for Genetic Programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behavior of Cartesian Genetic Programming (CGP), Linear Genetic Programming (LGP), Evolving Graphs by Graph Programming (EGGP) and traditional GP. By fixing some aspects of the config…
On the role of non-effective code in linear genetic programming
2019
In linear variants of Genetic Programming (GP) like linear genetic programming (LGP), structural introns can emerge, which are nodes that are not connected to the final output and do not contribute to the output of a program. There are claims that such non-effective code is beneficial for search, as it can store relevant and important evolved information that can be reactivated in later search phases. Furthermore, introns can increase diversity, which leads to higher GP performance. This paper studies the role of non-effective code by comparing the performance of LGP variants that deal differently with non-effective code for standard symbolic regression problems. As we find no decrease in p…
Gl-learning
2016
In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and to model highly complex systems. Our library implements the main state-of-the-art algorithms in the grammatical inference field (RPNI, EDSM, L*), redesigned through the OpenMP library for a parallel execution that drastically decreases execution times. To our best knowledge, it is also the first comprehensive library including a noise tolerance learning algorithm, such as Blue*, that significantly broadens the range of the potential application s…