Search results for " categorization"
showing 10 items of 54 documents
A segmentation algorithm for noisy images
2005
International audience; This paper presents a segmentation algorithm for gray-level images and addresses issues related to its performance on noisy images. It formulates an image segmentation problem as a partition of a weighted image neighborhood hypergraph. To overcome the computational difficulty of directly solving this problem, a multilevel hypergraph partitioning has been used. To evaluate the algorithm, we have studied how noise affects the performance of the algorithm. The alpha-stable noise is considered and its effects on the algorithm are studied. Key words : graph, hypergraph, neighborhood hypergraph, multilevel hypergraph partitioning, image segmentation and noise removal.
Automatic detection of hemangiomas using unsupervised segmentation of regions of interest
2016
In this paper we compare the performances of three automatic methods of identifying hemangioma regions in images: 1) unsupervised segmentation using the Otsu method, 2) Fuzzy C-means clustering (FCM) and 3) an improved region growing algorithm based on FCM (RG-FCM). For each image, the starting point of the algorithms is a rectangular region of interest (ROI) containing the hemangioma. For computing the performances of each method, the ROIs had been manually labeled in 2 classes: pixels of hemangioma and pixels of non-hemangioma. The computed scores are given separately for each image, as well as global performances across all ROIs for both classes. The best classification of non-hemangioma…
Unsupervised low-key image segmentation using curve evolution approach
2013
Low-key images widely exist in imaging-based systems such as space telescopes, medical imaging equipment, machine vision systems. Unsupervised low-key image segmentation is an important process for image analysis or digital measurement in these applications. In this paper, a novel active contour model with the probability density function (PDF) of gamma distribution for image segmentation is proposed. The flexible gamma distribution is used to describe both of the heterogeneous foreground and dark background in a low-key image. Besides, an unsupervised curve initialization method is also designed in this paper, which helps to accelerate the convergence speed of curve evolution. The effectiv…
Olfactory categorization: a developmental study.
2012
International audience; This study examined the ability of children to classify fruit and flower odors. We asked four groups of children (4-11 years of age) and a group of adults to identify, categorize, and evaluate the edibility, liking, and typicality of 12 fruit and flower odors. Results showed an increase in interindividual agreement with age for the taxonomic (fruit/flower) and function-based (edible/nonedible) categories but not for the hedonic component. So, it seems that this hedonic component is not the explicit basis for this increase in interindividual agreement when categorizing an odor as a fruit/flower odor or as being edible or nonedible. An age-related trend was also observ…
Bagging and Boosting with Dynamic Integration of Classifiers
2000
One approach in classification tasks is to use machine learning techniques to derive classifiers using learning instances. The co-operation of several base classifiers as a decision committee has succeeded to reduce classification error. The main current decision committee learning approaches boosting and bagging use resampling with the training set and they can be used with different machine learning techniques which derive base classifiers. Boosting uses a kind of weighted voting and bagging uses equal weight voting as a combining method. Both do not take into account the local aspects that the base classifiers may have inside the problem space. We have proposed a dynamic integration tech…
Iteratively Learning a Liver Segmentation Using Probabilistic Atlases: Preliminary Results
2016
This works deals with the concept of liver segmentation by using a priori information based on probabilistic atlases and segmentation learning based of previous steps. A probabilistic atlas is here understood as a probability or membership map that tells how likely is that a point belongs to a shape drawn from the shape distribution at hand. We devise a procedure to segment Perfusion Magnetic Resonance liver images that combines both: a probabilistic atlas of the liver and a segmentation algorithm based on global information of previous simpler segmentation steps, local information from close segmented slices and finally a mathematical morphology procedure, namely viscous reconstruction, to…
A new image segmentation approach using community detection algorithms
2015
Image segmentation has an important role in many image processing applications. Several methods exist for segmenting an image. However, this technique is still a relatively open topic for which various research works are regularly presented. With the recent developments on complex networks theory, image segmentation techniques based on graphs has considerably improved. In this paper, we present a new perspective of image segmentation, by applying three of the most efficient community detection algorithms, Louvain, infomap and stability optimization based on the louvain algorithm, and we extract communities in which the highest modularity feature is achieved. After we show that this measure …
Efficient Multi-scale Patch-Based Segmentation
2015
The objective of this paper is to devise an efficient and accurate patch-based method for image segmentation. The method presented in this paper builds on the work of Wu et al. [14] with the introduction of a compact multi-scale feature representation and heuristics to speed up the process. A smaller patch representation along with hierarchical pruning allowed the inclusion of more prior knowledge, resulting in a more accurate segmentation. We also propose an intuitive way of optimizing the search strategy to find similar voxel, making the method computationally efficient. An additional approach at improving the speed was explored with the integration of our method with Optimised PatchMatch…
Extracting cloud motion from satellite image sequences
2004
This paper present a new technique for the estimation of cloud motion, using a sequence of infrared satellite images. It can be considered a challenging task due to the complexity of phenomena implied, as non-linear events and a non-rigid motion. In this circumstances most motion models are not suitable and new algorithms have to be developed. We propose a novel method, combining an Automatic Multilevel Thresholding for image segmentation, a Block Matching Algorithm (BMA) and a best candidate block search along with a vector median regularization.
Feature extraction and correlation for time-to-impact segmentation using log-polar images
2004
In this article we present a technique that allows high-speed movement analysis using the accurate displacement measurement given by the feature extraction and correlation method. Specially, we demonstrate that it is possible to use the time to impact computation for object segmentation. This segmentation allows the detection of objects at different distances.