3,834 research outputs found
A comparative evaluation of interactive segmentation algorithms
In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25 with each algorithm, constrained within a time limit of 2 min for each object. To facilitate the experiments, a âscribble-drivenâ segmentation tool was developed to enable interactive image segmentation by simply marking areas of foreground and background with the mouse. As the participants refined and improved their respective segmentations, the corresponding updated segmentation mask was stored along with the elapsed time. We then collected and evaluated each recorded mask against a manually segmented ground truth, thus allowing us to gauge segmentation accuracy over time. Two benchmarks were used for the evaluation: the well-known Jaccard index for measuring object accuracy, and a new fuzzy metric, proposed in this paper, designed for measuring boundary accuracy. Analysis of the experimental results demonstrates the effectiveness of the suggested measures and provides valuable insights into the performance and characteristics of the evaluated algorithms
Perceptual-based textures for scene labeling: a bottom-up and a top-down approach
Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label
A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm
Biofilm is a formation of microbial material on tooth substrata. Several
methods to quantify dental biofilm coverage have recently been reported in the
literature, but at best they provide a semi-automated approach to
quantification with significant input from a human grader that comes with the
graders bias of what are foreground, background, biofilm, and tooth.
Additionally, human assessment indices limit the resolution of the
quantification scale; most commercial scales use five levels of quantification
for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current
state-of-the-art techniques in automatic plaque quantification fail to make
their way into practical applications owing to their inability to incorporate
human input to handle misclassifications. This paper proposes a new interactive
method for biofilm quantification in Quantitative light-induced fluorescence
(QLF) images of canine teeth that is independent of the perceptual bias of the
grader. The method partitions a QLF image into segments of uniform texture and
intensity called superpixels; every superpixel is statistically modeled as a
realization of a single 2D Gaussian Markov random field (GMRF) whose parameters
are estimated; the superpixel is then assigned to one of three classes
(background, biofilm, tooth substratum) based on the training set of data. The
quantification results show a high degree of consistency and precision. At the
same time, the proposed method gives pathologists full control to post-process
the automatic quantification by flipping misclassified superpixels to a
different state (background, tooth, biofilm) with a single click, providing
greater usability than simply marking the boundaries of biofilm and tooth as
done by current state-of-the-art methods.Comment: 10 pages, 7 figures, Journal of Biomedical and Health Informatics
2014. keywords: {Biomedical imaging;Calibration;Dentistry;Estimation;Image
segmentation;Manuals;Teeth},
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6758338&isnumber=636350
Two and three dimensional segmentation of multimodal imagery
The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes
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