36,875 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
A Q-Ising model application for linear-time image segmentation
A computational method is presented which efficiently segments digital
grayscale images by directly applying the Q-state Ising (or Potts) model. Since
the Potts model was first proposed in 1952, physicists have studied lattice
models to gain deep insights into magnetism and other disordered systems. For
some time, researchers have realized that digital images may be modeled in much
the same way as these physical systems (i.e., as a square lattice of numerical
values). A major drawback in using Potts model methods for image segmentation
is that, with conventional methods, it processes in exponential time. Advances
have been made via certain approximations to reduce the segmentation process to
power-law time. However, in many applications (such as for sonar imagery),
real-time processing requires much greater efficiency. This article contains a
description of an energy minimization technique that applies four Potts
(Q-Ising) models directly to the image and processes in linear time. The result
is analogous to partitioning the system into regions of four classes of
magnetism. This direct Potts segmentation technique is demonstrated on
photographic, medical, and acoustic images.Comment: 7 pages, 8 figures, revtex, uses subfigure.sty. Central European
Journal of Physics, in press (2010
Learning Features by Watching Objects Move
This paper presents a novel yet intuitive approach to unsupervised feature
learning. Inspired by the human visual system, we explore whether low-level
motion-based grouping cues can be used to learn an effective visual
representation. Specifically, we use unsupervised motion-based segmentation on
videos to obtain segments, which we use as 'pseudo ground truth' to train a
convolutional network to segment objects from a single frame. Given the
extensive evidence that motion plays a key role in the development of the human
visual system, we hope that this straightforward approach to unsupervised
learning will be more effective than cleverly designed 'pretext' tasks studied
in the literature. Indeed, our extensive experiments show that this is the
case. When used for transfer learning on object detection, our representation
significantly outperforms previous unsupervised approaches across multiple
settings, especially when training data for the target task is scarce.Comment: CVPR 201
Enhancement of Image Resolution by Binarization
Image segmentation is one of the principal approaches of image processing.
The choice of the most appropriate Binarization algorithm for each case proved
to be a very interesting procedure itself. In this paper, we have done the
comparison study between the various algorithms based on Binarization
algorithms and propose a methodologies for the validation of Binarization
algorithms. In this work we have developed two novel algorithms to determine
threshold values for the pixels value of the gray scale image. The performance
estimation of the algorithm utilizes test images with, the evaluation metrics
for Binarization of textual and synthetic images. We have achieved better
resolution of the image by using the Binarization method of optimum
thresholding techniques.Comment: 5 pages, 8 figure
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