11,016 research outputs found
Toward color image segmentation in analog VLSI: Algorithm and hardware
Standard techniques for segmenting color images are based on finding normalized RGB discontinuities, color histogramming, or clustering techniques in RGB or CIE color spaces. The use of the psychophysical variable hue in HSI space has not been popular due to its numerical instability at low saturations. In this article, we propose the use of a simplified hue description suitable for implementation in analog VLSI. We demonstrate that if theintegrated white condition holds, hue is invariant to certain types of highlights, shading, and shadows. This is due to theadditive/shift invariance property, a property that other color variables lack. The more restrictive uniformly varying lighting model associated with themultiplicative/scale invariance property shared by both hue and normalized RGB allows invariance to transparencies, and to simple models of shading and shadows. Using binary hue discontinuities in conjunction with first-order type of surface interpolation, we demonstrate these invariant properties and compare them against the performance of RGB, normalized RGB, and CIE color spaces. We argue that working in HSI space offers an effective method for segmenting scenes in the presence of confounding cues due to shading, transparency, highlights, and shadows. Based on this work, we designed and fabricated for the first time an analog CMOS VLSI circuit with on-board phototransistor input that computes normalized color and hue
Machine learning approach for segmenting glands in colon histology images using local intensity and texture features
Colon Cancer is one of the most common types of cancer. The treatment is
planned to depend on the grade or stage of cancer. One of the preconditions for
grading of colon cancer is to segment the glandular structures of tissues.
Manual segmentation method is very time-consuming, and it leads to life risk
for the patients. The principal objective of this project is to assist the
pathologist to accurate detection of colon cancer. In this paper, the authors
have proposed an algorithm for an automatic segmentation of glands in colon
histology using local intensity and texture features. Here the dataset images
are cropped into patches with different window sizes and taken the intensity of
those patches, and also calculated texture-based features. Random forest
classifier has been used to classify this patch into different labels. A
multilevel random forest technique in a hierarchical way is proposed. This
solution is fast, accurate and it is very much applicable in a clinical setup
ClassCut for Unsupervised Class Segmentation
Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].
Image segmentation with adaptive region growing based on a polynomial surface model
A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L∞ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces
Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection
We have developed an automatic method for segmenting fluorescence lifetime
(FLT) imaging microscopy (FLIM) images of cells inspired by a multi-resolution
community detection (MCD) based network segmentation method. The image
processing problem is framed as identifying segments with respective average
FLTs against a background in FLIM images. The proposed method segments a FLIM
image for a given resolution of the network composed using image pixels as the
nodes and similarity between the pixels as the edges. In the resulting
segmentation, low network resolution leads to larger segments and high network
resolution leads to smaller segments. Further, the mean-square error (MSE) in
estimating the FLT segments in a FLIM image using the proposed method was found
to be consistently decreasing with increasing resolution of the corresponding
network. The proposed MCD method outperformed a popular spectral clustering
based method in performing FLIM image segmentation. The spectral segmentation
method introduced noisy segments in its output at high resolution. It was
unable to offer a consistent decrease in MSE with increasing resolution.Comment: 21 pages, 6 figure
Fast Graph-Based Object Segmentation for RGB-D Images
Object segmentation is an important capability for robotic systems, in
particular for grasping. We present a graph- based approach for the
segmentation of simple objects from RGB-D images. We are interested in
segmenting objects with large variety in appearance, from lack of texture to
strong textures, for the task of robotic grasping. The algorithm does not rely
on image features or machine learning. We propose a modified Canny edge
detector for extracting robust edges by using depth information and two simple
cost functions for combining color and depth cues. The cost functions are used
to build an undirected graph, which is partitioned using the concept of
internal and external differences between graph regions. The partitioning is
fast with O(NlogN) complexity. We also discuss ways to deal with missing depth
information. We test the approach on different publicly available RGB-D object
datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset,
and compare the results with other existing methods
Non-Parametric Probabilistic Image Segmentation
We propose a simple probabilistic generative model for
image segmentation. Like other probabilistic algorithms
(such as EM on a Mixture of Gaussians) the proposed model
is principled, provides both hard and probabilistic cluster
assignments, as well as the ability to naturally incorporate
prior knowledge. While previous probabilistic approaches
are restricted to parametric models of clusters (e.g., Gaussians)
we eliminate this limitation. The suggested approach
does not make heavy assumptions on the shape of the clusters
and can thus handle complex structures. Our experiments
show that the suggested approach outperforms previous
work on a variety of image segmentation tasks
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