Color image segmentation: advances and prospects

By Heng-Da Cheng, Xihua Jiang, Ying Sun, Jingli Wang. Pattern Recognition, 2001

1,869 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3b9eee8d496d1c08db7f4d55ca2fd8b447174483

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Adaptive local thresholding for detection of nuclei in diversity stained cytology images (2011, methodology): “The L*a*b* color space image can be found from the RGB color space image using the conversions presented in [5].”
HTPheno: An image analysis pipeline for high-throughput plant phenotyping (2011, background): “They can operate in, for example, the following colour spaces: RGB, YIQ, YUV, I 1 I 2 I 3 , HSV, Nrgb, CIE L*u*v* or CIE L*a*b* [7].”
A REVIEW ON IMAGE SEGMENTATION TECHNIQUES (2016, background): “Image segmentation is one of the most important tasks in image processing which is used to partition an image into several disjoint subsets such that each subset corresponds to a meaningful part of the image [6].”
Automated breast cancer detection and classification using ultrasound images: A survey (2015, methodology): “Segmentation [87] is a partition of the image I into nonoverlapping regions”
A hierarchical approach to color image segmentation using homogeneity (2000, methodology): “The value of the homogeneity at each location of an image has a range from [0] [1].”
A Survey of Digital Map Processing Techniques (2014, methodology): “Histogram thresholding is an attribute-space–based technique that uses histograms of color values found in an image to identify values or value ranges that occur frequently and thus could represent possible color classes [Sahoo et al. 1988; Pal and Pal 1993; Cheng et al. 2001].”
Real-time scene text localization and recognition (2012, methodology): “In this paper, we consider RGB and HSI color spaces [3] and additionally an intensity gradient channel (∇) where each pixel is assigned the value of “gradient” approximated by maximal intensity difference between the pixel and its neighbors (see Figure 3):”
Color Image Segmentation Based on Mean Shift and Normalized Cuts (2007, background): “Color images carry much more information than gray-level ones [24].”
Improving RGB-D SLAM in dynamic environments: A motion removal approach (2017, neutral): “After VQ, the original set is represented by a limited number of vectors [38].”
Object-based urban vegetation mapping with high-resolution aerial photography as a single data source (2013, methodology): “IHS eliminates the correlation of red–green–blue (RGB) bands, and shows less overlap in the feature space for vegetation classification, and it is also used for shadow removal (Cheng et al. 2001; Pan, Li, and Sun 2007).”
State of the art on automatic road extraction for GIS update: a novel classification (2003, methodology): “In this field, Cheng et al. (2001) presents multiple references about the main segmentation supervised and unsupervised techniques.”
Edge and line oriented contour detection: State of the art (2011, background): “We make distinction between the following classes of contour detectors (i) region-oriented approaches [3,4], in which regions of constant or slowly varying texture or color are first identified; contours are then straightforwardly determined as closed region boundaries; (ii) edge and line oriented a”
Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping (2012, methodology): “A pseudo colour image offers more than one colour parameter and has been shown to provide additional information for the segmentation process when compared to greyscale imagery (Cheng et al., 2001, 2002).”
Towards perceptual shared autonomy for robotic mobile manipulation (2011, methodology): “There is a large literature on the problem of image segmentation [8] and range image segmentation [9], but most previous methods have a limited ability to extract objects 2011 IEEE International Conference on Robotics and Automation Shanghai International Conference Center May 9-13, 2011, Shanghai, ”