7,587 research outputs found
Colour Text Segmentation in Web Images Based on Human Perception
There is a significant need to extract and analyse the text in images on Web documents, for effective indexing, semantic analysis and even presentation by non-visual means (e.g., audio). This paper argues that the challenging segmentation stage for such images benefits from a human perspective of colour perception in preference to RGB colour space analysis. The proposed approach enables the segmentation of text in complex situations such as in the presence of varying colour and texture (characters and background). More precisely, characters are segmented as distinct regions with separate chromaticity and/or lightness by performing a layer decomposition of the image. The method described here is a result of the authors’ systematic approach to approximate the human colour perception characteristics for the identification of character regions. In this instance, the image is decomposed by performing histogram analysis of Hue and Lightness in the HLS colour space and merging using information on human discrimination of wavelength and luminance
Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation
In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations
Modeling Camera Effects to Improve Visual Learning from Synthetic Data
Recent work has focused on generating synthetic imagery to increase the size
and variability of training data for learning visual tasks in urban scenes.
This includes increasing the occurrence of occlusions or varying environmental
and weather effects. However, few have addressed modeling variation in the
sensor domain. Sensor effects can degrade real images, limiting
generalizability of network performance on visual tasks trained on synthetic
data and tested in real environments. This paper proposes an efficient,
automatic, physically-based augmentation pipeline to vary sensor effects
--chromatic aberration, blur, exposure, noise, and color cast-- for synthetic
imagery. In particular, this paper illustrates that augmenting synthetic
training datasets with the proposed pipeline reduces the domain gap between
synthetic and real domains for the task of object detection in urban driving
scenes
Text Extraction from Web Images Based on A Split-and-Merge Segmentation Method Using Color Perception
This paper describes a complete approach to the segmentation and extraction of text from Web images for subsequent recognition, to ultimately achieve both effective indexing and presentation by non-visual means (e.g., audio). The method described here (the first in the authors’ systematic approach to exploit human colour perception) enables the extraction of text in complex situations such as in the presence of varying colour (characters and background). More precisely, in addition to using structural features, the segmentation follows a split-and-merge strategy based on the Hue-Lightness- Saturation (HLS) representation of colour as a first approximation of an anthropocentric expression of the differences in chromaticity and lightness. Character-like components are then extracted as forming textlines in a number of orientations and along curves
Luminance cues constrain chromatic blur discrimination in natural scene stimuli
Introducing blur into the color components of a natural scene has very little effect on its percept, whereas blur introduced into the luminance component is very noticeable. Here we quantify the dominance of luminance information in blur detection and examine a number of potential causes. We show that the interaction between chromatic and luminance information is not explained by reduced acuity or spatial resolution limitations for chromatic cues, the effective contrast of the luminance cue, or chromatic and achromatic statistical regularities in the images. Regardless of the quality of chromatic information, the visual system gives primacy to luminance signals when determining edge location. In natural viewing, luminance information appears to be specialized for detecting object boundaries while chromatic information may be used to determine surface properties
Improvements to context based self-supervised learning
We develop a set of methods to improve on the results of self-supervised
learning using context. We start with a baseline of patch based arrangement
context learning and go from there. Our methods address some overt problems
such as chromatic aberration as well as other potential problems such as
spatial skew and mid-level feature neglect. We prevent problems with testing
generalization on common self-supervised benchmark tests by using different
datasets during our development. The results of our methods combined yield top
scores on all standard self-supervised benchmarks, including classification and
detection on PASCAL VOC 2007, segmentation on PASCAL VOC 2012, and "linear
tests" on the ImageNet and CSAIL Places datasets. We obtain an improvement over
our baseline method of between 4.0 to 7.1 percentage points on transfer
learning classification tests. We also show results on different standard
network architectures to demonstrate generalization as well as portability. All
data, models and programs are available at:
https://gdo-datasci.llnl.gov/selfsupervised/.Comment: Accepted paper at CVPR 201
Two Approaches for Text Segmentation in Web Images
There is a significant need to recognise the text in images on web pages, both for effective indexing and for presentation by non-visual means (e.g., audio). This paper presents and compares two novel methods for the segmentation of characters for subsequent extraction and recognition. The novelty of both approaches is the combination of (different in each case) topological features of characters with an anthropocentric perspective of colour perception— in preference to RGB space analysis. Both approaches enable the extraction of text in complex situations such as in the presence of varying colour and texture (characters and background)
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