3,052 research outputs found
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 perceptionâ 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)
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 perceptionâ 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)
A Fuzzy Approach to Text Segmentation in Web Images Based on Human Colour Perception
This chapter describes a new approach for the segmentation of text in images on Web pages. In the same spirit as the authorsâ previous work on this subject, this approach attempts to model the ability of humans to differentiate between colours. In this case, pixels of similar colour are first grouped using a colour distance defined in a perceptually uniform colour space (as opposed to the commonly used RGB). The resulting colour connected components are then grouped to form larger (character-like) regions with the aid of a propinquity measure, which is the output of a fuzzy inference system. This measure expresses the likelihood for merging two components based on two features. The first feature is the colour distance between the components, in the L*a*b* colour space. The second feature expresses the topological relationship of two components. The results of the method indicate a better performance than previous methods devised by the authors and possibly better (a direct comparison is not really possible due to the differences in application domain characteristics between this and previous methods) performance to other existing methods
Unsupervised Text Extraction from G-Maps
This paper represents an text extraction method from Google maps, GIS
maps/images. Due to an unsupervised approach there is no requirement of any
prior knowledge or training set about the textual and non-textual parts. Fuzzy
CMeans clustering technique is used for image segmentation and Prewitt method
is used to detect the edges. Connected component analysis and gridding
technique enhance the correctness of the results. The proposed method reaches
98.5% accuracy level on the basis of experimental data sets.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer
Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201
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
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching
This paper proposes a content based image retrieval (CBIR) system using the
local colour and texture features of selected image sub-blocks and global
colour and shape features of the image. The image sub-blocks are roughly
identified by segmenting the image into partitions of different configuration,
finding the edge density in each partition using edge thresholding followed by
morphological dilation. The colour and texture features of the identified
regions are computed from the histograms of the quantized HSV colour space and
Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture
feature vectors is computed for each region. The shape features are computed
from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching
(IRM) algorithm is used for finding the minimum distance between the sub-blocks
of the query and target image. Experimental results show that the proposed
method provides better retrieving result than retrieval using some of the
existing methods.Comment: 7 page
Hierarchical indexing for region based image retrieval
Region-based image retrieval system has been an active research area. In this study we developed an improved region-based image retrieval system. The system applies image segmentation to divide an image into discrete regions, which if the segmentation is ideal, correspond to objects. The focus of this research is to improve the capture of regions so as to enhance indexing and retrieval performance and also to provide a better similarity distance computation. During image segmentation, we developed a modified k-means clustering algorithm for image retrieval where hierarchical clustering algorithm is used to generate the initial number of clusters and the cluster centers. In addition, to during similarity distance computation we introduced object weight based on object\u27s uniqueness. Therefore, objects that are not unique such as trees and skies will have less weight. The experimental evaluation is based on the same 1000 COREL color image database with the FuzzyClub, IRM and Geometric Histogram and the performance is compared between them. As compared with existing technique and systems, such as IRM, FuzzyClub, and Geometric Histogram, our study demonstrate the following unique advantages: (i) an improvement in image segmentation accuracy using the modified k-means algorithm (ii)an improvement in retrieval accuracy as a result of a better similarity distance computation that considers the importance and uniqueness of objects in an image
Research Pattern Classification using imaging techniques for Infarct and Hemorrhage Identification in the Human Brain
Medical Image analysis and processing has great
significance in the field of medicine, especially in Non-
invasive treatment and clinical study. Medical imaging
techniques and analysis tools enable the Doctors and
Radiologists to arrive at a specific diagnosis. Medical Image
Processing has emerged as one of the most important tools
to identify as well as diagnose various disorders. Imaging
helps the Doctors to visualize and analyze the image for
understanding of abnormalities in internal structures. The
medical images data obtained from Bio-medical Devices
which use imaging techniques like Computed Tomography
(CT), Magnetic Resonance Imaging (MRI) and
Mammogram, which indicates the presence or absence of
the lesion along with the patient history, is an important
factor in the diagnosis. The algorithm proposes the use of
Digital Image processing tools for the identification of
Hemorrhage and Infarct in the human brain, by using a
semi-automatic seeded region growing algorithm for the
processing of the clinical images. The algorithm has been
extended to the Real-Time Data of CT brain images and
uses an intensity-based growing technique to identify the
infarct and hemorrhage affected area, of the brain. The
objective of this paper is to propose a seeded region
growing algorithm to assist the Radiologists in identifying
the Hemorrhage and Infarct in the human brain and to arrive
at a decision faster and accurate.¹Lp€
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