5 research outputs found
A Hough Transform based Technique for Text Segmentation
Text segmentation is an inherent part of an OCR system irrespective of the
domain of application of it. The OCR system contains a segmentation module
where the text lines, words and ultimately the characters must be segmented
properly for its successful recognition. The present work implements a Hough
transform based technique for line and word segmentation from digitized images.
The proposed technique is applied not only on the document image dataset but
also on dataset for business card reader system and license plate recognition
system. For standardization of the performance of the system the technique is
also applied on public domain dataset published in the website by CMATER,
Jadavpur University. The document images consist of multi-script printed and
hand written text lines with variety in script and line spacing in single
document image. The technique performs quite satisfactorily when applied on
mobile camera captured business card images with low resolution. The usefulness
of the technique is verified by applying it in a commercial project for
localization of license plate of vehicles from surveillance camera images by
the process of segmentation itself. The accuracy of the technique for word
segmentation, as verified experimentally, is 85.7% for document images, 94.6%
for business card images and 88% for surveillance camera images
A method for nose-tip based 3D face registration using maximum intensity algorithm
In this paper we present a novel technique of registering 3D images across
pose. In this context, we have taken into account the images which are aligned
across X, Y and Z axes. We have first determined the angle across which the
image is rotated with respect to X, Y and Z axes and then translation is
performed on the images. After testing the proposed method on 472 images from
the FRAV3D database, the method correctly registers 358 images thus giving a
performance rate of 75.84%.Comment: 5 page
Thermal Human face recognition based on Haar wavelet transform and series matching technique
Thermal infrared (IR) images represent the heat patterns emitted from hot
object and they do not consider the energies reflected from an object. Objects
living or non-living emit different amounts of IR energy according to their
body temperature and characteristics. Humans are homoeothermic and hence
capable of maintaining constant temperature under different surrounding
temperature. Face recognition from thermal (IR) images should focus on changes
of temperature on facial blood vessels. These temperature changes can be
regarded as texture features of images and wavelet transform is a very good
tool to analyze multi-scale and multi-directional texture. Wavelet transform is
also used for image dimensionality reduction, by removing redundancies and
preserving original features of the image. The sizes of the facial images are
normally large. So, the wavelet transform is used before image similarity is
measured. Therefore this paper describes an efficient approach of human face
recognition based on wavelet transform from thermal IR images. The system
consists of three steps. At the very first step, human thermal IR face image is
preprocessed and the face region is only cropped from the entire image.
Secondly, Haar wavelet is used to extract low frequency band from the cropped
face region. Lastly, the image classification between the training images and
the test images is done, which is based on low-frequency components. The
proposed approach is tested on a number of human thermal infrared face images
created at our own laboratory and Terravic Facial IR Database. Experimental
results indicated that the thermal infra red face images can be recognized by
the proposed system effectively. The maximum success of 95% recognition has
been achieved.Comment: 12 pages. arXiv admin note: substantial text overlap with
arXiv:1309.100
Automated Thermal Face recognition based on Minutiae Extraction
In this paper an efficient approach for human face recognition based on the
use of minutiae points in thermal face image is proposed. The thermogram of
human face is captured by thermal infra-red camera. Image processing methods
are used to pre-process the captured thermogram, from which different
physiological features based on blood perfusion data are extracted. Blood
perfusion data are related to distribution of blood vessels under the face
skin. In the present work, three different methods have been used to get the
blood perfusion image, namely bit-plane slicing and medial axis transform,
morphological erosion and medial axis transform, sobel edge operators.
Distribution of blood vessels is unique for each person and a set of extracted
minutiae points from a blood perfusion data of a human face should be unique
for that face. Two different methods are discussed for extracting minutiae
points from blood perfusion data. For extraction of features entire face image
is partitioned into equal size blocks and the total number of minutiae points
from each block is computed to construct final feature vector. Therefore, the
size of the feature vectors is found to be same as total number of blocks
considered. A five layer feed-forward back propagation neural network is used
as the classification tool. A number of experiments were conducted to evaluate
the performance of the proposed face recognition methodologies with varying
block size on the database created at our own laboratory. It has been found
that the first method supercedes the other two producing an accuracy of 97.62%
with block size 16X16 for bit-plane 4.Comment: 29 pages, Int. J. Computational Intelligence Studie
Localization of License Plates from Surveillance Camera Images: A Color Feature Based ANN Approach
Traffic monitoring system has now become an essential administrative part in most of the developed and developing countries. In general, such systems monitor/identify the vehicles exceeding speed limits, or monitor the vehicles crossing the stop line at red traffic signal. It may also be used for registering the vehicles getting entry in a shopping mall or in a railway station or in an airport. The key modules of these monitoring systems are: (i) localization of license plates within the image and (ii) recognizing the license number using an OCR system. The present work addresses the first module of the system. The color information of the license plate is used as the knowledge base for training an artificial neural network system using back propagation algorithm. The trained network is then used to find the potential license plate region within a new traffic image. The scheme is applied in a real life outdoor environment at some road crossings in an Indian city. The result is found to be quite satisfactory giving an accuracy of around 80%