International Science Index
Improved Processing Speed for Text Watermarking Algorithm in Color Images
Copyright protection and ownership proof of digital multimedia are achieved nowadays by digital watermarking techniques. A text watermarking algorithm for protecting the property rights and ownership judgment of color images is proposed in this paper. Embedding is achieved by inserting texts elements randomly into the color image as noise. The YIQ image processing model is found to be faster than other image processing methods, and hence, it is adopted for the embedding process. An optional choice of encrypting the text watermark before embedding is also suggested (in case required by some applications), where, the text can is encrypted using any enciphering technique adding more difficulty to hackers. Experiments resulted in embedding speed improvement of more than double the speed of other considered systems (such as least significant bit method, and separate color code methods), and a fairly acceptable level of peak signal to noise ratio (PSNR) with low mean square error values for watermarking purposes.
Lifting Wavelet Transform and Singular Values Decomposition for Secure Image Watermarking
In this paper, we present a technique of secure watermarking of grayscale and color images. This technique consists in applying the Singular Value Decomposition (SVD) in LWT (Lifting Wavelet Transform) domain in order to insert the watermark image (grayscale) in the host image (grayscale or color image). It also uses signature in the embedding and extraction steps. The technique is applied on a number of grayscale and color images. The performance of this technique is proved by the PSNR (Pick Signal to Noise Ratio), the MSE (Mean Square Error) and the SSIM (structural similarity) computations.
Object Detection Based on Plane Segmentation and Features Matching for a Service Robot
With the aging of the world population and the
continuous growth in technology, service robots are more and more
explored nowadays as alternatives to healthcare givers or personal
assistants for the elderly or disabled people. Any service robot
should be capable of interacting with the human companion, receive
commands, navigate through the environment, either known or
unknown, and recognize objects. This paper proposes an approach
for object recognition based on the use of depth information and
color images for a service robot. We present a study on two of the
most used methods for object detection, where 3D data is used to
detect the position of objects to classify that are found on horizontal
surfaces. Since most of the objects of interest accessible for service
robots are on these surfaces, the proposed 3D segmentation reduces
the processing time and simplifies the scene for object recognition.
The first approach for object recognition is based on color histograms,
while the second is based on the use of the SIFT and SURF feature
descriptors. We present comparative experimental results obtained
with a real service robot.
Contrast Enhancement of Color Images with Color Morphing Approach
Low contrast images can result from the wrong setting of image acquisition or poor illumination conditions. Such images may not be visually appealing and can be difficult for feature extraction. Contrast enhancement of color images can be useful in medical area for visual inspection. In this paper, a new technique is proposed to improve the contrast of color images. The RGB (red, green, blue) color image is transformed into normalized RGB color space. Adaptive histogram equalization technique is applied to each of the three channels of normalized RGB color space. The corresponding channels in the original image (low contrast) and that of contrast enhanced image with adaptive histogram equalization (AHE) are morphed together in proper proportions. The proposed technique is tested on seventy color images of acne patients. The results of the proposed technique are analyzed using cumulative variance and contrast improvement factor measures. The results are also compared with decorrelation stretch. Both subjective and quantitative analysis demonstrates that the proposed techniques outperform the other techniques.
Image Segmentation Using 2-D Histogram in RGB Color Space in Digital Libraries
This paper presents an unsupervised color image segmentation method. It is based on a hierarchical analysis of 2-D histogram in RGB color space. This histogram minimizes storage space of images and thus facilitates the operations between them. The improved segmentation approach shows a better identification of objects in a color image and, at the same time, the system is fast.
A Proposed Hybrid Color Image Compression Based on Fractal Coding with Quadtree and Discrete Cosine Transform
Fractal based digital image compression is a specific
technique in the field of color image. The method is best suited for
irregular shape of image like snow bobs, clouds, flame of fire; tree
leaves images, depending on the fact that parts of an image often
resemble with other parts of the same image. This technique has
drawn much attention in recent years because of very high
compression ratio that can be achieved. Hybrid scheme incorporating
fractal compression and speedup techniques have achieved high
compression ratio compared to pure fractal compression. Fractal
image compression is a lossy compression method in which selfsimilarity
nature of an image is used. This technique provides high
compression ratio, less encoding time and fart decoding process. In
this paper, fractal compression with quad tree and DCT is proposed
to compress the color image. The proposed hybrid schemes require
four phases to compress the color image. First: the image is
segmented and Discrete Cosine Transform is applied to each block of
the segmented image. Second: the block values are scanned in a
zigzag manner to prevent zero co-efficient. Third: the resulting image
is partitioned as fractals by quadtree approach. Fourth: the image is
compressed using Run length encoding technique.
Outdoor Anomaly Detection with a Spectroscopic Line Detector
One of the tasks of optical surveillance is to detect
anomalies in large amounts of image data. However, if the size of the
anomaly is very small, limited information is available to distinguish
it from the surrounding environment. Spectral detection provides a
useful source of additional information and may help to detect
anomalies with a size of a few pixels or less. Unfortunately, spectral
cameras are expensive because of the difficulty of separating two
spatial in addition to one spectral dimension. We investigate the
possibility of modifying a simple spectral line detector for outdoor
detection. This may be especially useful if the area of interest forms a
line, such as the horizon. We use a monochrome CCD that also
enables detection into the near infrared. A simple camera is attached
to the setup to determine which part of the environment is spectrally
imaged. Our preliminary results indicate that sensitive detection of
very small targets is indeed possible. Spectra could be taken from the
various targets by averaging columns in the line image. By imaging a
set of lines of various widths we found narrow lines that could not be
seen in the color image but remained visible in the spectral line
image. A simultaneous analysis of the entire spectra can produce
better results than visual inspection of the line spectral image. We are
presently developing calibration targets for spatial and spectral
focusing and alignment with the spatial camera. This will present
improved results and more use in outdoor application.
Image Steganography Using Least Significant Bit Technique
In any communication, security is the most important issue in today’s world. In this paper, steganography is the process of hiding the important data into other data, such as text, audio, video, and image. The interest in this topic is to provide availability, confidentiality, integrity, and authenticity of data. The steganographic technique that embeds hides content with unremarkable cover media so as not to provoke eavesdropper’s suspicion or third party and hackers. In which many applications of compression, encryption, decryption, and embedding methods are used for digital image steganography. Due to compression, the nose produces in the image. To sustain noise in the image, the LSB insertion technique is used. The performance of the proposed embedding system with respect to providing security to secret message and robustness is discussed. We also demonstrate the maximum steganography capacity and visual distortion.
Threshold Based Region Incrementing Secret Sharing Scheme for Color Images
In this era of online communication, which transacts data in 0s and 1s, confidentiality is a priced commodity. Ensuring safe transmission of encrypted data and their uncorrupted recovery is a matter of prime concern. Among the several techniques for secure sharing of images, this paper proposes a k out of n region incrementing image sharing scheme for color images. The highlight of this scheme is the use of simple Boolean and arithmetic operations for generating shares and the Lagrange interpolation polynomial for authenticating shares. Additionally, this scheme addresses problems faced by existing algorithms such as color reversal and pixel expansion. This paper regenerates the original secret image whereas the existing systems regenerates only the half toned secret image.
Using Self Organizing Feature Maps for Classification in RGB Images
Artificial neural networks have gained a lot of interest
as empirical models for their powerful representational capacity,
multi input and output mapping characteristics. In fact, most feedforward
networks with nonlinear nodal functions have been proved to
be universal approximates. In this paper, we propose a new
supervised method for color image classification based on selforganizing
feature maps (SOFM). This algorithm is based on
competitive learning. The method partitions the input space using
self-organizing feature maps to introduce the concept of local
neighborhoods. Our image classification system entered into RGB
image. Experiments with simulated data showed that separability of
classes increased when increasing training time. In additional, the
result shows proposed algorithms are effective for color image
Optimized Vector Quantization for Bayer Color Filter Array
Digital cameras to reduce cost, use an image sensor to
capture color images. Color Filter Array (CFA) in digital cameras
permits only one of the three primary (red-green-blue) colors to be
sensed in a pixel and interpolates the two missing components
through a method named demosaicking. Captured data is interpolated
into a full color image and compressed in applications. Color
interpolation before compression leads to data redundancy. This
paper proposes a new Vector Quantization (VQ) technique to
construct a VQ codebook with Differential Evolution (DE)
Algorithm. The new technique is compared to conventional Linde-
Buzo-Gray (LBG) method.
Data Hiding by Vector Quantization in Color Image
With the growing of computer and network, digital
data can be spread to anywhere in the world quickly. In addition,
digital data can also be copied or tampered easily so that the security
issue becomes an important topic in the protection of digital data.
Digital watermark is a method to protect the ownership of digital data.
Embedding the watermark will influence the quality certainly. In this
paper, Vector Quantization (VQ) is used to embed the watermark into
the image to fulfill the goal of data hiding. This kind of watermarking
is invisible which means that the users will not conscious the existing
of embedded watermark even though the embedded image has tiny
difference compared to the original image. Meanwhile, VQ needs a lot
of computation burden so that we adopt a fast VQ encoding scheme by
partial distortion searching (PDS) and mean approximation scheme to
speed up the data hiding process.
The watermarks we hide to the image could be gray, bi-level and
color images. Texts are also can be regarded as watermark to embed.
In order to test the robustness of the system, we adopt Photoshop to
fulfill sharpen, cropping and altering to check if the extracted
watermark is still recognizable. Experimental results demonstrate that
the proposed system can resist the above three kinds of tampering in
Content-Based Color Image Retrieval Based On 2-D Histogram and Statistical Moments
In this paper, we are interested in the problem of
finding similar images in a large database. For this purpose we
propose a new algorithm based on a combination of the 2-D
histogram intersection in the HSV space and statistical moments. The
proposed histogram is based on a 3x3 window and not only on the
intensity of the pixel. This approach overcome the drawback of the
conventional 1-D histogram which is ignoring the spatial distribution
of pixels in the image, while the statistical moments are used to
escape the effects of the discretisation of the color space which is
intrinsic to the use of histograms. We compare the performance of
our new algorithm to various methods of the state of the art and we
show that it has several advantages. It is fast, consumes little memory
and requires no learning. To validate our results, we apply this
algorithm to search for similar images in different image databases.
Fuzzy Inference System Based Unhealthy Region Classification in Plant Leaf Image
In addition to environmental parameters like rain,
temperature diseases on crop is a major factor which affects
production quality & quantity of crop yield. Hence disease
management is a key issue in agriculture. For the management of
disease, it needs to be detected at early stage. So, treat it properly &
control spread of the disease. Now a day, it is possible to use the
images of diseased leaf to detect the type of disease by using image
processing techniques. This can be achieved by extracting features
from the images which can be further used with classification
algorithms or content based image retrieval systems. In this paper,
color image is used to extract the features such as mean and standard
deviation after the process of region cropping. The selected features
are taken from the cropped image with different image size samples.
Then, the extracted features are taken in to the account for
classification using Fuzzy Inference System (FIS).
Color Image Enhancement Using Multiscale Retinex and Image Fusion Techniques
In this paper, an edge-strength guided multiscale
retinex (EGMSR) approach will be proposed for color image contrast
enhancement. In EGMSR, the pixel-dependent weight associated with
each pixel in the single scale retinex output image is computed
according to the edge strength around this pixel in order to prevent
from over-enhancing the noises contained in the smooth dark/bright
regions. Further, by fusing together the enhanced results of EGMSR
and adaptive multiscale retinex (AMSR), we can get a natural fused
image having high contrast and proper tonal rendition. Experimental
results on several low-contrast images have shown that our proposed
approach can produce natural and appealing enhanced images.
Color Image Segmentation Using SVM Pixel Classification Image
The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Wavelet - Based Classification of Outdoor Natural Scenes by Resilient Neural Network
Natural outdoor scene classification is active and
promising research area around the globe. In this study, the
classification is carried out in two phases. In the first phase, the
features are extracted from the images by wavelet decomposition
method and stored in a database as feature vectors. In the second
phase, the neural classifiers such as back-propagation neural network
(BPNN) and resilient back-propagation neural network (RPNN) are
employed for the classification of scenes. Four hundred color images
are considered from MIT database of two classes as forest and street.
A comparative study has been carried out on the performance of the
two neural classifiers BPNN and RPNN on the increasing number of
test samples. RPNN showed better classification results compared to
BPNN on the large test samples.
New Efficient Method for Coding Color Images
In this paper a novel color image compression
technique for efficient storage and delivery of data is proposed. The
proposed compression technique started by RGB to YCbCr color
transformation process. Secondly, the canny edge detection method is
used to classify the blocks into the edge and non-edge blocks. Each
color component Y, Cb, and Cr compressed by discrete cosine
transform (DCT) process, quantizing and coding step by step using
adaptive arithmetic coding. Our technique is concerned with the
compression ratio, bits per pixel and peak signal to noise ratio, and
produce better results than JPEG and more recent published schemes
(like CBDCT-CABS and MHC). The provided experimental results
illustrate the proposed technique that is efficient and feasible in terms
of compression ratio, bits per pixel and peak signal to noise ratio.
Rough Neural Networks in Adapting Cellular Automata Rule for Reducing Image Noise
The reduction or removal of noise in a color image is an essential part of image processing, whether the final information is used for human perception or for an automatic inspection and analysis. This paper describes the modeling system based on the rough neural network model to adaptive cellular automata for various image processing tasks and noise remover. In this paper, we consider the problem of object processing in colored image using rough neural networks to help deriving the rules which will be used in cellular automata for noise image. The proposed method is compared with some classical and recent methods. The results demonstrate that the new model is capable of being trained to perform many different tasks, and that the quality of these results is comparable or better than established specialized algorithms.
A Way of Converting Color Images to Gray Scale Ones for the Color-Blind -Applying to the Part of the Tokyo Subway Map-
This paper proposes a way of removing noises and reducing the number of colors contained in a JPEG image. Main purpose of this project is to convert color images to monochrome images for the color-blind. We treat the crispy color images like the Tokyo subway map. Each color in the image has an important information. But for the color blinds, similar colors cannot be distinguished. If we can convert those colors to different gray values, they can distinguish them. Therefore we try to convert color images to monochrome images.
Undecimated Wavelet Transform Based Contrast Enhancement
A novel undecimated wavelet transform based contrast enhancement algorithmis proposed to for both gray scale andcolor images. Contrast enhancement is realized by tuning the magnitude of approximation coefficients at each level with respect to the approximation coefficients of one higher level during the inverse transform phase in a center/surround enhancement sense.The performance of the proposed algorithm is evaluated using a statistical visual contrast measure (VCM). Experimental results on the proposed algorithm show improvement in terms of the VCM.
Perceptual JPEG Compliant Coding by Using DCT-Based Visibility Thresholds of Color Images
Effective estimation of just noticeable distortion (JND) for images is helpful to increase the efficiency of a compression algorithm in which both the statistical redundancy and the perceptual redundancy should be accurately removed. In this paper, we design a DCT-based model for estimating JND profiles of color images. Based on a mathematical model of measuring the base detection threshold for each DCT coefficient in the color component of color images, the luminance masking adjustment, the contrast masking adjustment, and the cross masking adjustment are utilized for luminance component, and the variance-based masking adjustment based on the coefficient variation in the block is proposed for chrominance components. In order to verify the proposed model, the JND estimator is incorporated into the conventional JPEG coder to improve the compression performance. A subjective and fair viewing test is designed to evaluate the visual quality of the coding image under the specified viewing condition. The simulation results show that the JPEG coder integrated with the proposed DCT-based JND model gives better coding bit rates at visually lossless quality for a variety of color images.
A Hybrid Approach for Color Image Quantization Using K-means and Firefly Algorithms
Color Image quantization (CQ) is an important
problem in computer graphics, image and processing. The aim of
quantization is to reduce colors in an image with minimum distortion.
Clustering is a widely used technique for color quantization; all
colors in an image are grouped to small clusters. In this paper, we
proposed a new hybrid approach for color quantization using firefly
algorithm (FA) and K-means algorithm. Firefly algorithm is a swarmbased
algorithm that can be used for solving optimization problems.
The proposed method can overcome the drawbacks of both
algorithms such as the local optima converge problem in K-means
and the early converge of firefly algorithm. Experiments on three
commonly used images and the comparison results shows that the
proposed algorithm surpasses both the base-line technique k-means
clustering and original firefly algorithm.
Night-Time Traffic Light Detection Based On SVM with Geometric Moment Features
This paper presents an effective traffic lights detection
method at the night-time. First, candidate blobs of traffic lights are
extracted from RGB color image. Input image is represented on the
dominant color domain by using color transform proposed by Ruta,
then red and green color dominant regions are selected as candidates.
After candidate blob selection, we carry out shape filter for noise
reduction using information of blobs such as length, area, area of
boundary box, etc. A multi-class classifier based on SVM (Support
Vector Machine) applies into the candidates. Three kinds of features
are used. We use basic features such as blob width, height, center
coordinate, area, area of blob. Bright based stochastic features are also
used. In particular, geometric based moment-s values between
candidate region and adjacent region are proposed and used to improve
the detection performance. The proposed system is implemented on
Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the
urban and rural road videos. Through the test, we show that the
proposed method using PF, BMF, and GMF reaches up to 93 % of
detection rate with computation time of in average 15 ms/frame.
A Way of Converting Color Images to Gray Scale Ones for the Color Blinds -Reducing the Colors for Tokyo Subway Map-
We proposes a way of removing noises and reducing the number of colors contained in a JPEG image. Main purpose of this project is to convert color images to monochrome images for the color blinds. We treat the crispy color images like the Tokyo subway map. Each color in the image has an important information. But for the color blinds, similar colors cannot be distinguished. If we can convert those colors to different gray values, they can distinguish them.
Fragile Watermarking for Color Images Using Thresholding Technique
In this paper, we propose ablock-wise watermarking scheme for color image authentication to resist malicious tampering of digital media. The thresholding technique is incorporated into the scheme such that the tampered region of the color image can be recovered with high quality while the proofing result is obtained. The watermark for each block consists of its dual authentication data and the corresponding feature information. The feature information for recovery iscomputed bythe thresholding technique. In the proofing process, we propose a dual-option parity check method to proof the validity of image blocks. In the recovery process, the feature information of each block embedded into the color image is rebuilt for high quality recovery. The simulation results show that the proposed watermarking scheme can effectively proof the tempered region with high detection rate and can recover the tempered region with high quality.
Coding of DWT Coefficients using Run-length Coding and Huffman Coding for the Purpose of Color Image Compression
In present paper we proposed a simple and effective method to compress an image. Here we found success in size reduction of an image without much compromising with it-s quality. Here we used Haar Wavelet Transform to transform our original image and after quantization and thresholding of DWT coefficients Run length coding and Huffman coding schemes have been used to encode the image. DWT is base for quite populate JPEG 2000 technique.
Improved Posterized Color Images based on Color Quantization and Contrast Enhancement
A conventional image posterization method
occasionally fails to preserve the shape and color of objects due to the
uneffective color reduction. This paper proposes a new image
posterizartion method by using modified color quantization for
preserving the shape and color of objects and color contrast
enhancement for improving lightness contrast and saturation.
Experiment results show that our proposed method can provide
visually more satisfactory posterization result than that of the
A Novel Approach to Image Compression of Colour Images by Plane Reduction Technique
Several methods have been proposed for color image
compression but the reconstructed image had very low signal to noise
ratio which made it inefficient. This paper describes a lossy
compression technique for color images which overcomes the
drawbacks. The technique works on spatial domain where the pixel
values of RGB planes of the input color image is mapped onto two
dimensional planes. The proposed technique produced better results
than JPEG2000, 2DPCA and a comparative study is reported based
on the image quality measures such as PSNR and MSE.Experiments
on real time images are shown that compare this methodology with
previous ones and demonstrate its advantages.
Tracking Objects in Color Image Sequences: Application to Football Images
In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.