MOST CITED - Top 20 PAPERS
01. Content Based Image Retrieval Using Color and Texture
Manimala Singha and K.Hemachandran
February 2012 | Cited by 170
The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. This paper presents the content based image retrieval, using features like texture and color, called WBCHIR (Wavelet Based Color Histogram Image Retrieval).The texture and color features are extracted through wavelet transformation and color histogram and the combination of these features is robust to scaling and translation of objects in an image. The proposed system has demonstrated a promising and faster retrieval method on a WANG image database containing 1000 general-purpose color images. The performance has been evaluated by comparing with the existing systems in the literature.02. Algorithm and Technique on Various Edge Detection : A Survey
Rashmi, Mukesh Kumar and Rohini Saxe
June 2013 | Cited by 74
An edge may be defined as a set of connected pixels that forms a boundary between two disjoints regions. Edge detection is basically, a method of segmenting an image into regions of discontinuity. Edge detection plays an important role in digital image processing and practical aspects of our life. .In this paper we studied various edge detection techniques as Prewitt, Robert, Sobel, Marr Hildrith and Canny operators. On comparing them we can see that canny edge detector performs better than all other edge detectors on various aspects such as it is adaptive in nature, performs better for noisy image, gives sharp edges , low probability of detecting false edges etc.03. Efficient CBIR Using Color Histogram Processing
Neetu Sharma.S, Paresh Rawat S and Jaikaran Singh.S
March 2011 | Cited by 68
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. The similarity of images depends on the feature representation.However users have difficulties in representing their information needs in queries to content based image retrieval systems. In this paper we investigate two methods for describing the contents of images. The first one characterizes images by global descriptor attributes, while the second is based on color histogram approach.To compute feature vectors for Global descriptor, required time is much less as compared to color histogram. Hence cross correlation value & image descriptor attributes are calculated prior histogram implementation to make CBIR system more efficient.The performance of this approach is measured and results are shown. The aim of this paper is to compare various global descriptor attributes and to make CBIR system more efficient. It is found that further modifications are needed to produce better performance in searching images.-
Sujay Narayana and Gaurav Prasad
December 2010 | Cited by 68
The science of securing a data by encryption is Cryptography whereas the method of hiding secret messages in other messages is Steganography, so that the secret’s very existence is concealed. The term ‘Steganography’ describes the method of hiding cognitive content in another medium to avoid detection by the intruders. This paper introduces two new methods wherein cryptography and steganography are combined to encrypt the data as well as to hide the encrypted data in another medium so the fact that a message being sent is concealed. One of the methods shows how to secure the image by converting it into cipher text by S-DES algorithm using a secret key and conceal this text in another image by steganographic method. Another method shows a new way of hiding an image in another image by encrypting the image directly by S-DES algorithm using a key image and the data obtained is concealed in another image. The proposed method prevents the possibilities of steganalysis also. 05. Red Blood Cells Estimation Using Hough Transform Technique
Nasrul Humaimi Mahmood and Muhammad Asraf Mansor
April 2012 | Cited by 53
The number of red blood cells contributes more to clinical diagnosis with respect to blood diseases. The aim of this research is to produce a computer vision system that can detect and estimate the number of red blood cells in the blood sample image. Morphological is a very powerful tool in image processing, and it is been used to segment and extract the red blood cells from the background and other cells. The algorithm used features such as shape of red blood cells for counting process, and Hough transform is introduced in this process. The result presented here is based on images with normal blood cells. The tested data consists of 10 samples and produced the accurate estimation rate closest to 96% from manual counting.06. Speech Recognition Using HMM with MFCC-An Analysis Using Frequency Specral Decomposion Technique
Ibrahim Patel and Y. Srinivas Rao
December 2010 | Cited by 49
This paper presents an approach to the recognition of speech signal using frequency spectral information with Mel frequency for the improvement of speech feature representation in a HMM based recognition approach. A frequency spectral information is incorporated to the conventional Mel spectrum base speech recognition approach. The Mel frequency approach exploits the frequency observation for speech signal in a given resolution which results in resolution feature overlapping resulting in recognition limit. Resolution decomposition with separating frequency is mapping approach for a HMM based speech recognition system. The Simulation results show an improvement in the quality metrics of speech recognition with respect to computational time, learning accuracy for a speech recognition system.07. Histopathological Image Analysis Using Image Processing Techniques: An Overview
A.D. Belsare and M.M. Mushrif
August 2012 | Cited by 48
This paper reviews computer assisted histopathology image analysis for cancer detection and classification. Histopathology refers to the examination of invasive or less invasive biopsy sample by a pathologist under microscope for locating, analyzing and classifying most of the diseases like cancer. The analysis of histoapthological image is done manually by the pathologist to detect disease which leads to subjective diagnosis of sample and varies with level of expertise of examiner. The pathologist examine the tissue structure, distribution of cells in tissue, regularities of cell shapes and determine benign and malignancy in image. This is very time consuming and more prone to intra and inter observer variability. To overcome this difficulty a computer assisted image analysis is needed for quantitative diagnosis of tissue. In this paper we reviews and summarize the applications of digital image processing techniques for histology image analysis mainly to cover segmentation and disease classification methods.08. A Review Paper : Noise Models in Digital Image Processing
Ajay Kumar Boyat and Brijendra Kumar Joshi
April 2015 | Cited by 42
Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.09. Image Denoising Using New Adaptive Based Median Filter
Suman Shrestha
August 2014 | Cited by 41
Noise is a major issue while transferring images through all kinds of electronic communication. One of the most common noise in electronic communication is an impulse noise which is caused by unstable voltage. In this paper, the comparison of known image denoising techniques is discussed and a new technique using the decision based approach has been used for the removal of impulse noise. All these methods can primarily preserve image details while suppressing impulsive noise. The principle of these techniques is at first introduced and then analysed with various simulation results using MATLAB. Most of the previously known techniques are applicable for the denoising of images corrupted with less noise density. Here a new decision based technique has been presented which shows better performances than those already being used. The comparisons are made based on visual appreciation and further quantitatively by Mean Square error (MSE) and Peak Signal to Noise Ratio (PSNR) of different filtered images.10. Performance Improvement in OFDM System by PAPR Reduction
Suverna Sengar and Partha Pratim Bhattacharya
April 2012 | Cited by 41
Orthogonal Frequency Division Multiplexing (OFDM) is an efficient method of data transmission for high speed communication systems. However, the main drawback of OFDM system is the high Peak to Average Power Ratio (PAPR) of the transmitted signals. OFDM consist of large number of independent subcarriers, as a result of which the amplitude of such a signal can have high peak values. Coding, phase rotation and clipping are among many PAPR reduction schemes that have been proposed to overcome this problem. Here two different PAPR reduction methods e.g. partial transmit sequence (PTS) and selective mapping (SLM) are used to reduce PAPR. Significant reduction in PAPR has been achieved using these techniques. The performances of the two methods are then compared.11. Combination of Local Descriptors and Global Features for Leaf Recognition
Maliheh Shabanzade, Morteza Zahedi and Seyyed Amin Aghvami
September 2011 | Cited by 39
Automatic leaf recognition system is a case coming to improve time-consuming and troublesome tasks which have mainly been carried out by botanists manually. This application as judged by common characteristics is popular in institutes for discovering new plant species, modernizing the management of botanical gardens and horticulture fields. In order to conduct a leaf recognition system, the features must be sufficiently distinctive to identify specific objects among many alternatives, where contain both local and global properties. So far, many researchers have represented some techniques which use local or global features only where face problems, such as many images are captured in different intensity, they are maybe sick or calamity, leaves have been damaged or cropped and so on. In this paper, a new method for leaf recognition system is proposed where both local descriptors and global features are employed, combined and finally the most discriminant features are selected by employing a linear discriminant analysis method. The experimental results show that using the feature vector containing the local features and global characteristics leads us to obtain 94.3% recognition rate.12. Review of Motion Estimation and Video Stabilization Techniques for Hand Held Mobile Video
Paresh Rawat and Jyoti Singhai
June 2011 | Cited by 39
Video stabilization is a video processing technique to enhance the quality of input video by removing the undesired camera motions. There are various approaches used for stabilizing the captured videos. Most of the existing methods are either very complex or does not perform well for slow and smooth motion of hand held mobile videos. Hence it is desired to synthesis a new stabilized video sequence, by removing the undesired motion between the successive frames of the hand held mobile video. Various 2D and 3D motion models used for the motion estimation and stabilization. The paper presents the review of the various motion models, motion estimation methods and the smoothening techniques. Paper also describes the direct pixel based and feature based methods of estimating the inter frame error. Some of the results of the differential motion estimation are also presented. Finally it closes with a open discussion of research problems in the area of motion estimation and stabilization.13. Ubiquitous Positioning: A Taxonomy for Location Determination on Mobile Navigation System
Wan Bejuri, Wan Mohd. Yaakob, Mohamad, Mohd. Murtadha and Sapri, Maimunah
March 2011 | Cited by 39
The location determination in obstructed area can be very challenging especially if Global Positioning System are blocked. Users will find it difficult to navigate directly on-site in such condition, especially indoor car park lot or obstructed environment. Sometimes, it needs to combine with other sensors and positioning methods in order to determine the location with more intelligent, reliable and ubiquity. By using ubiquitous positioning in mobile navigation system, it is a promising ubiquitous location technique in a mobile phone since as it is a familiar personal electronic device for many people. However, there is an increasing need for better development of proposed ubiquitous positioning systems. System developers are also lacking of good frameworks for understanding different options during building ubiquitous positioning systems. This paper proposes taxonomy to address both of these problems. The proposed taxonomy has been constructed from a literature study of papers and articles on positioning estimation that can be used to determine location everywhere on mobile navigation system. For researchers the taxonomy can also be used as an aid for scoping out future research in the area of ubiquitous positioning.14. An Overview of Multimodal Biometrics
P.S. Sanjekar and J. B. Patil
February 2013 | Cited by 37
Unimodal biometrics has several problems such as noisy data, intra class variation, inter class similarities, non universality and spoofing which cause this system less accurate and secure. To overcome these problems and to increase level of security multimodal biometrics is used. Multimodal biometrics makes the use of multiple source of information for personal authentication. Multimodal biometrics has becoming very popular now days since it is at the frontier of unimodal biometrics. This paper presents an overview of multimodal biometrics. This includes the block diagram of general multimodal biometrics, modules of multimodal biometric system, different levels of fusion in multimodal biometrics and related work is also covered in this paper.15. Image Encryption Using Differential Evolution Approach in Frequency Domain
Ibrahim S I Abuhaiba and Maaly A S Hassan
March 2011 | Cited by 37
This paper presents a new effective method for image encryption which employs magnitude and phase manipulation using Differential Evolution (DE) approach. The novelty of this work lies in deploying the concept of keyed discrete Fourier transform (DFT) followed by DE operations for encryption purpose. To this end, a secret key is shared between both encryption and decryption sides. Firstly two dimensional (2-D) keyed discrete Fourier transform is carried out on the original image to be encrypted. Secondly crossover is performed between two components of the encrypted image, which are selected based on Linear Feedback Shift Register (LFSR) index generator. Similarly, keyed mutation is performed on the real parts of a certain components selected based on LFSR index generator. The LFSR index generator initializes it seed with the shared secret key to ensure the security of the resulting indices. The process shuffles the positions of image pixels. A new image encryption scheme based on the DE approach is developed which is composed with a simple diffusion mechanism. The deciphering process is an invertible process using the same key. The resulting encrypted image is found to be fully distorted, resulting in increasing the robustness of the proposed work. The simulation results validate the proposed image encryption scheme.16. Combining Neural Networks for Skin Detection
Chelsia Amy Doukim, Jamal Ahmad Dargham, Ali Chekima and Sigeru Omatu
December 2010 | Cited by 36
Two types of combining strategies were evaluated namely combining skin features and combining skin classifiers. Several combining rules were applied where the outputs of the skin classifiers are combined using binary operators such as the AND and the OR operators, “Voting”, “Sum of Weights” and a new neural network. Three chrominance components from the YCbCr colour space that gave the highest correct detection on their single feature MLP were selected as the combining parameters. A major issue in designing a MLP neural network is to determine the optimal number of hidden units given a set of training patterns. Therefore, a “coarse to fine search” method to find the number of neurons in the hidden layer is proposed. The strategy of combining Cb /Cr and Cr features improved the correct detection by 3.01% compared to the best single feature MLP given by Cb-Cr . The strategy of combining the outputs of three skin classifiers using the “Sum of Weights” rule further improved the correct detection by 4.38% compared to the best single feature MLP.17. High Speed and Area Efficient 2D DWT Processor Based Image Compression
Sugreev Kaur and Rajesh Mehra
December 2010 | Cited by 31
This paper presents a high speed and area efficient DWT processor based design for Image Compression applications. In this proposed design, pipelined partially serial architecture has been used to enhance the speed along with optimal utilization and resources available on target FPGA. The proposed model has been designed and simulated using Simulink and System Generator blocks, synthesized with Xilinx Synthesis tool (XST) and implemented on Spartan 2 and 3 based XC2S100-5tq144 and XC3S500E-4fg320 target device. The results show that proposed design can operate at maximum frequency 231 MHz in case of Spartan 3 by consuming power of 117mW at 28 degree/c junction temperature. The result comparison has shown an improvement of 15% in speed.18. Vehicle Detection and Tracking Techniques : A Concise Review
Raad Ahmed Hadi, Ghazali Sulong and Loay Edwar George
February 2014 | Cited by 30
Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems19. Feature Extraction Using MFCC
Shikha Gupta, Jafreezal Jaafar, Wan Fatimah wan Ahmad and Arpit Bansal
August 2013 | Cited by 30
Mel Frequency Ceptral Coefficient is a very common and efficient technique for signal processing. This paper presents a new purpose of working with MFCC by using it for Hand gesture recognition. The objective of using MFCC for hand gesture recognition is to explore the utility of the MFCC for image processing. Till now it has been used in speech recognition, for speaker identification. The present system is based on converting the hand gesture into one dimensional (1-D) signal and then extracting first 13 MFCCs from the converted 1-D signal. Classification is performed by using Support Vector Machine. Experimental results represents that proposed application of using MFCC for gesture recognition have very good accuracy and hence can be used for recognition of sign language or for other household application with the combination for other techniques such as Gabor filter, DWT to increase the accuracy rate and to make it more efficient.20. Artificial Neural Network Based Optical Character Recognition
Vivek Shrivastava and Navdeep Sharma
October 2012 | Cited by 28
Optical Character Recognition deals in recognition and classification of characters from an image. For the recognition to be accurate, certain topological and geometrical properties are calculated, based on which a character is classified and recognized. Also, the Human psychology perceives characters by its overall shape and features such as strokes, curves, protrusions, enclosures etc. These properties, also called Features are extracted from the image by means of spatial pixel-based calculation. A collection of such features, called Vectors, help in defining a character uniquely, by means of an Artificial Neural Network that uses these Feature Vectors.