Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1056
Статья научная
Ant Colony Optimization (ACO) is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Thus by assigning the weights or priority to the neighboring pixels, the ant decides in which direction it can move. The method is applied on Medical images and experimental results are provided to support the superior performance of the proposed approach and the existing method.
Бесплатно
Edge Information for Boosting Discriminating Power of Texture Retrieval Techniques
Статья научная
Texture is a powerful image property for object and scene characterization, consequently, a large number of techniques has been developed for describing, classifying and retrieving texture images. On the other hand, edge information is proven to be an important cue used by the human visual system. Several physiological experiments have shown that, when looking at an object, human eyes explore different locations of that object through saccadic eye movements but they spend more time fixating edge regions. Based on this result, we hypothesize that a better performance could be obtained when analyzing an image (texture images in this case) if the visual features extracted from edge regions are given higher weights than those extracted from uniform regions. To check the validity of this hypothesis, we have modified several existing texture retrieval techniques in a way that incorporates the proposed idea and compared their performance with that of the original techniques. The results of the experiments that have been conducted on three common datasets confirmed the effectiveness of the proposed approach, since a significant improvement in the retrieval rate is obtained for all tested techniques. The experiments have also shown an improvement in the robustness to noise.
Бесплатно
Edge detection based on ant colony optimization using adaptive thresholding technique
Статья научная
Image edge detection is a process where true edges of an image are identified. In past, gradient based methods in which first or second order pixel difference is used to find discontinuities and if magnitude value of gradient is higher than certain threshold then that pixel under observation is identified as edge pixel. These methods are full of error, because in addition to true edges they also find false edges and infect false edges are more in comparison to true edges. To solve such problem, swarm intelligence based ant colony optimization based edge detection method is detailed where numbers of falsely detected edges are very small. The performance of the ant colony optimization (ACO) is done in terms of Peak Signal to Noise Ratio, Performance Ratio and Efficiency.
Бесплатно
Edibility detection of mushroom using ensemble methods
Статья научная
Mushrooms are the most familiar delicious food which is cholesterol free as well as rich in vitamins and minerals. Though nearly 45,000 species of mushrooms have been known throughout the world, most of them are poisonous and few are lethally poisonous. Identifying edible or poisonous mushroom through the naked eye is quite difficult. Even there is no easy rule for edibility identification using machine learning methods that work for all types of data. Our aim is to find a robust method for identifying mushrooms edibility with better performance than existing works. In this paper, three ensemble methods are used to detect the edibility of mushrooms: Bagging, Boosting, and random forest. By using the most significant features, five feature sets are made for making five base models of each ensemble method. The accuracy is measured for ensemble methods using five both fixed feature set-based models and randomly selected feature set based models, for two types of test sets. The result shows that better performance is obtained for methods made of fixed feature sets-based models than randomly selected feature set-based models. The highest accuracy is obtained for the proposed model-based random forest for both test sets.
Бесплатно
Effect of Reducing Colors Number on the Performance of CBIR System
Статья научная
Taking inspiration from the fact that a human can distinguish only a limited number of colors, reducing the number of colors is an interesting task to be incorpo-rated in image retrieval systems that is based on using only the most discriminative colors, which most of the time yields better results. Accordingly, the main goal of this paper is to study the influence on performance of reducing the colors number contained in images. Accomplishing this task poses an extra overhead on the system, which requires more com-putation time, but, on the other hand, can accelerate the comparison process. Due to their popularity and success, we specifically concentrate this study on histogram in-dexing methods, using both Euclidean distance and histo-gram intersection to assess consequently the distance and the similarity between images. Some simple, pertinent ideas related to the way we compare a pair of images using Euclidean Distance are given in the end of the pa-per, supported by preliminary obtained results.
Бесплатно
Effect of Single and Multiple ROI Coding on JPEG2000 Performance
Статья научная
Images are an integral part of advertisements. Images make the web pages heavy. It increases the response time if the size of the image is large and or available bandwidth is low. The consequence of it is viewer may lose his interest in the particular advertisement if he has to wait for a longer time. Image compression is one of the solutions to this problem. In advertisement images, ROI is of prime importance. Though the context of ROI and background regions are not of prime importance, they cannot be totally discarded. This paper investigates the effect of ROI coding on JPEG2000 performance. It proposes Multiple ROI (MROI) coding for compression of natural and advertisement images at moderate compression ratio. The proposed MROI coding prioritizes ROI codeblocks according to the ROI importance, and contribution of ROI in the specific ROI codeblock. It improves fine-grain accuracy at codeblock level also efficiently utilize the given bit budget with a negligible increase in encoding time.
Бесплатно
Effective Reverse Converter for General Three Moduli Set{(2^n)-1,(2^n)+1,(2^(pn+1))-1}
Статья научная
Residue number system is a non¬-weighted integer number system which uses the residues of division of ordinary numbers by some modules for representing that ordinary numbers. In this paper, the general three moduli set {(2^n)-1,(2^n)+1,(2^(pn+1))-1} based on CRT algorithm is proposed in which “p” is an even number greater than zero. The special case of this set for p=2 which is {(2^n)-1,(2^n)+1,(2^(pn+1))-1} is also described in this paper. Since the dynamic range of this set is odd, some difficult problems in RNS can be easily solved based on this set using parity checking. The proposed reverse converter is better in speed and hardware in comparison to reverse converters in similar dynamic range. Moreover, from the complexity point of view, the internal arithmetic circuits of this moduli set is improved and is less complex than the other sets in similar dynamic range.
Бесплатно
Статья научная
In this work, a 5 state left to right HMM-based Bangla Isolated word speech recognizer has been developed. To train and test the recognizer, a small corpus of various sampling frequencies have been developed in noisy as well as the noiseless environment. The number of filter banks is varied during the feature extraction phase for both MFCC and PLP. The effects of 2nd and 3rd differential coefficients have also been observed. Experimental results exhibit that MFCC based feature extraction technique is better in CLASSROOM environment on the contrary PLP based technique performs better not only in a noiseless environment but also in when AC or FAN noise is present. We have also noticed that higher sampling frequency and higher filter order don’t always help to improve the performance.
Бесплатно
Efficient 2D convolution filters implementations on graphics processing unit using NVIDIA CUDA
Статья научная
Convolution algorithms present a key component and a significant step in image processing field. Despite their high arithmetic complexity, these algorithms are widely used because of their great importance for extracting image properties and features. Convolution algorithms require significant computing time, for that we propose a GPU acceleration of these algorithms by using the programming language CUDA presented by NVIDIA. Since these algorithms consume a lot of computing power, we understand the impact of the implementation of this type of algorithm on the acceleration of processing. GPU implementation present a suitable path to achieve better results than other implementation , for that optimizing time consuming time consuming of applications became an increasingly important task in many research areas. The goal of this work is to try to boost convolution algorithms execution time by adopting GPU implementations to accelerate treatments and to achieve real time constraints.
Бесплатно
Efficient Acoustic Front-End Processing for Tamil Speech Recognition using Modified GFCC Features
Статья научная
Giving suitable input and features are always essential to obtain better accuracy in Automatic Speech Recognition (ASR). The type of signal and feature vectors given as an input is highly essential as the pattern matching algorithms strongly depends on these two components. The primary goal of this paper is to propose a suitable Pre-processing and feature extraction techniques for speaker independent speech recognition for Tamil language. The five pass Pre-processing and three types of modified feature extraction techniques are introduced using Gammatone Filtering and Cochleagram Coefficients (GFCC) to achieve better recognition performance. The modified GFCC features using multi taper Yule walker AR power spectrum, combinational features using Formant Frequencies (FF), combined frequency warping and feature normalization techniques using Linear Predictive Coding (LPC) and Cepstral Mean Normalization (CMN) are investigated. The experimental results prove that the proposed techniques have produced high recognition accuracy when compared with the conventional GFCC feature extraction technique.
Бесплатно
Efficient Algorithm for Railway Tracks Detection Using Satellite Imagery
Статья научная
Satellite imagery can produce maps including roads, railway tracks, buildings, bridges, oceans, lakes, rivers, etc. In developed countries like USA, Canada, Australia, Europe, images produced by Google map are of high resolution and good quality. On the other hand, mostly images of the third world countries like Pakistan, Asian and African countries are of poor quality and not clearly visible. Similarly railway tracks of these countries are hardly visible in Google map. We have developed an efficient algorithm for railway track detection from a low quality image of Google map. This would lead to detect damaged railway track, railway crossings and help to schedule/divert locomotive movements in order to avoid catastrophe.
Бесплатно
Статья научная
This paper presents a design of low complexity multichannel Nearly Perfect Reconstruction (NPR) Cosine Modulated Filter Bank (CMFB). CMFBs are used extensively because of ease realization and the inherent advantage of high stop-band attenuation. But, when the number of channel becomes large, it leads to certain limitations as it would require large number of filter coefficients to be optimized and hence longer CPU time; e.g. 32-band or 64-band CMFB. Large number of filter coefficients would also mean that computational complexity of the prototype filter is extremely increased that tends to slow down the convergence to best possible solution. Here, the prototype filter is designed using modified Interpolated Finite Impulse Response (IFIR) technique where masking filter is replaced by multiplier free cascaded structure and coefficients of model filter are converted to nearest Canonical Signed Digit (CSD). The interpolation factor is chosen in such a way that computational cost of the overall filter and different error parameters are reduced. The proposed approach thus leads to reduction in stop-band energy as well as high Side-Lobe-Fall-off-Rate (SLFOR). Three examples have been included to demonstrate the effectiveness of the proposed technique over the existing design methods and savings in computational complexity is also highlighted.
Бесплатно
Efficient Global and Region Content Based Image Retrieval
Статья научная
In this paper, we present an efficient content based image retrieval system that uses texture and color as visual features to describe the image and its segmented regions. Our contribution is of three directions. First, we use Gabor filters to extract texture features from the whole image or arbitrary shaped regions extracted from it after segmentation. Second, to speed up retrieval, the database images are segmented and the extracted regions are clustered according to their feature vectors using Self Organizing Map (SOM). This process is performed offline before query processing; therefore to answer a query, our system does not need to search the entire database images. Third, to further increase the retrieval accuracy of our system, we combine the region features with global features to obtain a more efficient system. The experimental evaluation of the system is based on a 1000 COREL color image database. From experimentation, it is evident that our system performs significantly better and faster compared with other existing systems. We provide a comparison between retrieval results based on features extracted from the whole image, and features extracted from image regions. The results demonstrate that a combination of global and region based approaches gives better retrieval results for almost all semantic classes.
Бесплатно
Статья научная
The paper proposes a numerical TTS-synthesis system in Marathi, Hindi and English languages. The system is in audible forms based on the sounds generated from several numeric units. A hybrid technique is newly launched for a numerical text-to-speech technology. The technique is divided into different phases. These numerical phases include pre-processing, numeric unit detection, numeric and speech unit matching; speech unit concatenation and speech generation. In order to enhance the syntactic generation of audible forms in three languages, two discipline tests were performed. The prosodic test has been obtained for evaluating on the statistical readings. Overall quality issue (OQI) test is a subjective test which is performed by various persons who are aware of three mentioned languages. On the basis of two distinct parameters of OQI test, all scores are positively provided. Initial parameter compromises with listening quality. The second parameter, awareness rate improves a level of the intelligibility. The ultimate satisfactory results of artificial sound generation in three unrelated languages were touched to humankind voice.
Бесплатно
Efficient Modelling Technique based Speaker Recognition under Limited Speech Data
Статья научная
As on date, Speaker-specific feature extraction and modelling techniques has been designed in automatic speaker recognition (ASR) for a sufficient amount of speech data. Once the speech data is limited the ASR performance degraded drastically. ASR system for limited speech data is always a highly challenging task due to a short utterance. The main goal of ASR to form a judgment for an incoming speaker to the system as being which member of registered speakers. This paper presents a comparison of three different modelling techniques of speaker specific extracted information (i) Fuzzy c-means (FCM) (ii) Fuzzy Vector Quantization2 (FVQ2) and (iii) Novel Fuzzy Vector Quantization (NFVQ). Using these three modelling techniques, we developed a text independent automatic speaker recognition system that is computationally modest and equipped for recognizing a non-cooperative speaker. In this investigation, the speaker recognition efficiency is compared to less than 2 sec of text-independent test and train utterances of Texas Instruments and Massachusetts Institute of Technology (TIMIT) and self-collected database. The efficiency of ASR has been improved by 1% with the baseline by hiding the outliers and assigns them by their closest codebook vectors the efficiency of proposed modelling techniques is 98.8%, 98.1% respectively for TIMIT and self-collected database.
Бесплатно
Статья научная
This research presents a framework to detect a questionable observer depending on a specific activity named “frequent iris movement”. We have focused on some activities and behaviors upon which we can classify one as questionable. So this research area is not only an important part of computer vision and artificial intelligence, but also a major part of human activity recognition (HAR). We have used Haar Cascade Classifier to detect irises of both left and right eyes. Then running some morphological operation we have detected the midpoint between left and right irises; and based on some characteristics of midpoint movement we have detected a specific activity – frequent iris movement. Depending on this activity we are declaring someone as questionable observer. To validate this research we have created our own dataset with 86 videos, where 15 individuals have volunteered. We have achieved an accuracy of 90% for the first 100 frames or 3.33 seconds of each of our videos and an accuracy of 93% for the first 150 frames or 5.00 seconds of each of our videos. No work has been done yet on basis of this specific activity to detect someone as questionable and furthermore our work outperforms most of the existing work on questionable observe detection and suspicious activity recognition.
Бесплатно
Efficient image retrieval through hybrid feature set and neural network
Статья научная
Images are an important part of daily life. Any person cannot easily control the huge repository of digitally existing images. Extensive scanning of the image database is very much essential to search a particular image from the huge repository. In some cases, this procedure becomes very exhaustive also. As a result, if a count of ten thousand, lakhs or considerably more images are included in the image database, then it may be transformed into a tedious and never-ending process. Content-based image retrieval (CBIR) is a technique, which is used for retrieving an image. This type of image retrieval procedure is centered on the real content of the image. This paper proposed a model of the hybrid feature set of Haar wavelets and Gabor features and analyzed with different existing models image retrieval. Content-based image retrieval using hybrid feature set of Haar wavelets and Gabor features superiors on other models.
Бесплатно
Статья научная
Human brain signals obtained by the human brain sensor electrodes measure the cerebral activities on the human brain. The main aim of our research is to improve the human brain activities based on the human brain signal. The entire procedure contains three steps. The first step is to acquire the brain signal, then develop this brain signal with the proposed method and finally improve the human brain activities with this modified brain signal. The entire procedure will proceed in a proposed Neuroheadset device embedded with necessary sensors using the non-invasive technique. This device will help to acquire the brain signal, modify this signal and improve the brain activities with this modified brain signal. In this research, we illustrated the first two steps like signal acquisition and signal modification. In the experiment, we used Electroencephalogram as an efficient non-invasive signal acquisition technique for acquiring the brain signal and also introduced a proposed method to modify this signal. This method helped to improve the human brain signal using the required times of the iteration process. In the experiment level, several iteration processes have been done to get above 90% improvement rate of the brainwaves. In this research, the improved signal has been considered based on the generated brain signal in various aspects like human intelligence, memory and also the capability of better feelings.
Бесплатно
Статья научная
Sun being a non-conventional source of energy produces solar energy which is clean and available in abundance. The power obtained from solar panels is used to charge solar batteries and utilized to get continuous power supply. Solar panels are installed in open atmosphere and when subjected to different weather conditions involve many problems such as damage to different components, loss of power generation and heating. The different deposits on solar panel surface such as cement deposits, bird droppings increase temperature of deposited area and produce heating. The heating in solar panels develop hot spots. Batteries placed in a battery room attain high temperature and produce heat. This overheating affect working and performance of solar panels and batteries. These effects of heat are not visible by naked eye but are visible in thermal images, captured using thermal imaging camera. This paper focus on implementing an efficient visualization technique to segment the desired portion of heat from thermal images of solar panels and batteries using watershed transform.
Бесплатно
Статья научная
We are proposing a unique novel algorithm for tracking human face(s) in different background video sequences. In the beginning, Eigen features and corner points are extracted from the detected face(s). HOG (Histograms of Oriented Gradients) features are isolated from corner points. Eigen and HOG features are combined together. Using these combined features, point tracker keeps track of the face(s) in the frames of the video sequence. Proposed algorithm is being tested on challenging datasets video sequences with technical challenges such as partial occlusion (e.g. moustache, beard, spectacles, helmet, headscarf etc.), changes in expression, variations in illumination and pose; and measured for performance using standard metrics such as accuracy, precision, recall and specificity. Experimental results clearly indicate the robustness of the proposed algorithm on all different background challenging video sequences.
Бесплатно
