International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1128
Статья научная
Speech emotions have potential to provide valuable source of information which can lead us toward human perception and decision making process. This paper analyzes the variation and effect on prosodic features (Formant and Pitch) of female and male speakers in two different emotions (angry and neutral) and softwares (PRAAT and MATLAB) in Urdu language using two ways ANOVA testing. The objective of this paper is to determine the significant effect of emotions and softwares on prosodic features (Pitch and Formant) using recorded speech emotion of both male and female voices of same age group in Urdu language. Experimental results of two-way ANOVA testing considerably show that emotions have effect on pitch and formant both in male and female voice unlike software.
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A Study on Classification for Static and Moving Object in Video Surveillance System
Статья научная
Visual surveillance System is used for analysis and interpretation of object behaviors. It involves object classification to understand the visual events in videos. In this review paper various object classification methods are used. Classification technique plays an important role in surveillance system that is used for the classification of both objects like static and moving objects in a better way. The methods in object classification are used to extract meaningful information and various features that are needed for representation of data. In this survey, we described various approaches for moving objects that are used in classification for video surveillance system based on shape and motion.
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A Survey and Theoretical View on Compressive Sensing and Reconstruction
Статья научная
Most of the current embedded systems operate on digital domain even though input and output is analog in nature. All these devices contain ADC (Analog to Digital converter) to convert the analog signal in to digital domain which is used for processing as per the application. Images, videos and other data can be exactly recovered from a set of uniformly spaced samples taken at the Nyquist rate. Due to the recent technology signal bandwidth is becoming wider and wider. To meet the higher demand, signal acquisition system need to be improved. Traditional Nyquist rate which is used in signal acquisition suggests taking more numbers of samples to increase the bandwidth but while reconstruction most of the samples are not used. If samples are as per Nyquist rate then, this increases the complexity of encoder, storage of samples and signal processing. To avoid this new concept Compressive Sensing is used as an alternative for traditional sampling theory. This paper presents a survey and simplified theoretical view on compressive sensing and reconstruction and proposed work is introduced.
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A Survey of Artificial Life and Nature-inspired Techniques in Computer Graphics and Visualization
Статья научная
Artificial life and other nature-inspired techniques have been applied to many problems in computer graphics. Some of these techniques are based on observations of organic systems, such as slime molds and flocking animals, and can mimic some of their behaviors and structures. The emergent behavior of these systems can improve the realism of procedurally-generated assets used in computer graphics applications, such as animation and texture maps. In this work, we provide a survey of these techniques and applications, including cellular automata, differential growth, reaction-diffusion, and Physarum. The techniques are compared and contrasted, and the common themes and patterns are elucidated to create a taxonomy which can be useful to researchers studying existing techniques and developing new ones.
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A Survey of Compressive Sensing Based Greedy Pursuit Reconstruction Algorithms
Статья научная
Conventional approaches to sampling images use Shannon theorem, which requires signals to be sampled at a rate twice the maximum frequency. This criterion leads to larger storage and bandwidth requirements. Compressive Sensing (CS) is a novel sampling technique that removes the bottleneck imposed by Shannon's theorem. This theory utilizes sparsity present in the images to recover it from fewer observations than the traditional methods. It joins the sampling and compression steps and enables to reconstruct with the only fewer number of observations. This property of compressive Sensing provides evident advantages over Nyquist-Shannon theorem. The image reconstruction algorithms with CS increase the efficiency of the overall algorithm in reconstructing the sparse signal. There are various algorithms available for recovery. These algorithms include convex minimization class, greedy pursuit algorithms. Numerous algorithms come under these classes of recovery techniques. This paper discusses the origin, purpose, scope and implementation of CS in image reconstruction. It also depicts various reconstruction algorithms and compares their complexity, PSNR and running time. It concludes with the discussion of the various versions of these reconstruction algorithms and future direction of CS-based image reconstruction algorithms.
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A Survey on PARATREE and SUSVD Decomposition Techniques and Their Use in Array Signal Processing
Статья научная
The present manuscript is intended to review few applications of tensor decomposition model in array signal processing. Tensor decomposition models like HOSVD, SVD and PARAFAC are useful in signal processing. In this paper we shall use higher order tensor decomposition in signal processing. Also, a novel orthogonal non-iterative tensor decomposition technique (SUSVD), which is scalable to arbitrary high dimensional tensor, has been applied in MIMO channel estimation. The SUSVD provides a tensor model with hierarchical tree structure between the factors in different dimensions. We shall use a new model known as PARATREE, which is related to PARAFAC tensor models. The PARAFAC and PARATREE both describe a tensor as a sum of rank-1 tensors, but PARATREE has several advantages over PARAFAC, when it is applied as a lower rank approximation technique. PARATREE is orthogonal, fast and reliable to compute, and the order of the decomposition can be adaptively adjusted. The low rank PARATREE approximation has been applied to measure noise suppression for tensor valued MIMO channel sounding measurements.
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A Survey on Shadow Removal Techniques for Single Image
Статья научная
Shadows are physical phenomena that appear on a surface when direct light from a source is unable to reach the surface due to the presence of an object between the source and the surface. The formation of shadows and their various features has evolved as a topic of discussion among researchers. Though the presence of shadows can aid us in understanding the scene model, it might impair the performance of applications such as object detection. Hence, the removal of shadows from videos and images is required for the faultless working of certain image processing tasks. This paper presents a survey of notable shadow removal techniques for single image available in the literature. For the purpose of the survey, the various shadow removal algorithms are classified under five categories, namely, reintegration methods, relighting methods, patch-based methods, color transfer methods, and interactive methods. Comparative study of qualitative and quantitative performances of these works is also included. The pros and cons of various approaches are highlighted. The survey concludes with the following observations- (i) shadow removal should be performed in real time since it is usually considered as a preprocessing task, (ii) the texture and color information of the regions underlying the shadow must be recovered, (iii) there should be no hard transition between shadow and non-shadow regions after removing the shadows.
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A System for Offline Recognition of Handwritten Characters in Malayalam Script
Статья научная
In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets.
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Статья научная
Encryption is used to prevent data from unauthorized access and with the appalling headway in network technology seen in the past decade; it has become the need of time to encrypt the images before sending over open network. Though researchers has proposed contrastive methods to encrypt images but correlation between pixels RGB value play a imperative part to guess for original image. So, here we introduce a new image encryption method which first rearranges the pixels within image on basis of RGB values and then forward intervening image for encryption. Experimentally it has shown that pixel rearrangement is enough from image encryption point of view but to send image over open network; inter-pixel displacement algorithm is applied to dispense more armament to image before transmission.
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A Unique Wavelet Steganography Based Voice Biometric Protection Scheme
Статья научная
Voice biometric is an easy and cost effective biometric technique which requires minimalistic hardware and software complexity. General voice biometric needs a voice phrase by user which is processed with Mel Filter and Vector Quantized features are extracted. Vector quantization reduces the codebook size but decreases the accuracy of recognition. Therefore we propose a voice biometric system where voice file's non quantized code books are matched with spoken phrase. In order to ensure security to such direct voice sample we embed the voice file in a randomly selected image using DWT technique. Imposters are exposed to only images and are unaware of the voice files. We show that the technique produces better efficiency in comparison to VQ based technique.
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A comparative investigation into edge detection techniques based on computational intelligence
Статья научная
Soft Computing becomes visible in the field of computer science. The soft computing (SC) comprises of several basic methods such as Fuzzy logic (FL), Evolutionary Computation (EC) and Machine Learning (ML). Soft computing has many real-world applications in domestic, commercial and industrial situations. Edge detection in image processing is the most important applications where soft computing becomes popular. Edge detection decreases the measure of information and filters out undesirable information and gives the desirable information in an image. In image processing edge detection is a fundamental step. For this, high level Computational Intelligence based edge detections methods are required for different images. Computational Intelligence deals with ambiguous and low cost solution. The mind of the human is the key factor of the soft computing. In this paper, we included Binary particle Swarm Optimization (BPSO), Distinct Particle Swarm Optimization (DPSO), Genetic Algorithm (GA) and Ant Colony optimization (ACO) techniques. The ground truth images are taken as reference edge images and all the edge images acquired by different computational intelligent techniques for edge detection systems are contrasted with reference edge image with ascertain the Precision, Recall and F-Score. The techniques are tested on 100 test images from the BSD500 datasets. Experimental results show that the BPSO provides promising results in comparison with the other techniques such as DPSO, GA and ACO.
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A computer vision based lane detection approach
Статья научная
Automatic lane detection to help the driver is an issue considered for the advancement of Advanced Driver Assistance Systems (ADAS) and a high level of application frameworks because of its importance in drivers and passerby safety in vehicular streets. But still, now it is a most challenging problem because of some factors that are faced by lane detection systems like as vagueness of lane patterns, perspective consequence, low visibility of the lane lines, shadows, incomplete occlusions, brightness and light reflection. The proposed system detects the lane boundary lines using computer vision-based technologies. In this paper, we introduced a system that can efficiently identify the lane lines on the smooth road surface. Gradient and HLS thresholding are the central part to detect the lane lines. We have applied the Gradient and HLS thresholding to identify the lane line in binary images. The color lane is estimated by a sliding window search technique that visualizes the lanes. The performance of the proposed system is evaluated on the KITTI road dataset. The experimental results show that our proposed method detects the lane on the road surface accurately in several brightness conditions.
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A dataset for speech recognition to support Arabic phoneme pronunciation
Статья научная
It is difficult for some children to pronounce some phonemes such as vowels. In order to improve their pronunciation, this can be done by a human being such as teacher or parents. However, it is difficult to discover the error in the pronunciation without talking with each student individually. With a large number of students in classes nowadays, it is difficult for teachers to communicate with students separately. Therefore, this study proposes an automatic speech recognition system which has the capacity to detect the incorrect phoneme pronunciation. This system can automatically support children to improve their pronunciation by directly asking children to pronounce a phoneme and the system can tell them if it is correct or not. In the future, the system can give them the correct pronunciation and let them practise until they get the correct pronunciation. In order to construct this system, an experiment was done to collect the speech database. In this experiment 89, elementary school children were asked to produce 28 Arabic phonemes 10 times. The collected database contains 890 utterances for each phoneme. For each utterance, fundamental frequency f0, the first 4 formants are extracted and 13 MFCC co-efficients were extracted for each frame of the speech signal. Then 7 statics were applied for each signal. These statics are (max, min, range, mean, mead, variance and standard divination) therefore for each utterance to have 91 features. The second step is to evaluate if the phoneme is correctly pronounced or not using human subjects. In addition, there are six classifiers applied to detect if the phoneme is correctly pronounced or not by using the extracted acoustic features. The experimental results reveal that the proposed method is effective for detecting the miss pronounced phoneme ("أ").
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A double layered segmentation algorithm for cervical cell images based on GHFCM and ABC
Статья научная
Cancer is a life threatening disease and it engulfs the lives of many women. Due to the technology advancement, the medical science is drastically improved. A statistical report claims that the diagnostic decisions of radiologists show more false positive rates, which is very dangerous. However, when the radiologists are supported by computer aided applications, the false positive results are considerably reduced. Understanding the potentiality of computer aided applications, this paper presents a double layered segmentation algorithm for cervical cell images. The entire work is subdivided into three important phases, which are cervical image pre-processing, coarse and fine level segmentation. The pre-processing phase attempts to remove the noise and enhance the image quality by means of adaptive mean filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique respectively. The coarse level segmentation process is achieved by Generalized Hierarchical Fuzzy C Means (GHFCM) and the fine level segmentation process is carried out by Artificial Bee Colony (ABC) algorithm. The performance of the proposed segmentation algorithm is analysed in terms of accuracy, sensitivity and specificity. The experimental results show the efficacy of the proposed segmentation algorithm.
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A heuristic strategy for sub-optimal thick-edged polygonal approximation of 2-D planar shape
Статья научная
This paper presents a heuristic approach to approximate a two-dimensional planar shape using a thick-edged polygonal representation based on some optimal criteria. The optimal criteria primarily focus on derivation of minimal thickness for an edge of the polygonal shape representation to handle noisy contour. Vertices of the shape-approximating polygon are extracted through a heuristic exploration using a digital geometric approach in order to find optimally thick-line to represent a discrete curve. The merit of such strategies depends on how efficiently a polygon having minimal number of vertices can be generated with modest computational complexity as a meaningful representation of a shape without loss of significant visual characteristics. The performance of the proposed frame- work is comparable to the existing schemes based on extensive empirical study with standard data set.
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A low cost indoor positioning system using computer vision
Статья научная
In the era of robotics, positioning is one of the major problems in an indoor environment. A Global Positioning System (GPS), which is quite reliable system when it comes to outdoor environments and its accuracy falls in the range of meters. But for indoor environment, which requires a positioning accuracy in centimeters scale, the GPS cannot achieve this task due to its signal loss and scattering caused by the building walls. Therefore, an Indoor Positioning System (IPS) based on several technologies and techniques has been developed to overcome this issue. Nowadays, IPS becomes an active growing research topic because of its limitless implementations in a variety of applications. This paper represents the development of a low cost optical indoor positioning system solution where a static commercial camera is the only sensor. High accuracy in localization within the range of 1 cm is achieved. Detection, classification, and tracking techniques of an object are tested on mobile robots. The system is ideal for an indoor robotic warehouse application, where minimal infrastructure and cost parameters are required. The resulted positioning data are compared to the real measurement, and sent to the rovers via a lightweight broker-based publish/subscribe messaging protocol called Message Queuing Telemetry Transport (MQTT), where the only requirement between the client publisher and subscriber is the availability of a WLAN connection.
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A low-complexity algorithm for contrast enhancement of digital images
Статья научная
As known, the contrast is a highly important feature by which the visual quality of digital images can be judged as adequate or poor. Hence, many methods exist for contrast enhancement, where the complexity of those methods usually varies due to the utilization of different concepts. In this article, a simple yet efficient algorithm is introduced for contrast enhancement of digital images. The proposed algorithm consists of four distinct stages: In the first stage, the hyperbolic sine function is applied to provide a simple contrast modification. In the second stage, a modified power-law function is utilized to control the amount of contrast adjustment. In the third stage, the standard sigmoid function is used to remap the image pixels into an “S” shape, which can provide further contrast enhancement. In the final stage, a contrast stretching function is applied to remap the image pixels into their natural dynamic range. The performed computer experiments on different low-contrast images demonstrated the efficiency of the proposed algorithm in processing synthetic and real degraded images, as it provided better and clearer results when compared to several existing contrast enhancement algorithms. To end with, the proposed algorithm can be used as a contrast processing step in many image-related applications.
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A machine learning algorithm for biomedical images compression using orthogonal transforms
Статья научная
Compression methods are increasingly used for medical images for efficient transmission and reduction of storage space. In this work, we proposed a compression scheme for colored biomedical image based on vector quantization and orthogonal transforms. The vector quantization relies on machine learning algorithm (K-Means and Splitting Method). Discrete Walsh Transform (DWaT) and Discrete Chebyshev Transform (DChT) are two orthogonal transforms considered. In a first step, the image is decomposed into sub-blocks, on each sub-block we applied the orthogonal transforms. Machine learning algorithm is used to calculate the centers of clusters and generates the codebook that is used for vector quantization on the transformed image. Huffman encoding is applied to the index resulting from the vector quantization. Parameters Such as Mean Square Error (MSE), Mean Average Error (MAE), PSNR (Peak Signal to Noise Ratio), compression ratio, compression and decompression time are analyzed. We observed that the proposed method achieves excellent performance in image quality with a reduction in storage space. Using the proposed method, we obtained a compression ratio greater than 99.50 percent. For some codebook size, we obtained a MSE and MAE equal to zero. A comparison between DWaT, DChT method and existing literature method is performed. The proposed method is really appropriate for biomedical images which cannot tolerate distortions of the reconstructed image because the slightest information on the image is important for diagnosis.
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Статья научная
Removing the noise from digital color images plays a vital role in many of the image processing applications. Salt and Pepper noise is one type of the impulse noise which corrupts images during image capture or transmission or storage etc. This paper proposes and implements a new decision based median filter using cloud model to restore the highly corrupted digital color images. The proposed filter is tested on different images and shows better performance than standard median filter, adaptive median filter, decision based median filter and modified decision based median filter in terms of root mean square error, peak signal to noise ratio and image quality index.
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Статья научная
Skew detection and correction is one of the major preprocessing steps in the document analysis and understanding. In this paper we are proposing a new method called “Principle-axis farthest pairs Quadrilateral (PFPQ)” mainly for detecting skew in the Telugu language document and also in other Indian languages. One of the popular and classical languages of India is Telugu language. The Telugu language is spoken by more than 80 million people. The Telugu language consists of simple and complex characters attached with some extra marks known as “maatras” and “vatthulu”. This makes the process of skewing of Telugu document is more complex when compared to other languages. The PFPQ, initially performs pre-processing and divides the text in to connected components and estimates principle axis furthest pair quadrilateral then removes the small and large portions of quadrilaterals of connected components. Then by using painting and directional smearing algorithms the PFPQ estimates the skew angle and performs the de-skew. We tested extensively the proposed algorithm with five different kinds of documents collected from various categories i.e., Newspapers, Magazines, Textbooks, handwritten documents, Social media and documents of other Indian languages. The images of these documents also contain complex categories like scientific formulas, statistical tables, trigonometric functions, images, etc. and encouraging results are obtained.
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