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نتیجه جستجو - Histogram

تعداد مقالات یافته شده: 40
ردیف عنوان نوع
1 Plant leaf disease detection using computer vision and machine learning algorithms
تشخیص بیماری برگ گیاه با استفاده از بینایی کامپیوتری و الگوریتم های یادگیری ماشین-2022
Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recom- mended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farm- ers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to 256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally, the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM), Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples.
keywords: شبکه های عصبی کانولوشنال | تبدیل موجک گسسته | تجزیه و تحلیل مؤلفه های اصلی | نزدیکترین همسایه | بیماری برگ | Convolutional Neural Networks | Discrete Wavelet Transform | Principal Component Analysis | Nearest Neighbor | Leaf disease
مقاله انگلیسی
2 oReview on fingerprint-based identification system
مرور سیستم شناسایی مبتنی بر اثر انگشت-2021
The Biometric fingerprints are the widely utilized personal recognition tool because of their uniqueness, reliability and individuality. The fingerprint images consist of a design of the canyon & corrugation on human’s fingertips. Fingerprint validation is perhaps the most experienced methods for every biometric technique that has been rigorously substantiate through several applications. Every human being recognition methods using fingerprints are depending on one of the 3 methods: hybrid, correlation-based and Minutiae-based. This paper gives the review of different fingerprint recognition methods & then discusses the general minutiae-depend fingerprint identification systems. In present time the best form of recognizing the person or investigation of any case is figure print. Identifying speculate depend on fingerprint is a proceeding that is exceedingly important to the forensics & law for enforcement agencies. A small numbers of minutiae & the noise attribute make it exceedingly difficult to instinctive match the fingerprints to their acquaintance full prints that are accumulated in databases.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Virtual Conference on Sustainable Materials (IVCSM-2k20).
Keywords: Correlation | Finger prints | Histogram | Ridges | Segmentation
مقاله انگلیسی
3 Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics
ارزیابی کیفیت چای سیاه کنگو با استفاده از یک سیستم بینایی کامپیوتری ساخته شده در آزمایشگاه همراه با ویژگی های مورفولوژیکی و شیمی سنجی-2021
The feature of external shape in tea is a vital quality index that determines the rank quality of tea. The potential of a lab-made computer vision system (CVS) coupled with morphological features and chemometric tools is investigated for evaluating Congou black tea quality. First, Raw images of 700 tea samples from seven different quality grades are acquired using the CVS. The original images collected are processed by graying, binarization, and median de-noising. Then, six morphological parameters (viz. width, length, area, perimeter, length-width ratio, and rectangularity) from the samples are extracted by the shape segmentation of each tea leaf image, and the corresponding feature histogram is obtained. Finally, support vector machine (SVM) and least squares- support vector machine (LS-SVM) are utilized to build identification models based on the histogram distribution characteristic vectors. Three kernel methods (linear kernel, polynomial kernel, and radial basis function kernel) are compared for monitoring tea quality. The results show that the optimal LS-SVM model has a 12% higher correct discrimination rate (CDR) than the SVM model. The polynomial kernel LS-SVM model yields satisfactory classification results with the CDR of 100% based on selected six shape features in the calibration and prediction sets. This work demonstrates that it is feasible to discriminate Congou black tea quality using CVS technology along with morphological features and nonlinear chemometric methods. A new perspective on the sizes of morphological characteristics is proposed as an identifier of Congou black tea quality.
Keywords: Congou black tea | Computer vision system | Morphological features | Least squares-support vector machine | Kernel method
مقاله انگلیسی
4 Microplastic abundance quantification via a computer-vision-based chemometrics-assisted approach
تعیین میزان فراوانی میکروپلاستیک از طریق رویکرد شیمی درمانی مبتنی بر بینایی ماشین-2021
Microplastic (MP) contamination is a topic of growing global concern; these particles are ubiquitous in environmental ecosystems and have been found in aquatic, terrestrial, and atmospheric mediums. However, the protocols to quantify MPs in environmental samples have limitations and may lead to overestimation and/or underestimation of the plastic debris. Therefore, the aim of this research was to develop a simple procedure to determine the abundance of MPs using digital image processing and chemometric treatment. The proposed method combined computer-vision-based and multivariate calibration by partial least squares coupled with in- terval selection (iPLS and successive algorithm projection - iSPA). The abundance ranges of the yellow, blue, black, colourless, green, and red MPs were 1–212, 7–134, 0–50, 6–290, 0–113, and 20–392, respectively. When the models were applied to an independent set of samples, the following RMSEP values were found: 9.8 (yellow),6.4 (blue), 3.5 (black), 8.1 (colourless), 7.5 (green), and 19.3 (red). The results showed that image processing has the potential to quantify MPs with respect their colour. This method could help to reduce time-consuming and to avoid subjectivity in future analyses.
Keywords: Image processing | Colour histogram | Multivariate calibration | Microplastic contamination
مقاله انگلیسی
5 Catch them alive: A malware detection approach through memory forensics, manifold learning and computer vision
آنها را زنده بگیرید: یک روش تشخیص بدافزار از طریق پزشکی قانونی حافظه ، یادگیری چندگانه و بینایی ماشین-2021
The everlasting increase in usage of information systems and online services have triggered the birth of the new type of malware which are more dangerous and hard to detect. In particular, according to the recent reports, the new type of fileless malware infect the victims’ devices without a persistent trace (i.e. file) on hard drives. Moreover, existing static malware detection methods in literature often fail to detect sophisticated malware utilizing various obfuscation and encryption techniques. Our contribution in this study is two-folded. First, we present a novel approach to recognize malware by capturing the memory dump of suspicious processes which can be represented as a RGB image. In contrast to the conventional approaches followed by static and dynamic methods existing in the literature, we aimed to obtain and use memory data to reveal visual patterns that can be classified by employing computer vision and machine learning methods in a multi-class open-set recognition regime. And second, we have applied a state of art manifold learning scheme named UMAP to improve the detection of unknown malware files through binary classification. Throughout the study, we have employed our novel dataset covering 4294 samples in total, including 10 malware families along with the benign executables. Lastly, we obtained their memory dumps and converted them to RGB images by applying 3 different rendering schemes. In order to generate their signatures (i.e. feature vectors), we utilized GIST and HOG (Histogram of Gradients) descriptors as well as their combination. Moreover, the obtained signatures were classified via machine learning algorithms of j48, RBF kernel-based SMO, Random Forest, XGBoost and linear SVM. According to the results of the first phase, we have achieved prediction accuracy up to 96.39% by employing SMO algorithm on the feature vectors combined with GIST+HOG. Besides, the UMAP based manifold learning strategy has improved accuracy of the unknown malware recognition models up to 12.93%, 21.83%, 20.78% on average for Random Forest, linear SVM and XGBoost algorithms respectively. Moreover, on a commercially available standard desktop computer, the suggested approach takes only 3.56 s for analysis on average. The results show that our vision based scheme provides an effective protection mechanism against malicious applications.
Keywords: Memory forensics | Memory dump | Machine learning | Computer vision | Malware detection | Manifold learning
مقاله انگلیسی
6 Joint discriminative feature learning for multimodal finger recognition
یادگیری ویژگی های تبعیض آمیز مشترک برای تشخیص انگشتان چند حالته-2021
Recently, finger-based multimodal biometrics, due to its high security and stability, has received considerable attention compared with unimodal biometrics. However, existing multimodal finger feature ex- traction approaches separately extract the features of different modalities, at the same time ignoring correlations among these different modalities. Furthermore, most of the conventional finger feature representation approaches are hand-crafted by design, which require strong prior knowledge. It is therefore very important to explore and develop a suitable feature representation and fusion strategy for mul- timodal biometrics recognition. In this paper, we proposed a joint discriminative feature learning (JDFL) framework for multimodal finger recognition by combining finger vein (FV) and finger knuckle print (FKP) patterns. For the FV and FKP images, we first established the informative dominant direction vector by convoluting a bank of Gabor filters and the original finger image. Then, we developed a simple yet effective feature learning algorithm, which simultaneously maximized the distance of between-class samples and minimized the distance of within-class samples, as well as maximized the correlation among inter- modality samples of the within-class. Finally, we integrated the block-wise histograms of the learned feature maps together for multimodal finger fusion recognition. Experimental results demonstrated that the proposed approach has a better recognition performance than state-of-the-art finger recognition methods.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Multimodal biometrics | Feature fusion | Inter-modality | Joint feature learning
مقاله انگلیسی
7 Vision-assisted recognition of stereotype behaviors for early diagnosis of Autism Spectrum Disorders
تشخیص رفتارهای کلیشه ای برای تشخیص زودهنگام اختلالات طیف اوتیسم با کمک بینایی ماشین-2021
Medical diagnosis supported by computer-assisted technologies is getting more popularity and acceptance among medical society. In this paper, we propose a non-intrusive vision-assisted method based on human action recognition to facilitate the diagnosis of Autism Spectrum Disorder (ASD). We collected a novel and comprehensive video dataset f the most distinctive Stereotype actions of this disorder with the assistance of professional clinicians. Several frameworks as a function of different input modalities were developed and used to produce extensive baseline results. Various local descriptors, which are commonly used within the Bag-of-Visual-Words approach, were tested with Multi-layer Perceptron (MLP), Gaussian Naive Bayes (GNB), and Support Vector Machines (SVM) classifiers for recognizing ASD associated behaviors. Additionally, we developed a framework that first receives articulated pose-based skeleton sequences as input and follows an LSTM network to learn the temporal evolution of the poses. Finally, obtained results were compared with two fine-tuned deep neural networks: ConvLSTM and 3DCNN. The results revealed that the Histogram of Optical Flow (HOF) descriptor achieves the best results when used with MLP classifier. The promising baseline results also confirmed that an action-recognition-based system can be potentially used to assist clinicians to provide a reliable, accurate, and timely diagnosis of ASD disorder.© 2021 Elsevier B.V. All rights reserved.
Keywords: Action recognition | Autism Spectrum Disorder | Patient monitoring | Bag-of-visual-words | Convolutional neural networks
مقاله انگلیسی
8 Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors
ارزیابی کیفی چای سیاه کیمون با ترکیب داده های بدست آمده از طیف سنجی بازتابنده مادون قرمز نزدیک و حسگرهای بینایی ماشین-2021
Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a singlesensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades.
Keywords: Keemun black tea | Near-infrared reflectance spectroscopy | Computer vision system | Feature fusion | Convolutional neural network | Quality identification
مقاله انگلیسی
9 Color Image Enhancement based on Gamma Encoding and Histogram Equalization
بهبود تصویر رنگی بر اساس رمزگذاری گاما و یکسان سازی هیستوگرام-2021
Image Enhancement is used as a preprocessing step in many computer vision applications. It provides enhanced input for other computerized image processing methods. Many preprocessing techniques can be applied to images depending on the application domain. In this paper we are proposing an image enhancement technique for color images that can be used as preprocessing step in many computer vision applications. It can also be used as a data augmentation technique in object detection. Luminance component of images is sometimes not captured by cameras and displayed by monitors properly. To remove this drawback of devices we have used gamma encoding. Four different values of gamma are evaluated depending on the quality of images. Image is then converted into YUV Color space. Y component represents the luminance. U and V components represent color. After that Contrast Limited Adaptive Histogram Equalization is applied to the Y component to improve the contrast of the image. The results are compared with the state-of-the-art methods on the basis of Peak Signal to noise Ratio (PSNR) and Mean Square Error (MSE). Quantitative results show that proposed algorithm results in improved value of PSNR and decreased value of MSE as compared to existing methods. Qualitative comparison is also done and results show improvement over the existing techniques.© 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials, Manufacturing and Mechanical Engineering for Sustainable Developments-2020.
Keywords: Histogram | Intensity | Luminance | Contrast stretching
مقاله انگلیسی
10 The biometric recognition system based on near-infrared finger vein image
سیستم تشخیص بیومتریک بر اساس تصویر رگ انگشت نزدیک مادون قرمز-2021
It is a difficult task to extract vein features accurately since the finger-vein images captured by near infrared light are always poor in quality. This paper proposes a novel finger vein feature representation scheme based on pyramid histograms of oriented gradients and local phase quantization. As the vein networks consist of abundant texture and orientation features, a texture feature description operator at various scales is employed on the finger vein image to reduce the effects of geometric deformation occurred image acquisition due to the different posture and position of fingers. To solve the adverse effects of image blurring caused by uneven illumination, local phase quantization is then introduced to extract vein features. Finally, the above mentioned extracted two kinds of texture characteristics of vein image are fused at feature level by concatenated histograms to obtain accurate vein feature named pyramid local phase quantization histogram (PLPQ). In this way, we encode the vein image in- formation not only in frequency domain but also among different orientations and scales. We perform rigorous experiments on two publicly available databases named FV-USM and MMCBUN, and the results of the experiments reveal that proposed fusion system can make promising improvement of finger vein recognition performance.
Keywords: Finger texture | Finger vein recognition | Pyramid histogram of oriented gradients | Local phase quantization
مقاله انگلیسی
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