Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images

In this paper, a novel method for generation of multiple description (MD) wavelet based image coding is proposed by using Multi-Objective Evolutionary Algorithms (MOEAs). Complexity of the multimedia transmission problem has been increased for MD coders if an input image is affected by any type of noise. In this case, it is necessary to solve two different problems which are designing the optimal side quantizers and estimating optimal parameters of the denoising filter. Existing MD coding (MDC) generation methods are capable of solving only one problem which is to design side quantizers from the given noise-free image but they can fail reducing any type of noise on the descriptions if they applied to the given noisy image and this will cause bad quality of multimedia transmission in networks. Proposed method is used to overcome these difficulties to provide effective multimedia transmission in lossy networks. To achieve it, Dual Tree-Complex Wavelet Transform (DT-CWT) is first applied to the noisy image to obtain the subbands or set of coefficients which are used as a search space in the optimization problem. After that, two different objective functions are simultaneously employed in the MOEA to find pareto optimal solutions with the minimum costs by evolving the initial individuals through generations. Thus, optimal quantizers are created for MDCs generation and obtained optimum parameters are used in the image filter to remove the mixed Gaussian impulse noise on the descriptions effectively. The results demonstrate that proposed method is robust to the mixed Gaussian impulse noise, and offers a significant improvement of optimal side quantizers for balanced MDCs generation at different bitrates. © 2018 Elsevier B.V

Change Detection in Multispectral Landsat Images Using Multiobjective Evolutionary Algorithm

In this letter, we propose a novel method for unsupervised change detection in multitemporal multispectral Landsat images using multiobjective evolutionary algorithm (MOEA). The proposed method minimizes two different objective functions using MOEA to provide tradeoff between each other. The objective functions are used for evaluating changed and unchanged regions of the difference image separately. The difference image is obtained by using the structural similarity index measure method, which provides combination of the comparisons of luminance, contrast, and structure between two images. By evolving a population of solutions in the MOEA, a set of Pareto optimal solution is estimated in a single run. To find the best solution, a Markov random field fusion approach is used. Experiments on semisynthetic and real-world data sets show the efficiency and effectiveness of the proposed method

Object recognition using shape growth pattern

This paper proposes a preprocessing stage to augment the bank of features that one can retrieve from binary images to help increase the accuracy of pattern recognition algorithms. To this end, by applying successive dilations to a given shape, we can capture a new dimension of its vital characteristics which we term hereafter: the shape growth pattern (SGP). This work investigates the feasibility of such a notion and also builds upon our prior work on structure preserving dilation using Delaunay triangulation. Experiments on two public data sets are conducted, including comparisons to existing algorithms. We deployed two renowned machine learning methods into the classification process (i.e., convolutional neural network-CNN- and random forests-RF-) since they perform well in pattern recognition tasks. The results show a clear improvement of the proposed approach's classification accuracy (especially for data sets with limited training samples) as well as robustness against noise when compared to existing methods

Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering

In this paper, a new approach is proposed to enhance the handwriting image by using learning-based windowing contrast enhancement and Gaussian Mixture Model (GMM). A fixed size window moves over the handwriting image and two quantitative methods which are discrete entropy (DE) and edge-based contrast measure (EBCM) are used to estimate the quality of each patch. The obtained results are used in the unsupervised learning method by using k-means clustering to assign the quality of handwriting as bad (if it is low contrast) or good (if it is high contrast). After that, if the corresponding patch is estimated as low contrast, a contrast enhancement method is applied to the window to enhance the handwriting. GMM is used as a final step to smoothly exchange information between original and enhanced images to discard the artifacts to represent the final image. The proposed method has been compared with the other contrast enhancement methods for different datasets which are Swedish historical documents, DIBCO2010, DIBCO2012 and DIBCO2013. Results illustrate that proposed method performs well to enhance the handwriting comparing to the existing contrast enhancement methods. © 2016 IEEE

A new feature selection scheme for emotion recognition from text

This paper presents a new scheme for term selection in the field of emotion recognition from text. The proposed framework is based on utilizing moderately frequent terms during term selection. More specifically, all terms are evaluated by considering their relevance scores, based on the idea that moderately frequent terms may carry valuable information for discrimination as well. The proposed feature selection scheme performs better than conventional filter-based feature selection measures Chi-Square and Gini-Text in numerous cases. The bag-of-words approach is used to construct the vectors for document representation where each selected term is assigned the weight 1 if it exists or assigned the weight 0 if it does not exist in the document. The proposed scheme includes the terms that are not selected by Chi-Square and Gini-Text. Experiments conducted on a benchmark dataset show that moderately frequent terms boost the representation power of the term subsets as noticeable improvements are observed in terms of Accuracies. © 2020 by the authors.Open access</p

Outdoor Performance Assessment of New and Old Photovoltaic Panel Technologies Using a Designed Multi-Photovoltaic Panel Power Measurement System

This paper presents a new multi-photovoltaic panel measurement and analysis system (PPMAS) developed for measurement of atmospheric parameters and generated power of photovoltaic (PV) panels. Designed system presented with an experimental study evaluates performance of four new and four 5-year-old PV panel technologies which are based on polycrystalline (Poly), monocrystalline (Mono), copper indium selenide (CIS), and cadmium telluride (CdTe) in real time, under same atmospheric conditions. The PPMAS system with the PV panels is installed in Yildirim Beyazit University, Ankara Province, in Turkey. The designed PPMAS consists of three different subsystems which are (1) photovoltaic panel measurement subsystem (PPMS), (2) meteorology measurement subsystem (MMS), and (3) data acquisition subsystem (DAS). PPMS is used to measure the power generation for PV panels. MMS involves different types of sensors, and it is designed to determine atmospheric conditions including wind speed, wind direction, outdoor temperature, humidity, ambient light, and panel temperatures. The measured values by PPMS and MMS are stored in a database using DAS subsystem. In order to improve the measurement accuracy, PPMS and MMS are calibrated. This study also focuses on outdoor testing performances of four new and four 5-year-old PV panels. Average monthly panel efficiencies are estimated as 8.46%, 8.11%, 5.65%, and 3.88% for new Mono, new Poly, new CIS, and new CdTe PV panels, respectively. Moreover, average monthly panel efficiencies of old panels are calculated as 8.22%, 7.85%, 5.35%, and 3.63% in the same order. Test results obtained from the experimental system are also statistically examined and discussed to analyze the performance of PV panels in terms of monthly panel efficiencies. © 2020 Mehmet Karabulut et al.open access</p

Forecasting Sales of Truck Components : A Machine Learning Approach

In this paper, forecasting sales model for truck components using machine learning algorithms is proposed. The forecasting model helps companions (i.e. Volvo Trucks) in the activity of trade and business. It also plays a major role for firms in decision-making operations in the areas corresponding to sales, production, purchasing, finance, and accounting. In order to achieve good forecasting sales mode, firstly, a normalization approach is performed on the time-series data to reduce and eliminate the data redundancy. After that, feature extraction and selection techniques are employed on the normalized data. Finally, different machine learning methods such as Support Vector Machine Regression, Ridge Regression, Gradient Boosting Regression and Random Forest Regression have been applied to the features of the normalized time-series data. Results depict that ridge regression method gives the most promising forecasting sale results of truck components compared to the other machine learning methods. © 2020 IEEE.open access</p

Comparison of Keypoint Detectors and Descriptors for Relative Radiometric Normalization of Bitemporal Remote Sensing Images

This paper compares the performances of the most commonly used keypoint detectors and descriptors (SIFT, SURF, KAZE, AKAZE, ORB, and BRISK) for Relative Radiometric Normalization (RRN) of unregistered bitemporal multi-spectral images. The keypoints matched between subject and reference images represent possible unchanged regions and are used in forming a Radiometric Control Set (RCS). The initial RCS is further refined by removing the matched keypoints with a low cross-correlation. The final RCS is used to approximate a linear mapping between the corresponding bands of the subject and reference images. This procedure is validated on five datasets of unregistered multi-spectral image pairs acquired by inter/intra sensors in terms of RRN accuracy, visual quality, quality and quantity of the samples in the RCS, and computing time. The experimental results show that keypoint-based RRN is robust against variations in spatial-resolution, illumination, and sensors. The blob detectors (SURF, SIFT, KAZE, and AKAZE) are more accurate on average than the corner detectors (ORB and BRISK) in RRN. However, they are slower in computing. The source code and datasets used in experiments are available at https://github.com/ArminMoghimi/keypoint-based-RRN to support reproducible research in remote sensing. CCBYopen access</p

FastUAV-NET : A multi-UAV detection algorithm for embedded platforms

In this paper, a real-time deep learning-based framework for detecting and tracking Unmanned Aerial Vehicles (UAVs) in video streams captured by a fixed-wing UAV is proposed. The proposed framework consists of two steps, namely intra-frame multi-UAV detection and the inter-frame multi-UAV tracking. In the detection step, a new multi-scale UAV detection Convolutional Neural Network (CNN) architecture based on a shallow version of You Only Look Once version 3 (YOLOv3-tiny) widened by Inception blocks is designed to extract local and global features from input video streams. Here, the widened multi-UAV detection network architecture is termed as FastUAV-NET and aims to improve UAV detection accuracy while preserving computing time of one-step deep detection algorithms in the context of UAV-UAV tracking. To detect UAVs, the FastUAV-NET architecture uses five inception units and adopts a feature pyramid network to detect UAVs. To obtain a high frame rate, the proposed method is applied to every nth frame and then the detected UAVs are tracked in intermediate frames using scalable Kernel Correlation Filter algorithm. The results on the generated UAV-UAV dataset illustrate that the proposed framework obtains 0.7916 average precision with 29 FPS performance on Jetson-TX2. The results imply that the widening of CNN network is a much more effective way than increasing the depth of CNN and leading to a good trade-off between accurate detection and real-time performance. The FastUAV-NET model will be publicly available to the research community to further advance multi-UAV-UAV detection algorithms. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.open access</p

ARDIS : A Swedish Historical Handwritten Digit Dataset

This paper introduces a new image-based handwrittenhistorical digit dataset named ARDIS (Arkiv DigitalSweden). The images in ARDIS dataset are extractedfrom 15,000 Swedish church records which were writtenby different priests with various handwriting styles in thenineteenth and twentieth centuries. The constructed datasetconsists of three single digit datasets and one digit stringsdataset. The digit strings dataset includes 10,000 samplesin Red-Green-Blue (RGB) color space, whereas, the otherdatasets contain 7,600 single digit images in different colorspaces. An extensive analysis of machine learning methodson several digit datasets is examined. Additionally, correlationbetween ARDIS and existing digit datasets ModifiedNational Institute of Standards and Technology (MNIST)and United States Postal Service (USPS) is investigated. Experimental results show that machine learning algorithms,including deep learning methods, provide low recognitionaccuracy as they face difficulties when trained on existingdatasets and tested on ARDIS dataset. Accordingly, ConvolutionalNeural Network (CNN) trained on MNIST andUSPS and tested on ARDIS provide the highest accuracies 58.80% and 35.44%, respectively. Consequently, the resultsreveal that machine learning methods trained on existingdatasets can have difficulties to recognize digits effectivelyon our dataset which proves that ARDIS dataset hasunique characteristics. This dataset is publicly available forthe research community to further advance handwritten digitrecognition algorithms.open access</p