Histogram of Oriented Phase (HOP): A New Descriptor Based on Phase Congruency

In this paper we present a low level image descriptor called Histogram of Oriented Phase based on phase congruency concept and the Principal Component Analysis (PCA). Since the phase of the signal conveys more information regarding signal structure than the magnitude, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the image with respect to its neighborhood. Histograms of the phase congruency values of the local regions in the image are computed with respect to its orientation. These histograms are concatenated to construct the Histogram of Oriented Phase (HOP) features. The dimensionality of HOP features is reduced using PCA algorithm to form HOP-PCA descriptor. The dimensionless quantity of the phase congruency leads the HOP-PCA descriptor to be more robust to the image scale variations as well as contrast and illumination changes. Several experiments were performed using INRIA and DaimlerChrysler datasets to evaluate the performance of the HOP-PCA descriptor. The experimental results show that the proposed descriptor has better detection performance and less error rates than a set of the state of the art feature extraction methodologies

Histogram of Oriented Phase and Gradient (HOPG) Descriptor for Improved Pedestrian Detection

This paper presents a new pedestrian detection descriptor named Histogram of Oriented Phase and Gradient (HOPG) based on a combination of the Histogram of Oriented Phase (HOP) features and the Histogram of Oriented Gradient features (HOG). The proposed descriptor extracts the image information using both the gradient and phase congruency concepts. Although the HOG based method has been widely used in the human detection systems, it lacks to deal effectively with the images impacted by the illumination variations and cluttered background. By fusing HOP and HOG features, more structural information can be identified and localized in order to obtain more robust and less sensitive descriptors to lighting variations. The phase congruency information and the gradient of each pixel in the image are extracted with respect to its neighborhood. Histograms of the phase congruency and the gradients of the local segments in the image are computed with respect to its orientations. These histograms are concatenated to construct the HOPG descriptor. The performance evaluation of the proposed descriptor was performed using INRIA and DaimlerChrysler datasets. A linear support vector machine (SVM) classifier is used to train the pedestrians. The experimental results show that the human detection system based on the proposed features has less error rates and better detection performance over a set of state of the art feature extraction methodologies

DC Microgrid based on Battery, Photovoltaic, and fuel Cells; Design and Control

Microgrids offer flexibility in power generation in a way of using multiple renewable energy sources. In the past few years, microgrids become a very active research area in terms of design and control strategies. Most of the microgrids use DC/DC converters to connect renewable energy sources to the load. In this paper, the simulation model of a DC microgrid with three different energy sources (Lithium-ion battery (LIB), photovoltaic (PV) array, and fuel cell) and external variant power load is built with MATLAB/Simulink and the simulative results show that the stability of DC microgrid can be guaranteed by the proposed maximum power point controller MPPT. The three energy sources are connected to the load through DC/DC converters, one for each. This type of topology ensures protection for each energy source as well as optimum stability at the load

Multi-Hypothesis Approach for Efficient Human Detection in Complex Environment

Over the last decade, detection of human beings become one of the most significant tasks in computer vision due to its extended applications that include human computer interaction, visual surveillance, person identification, event detection, gender classification, robotics, automatic navigation, and safety systems. However, this task is rather challenging because of the fluctuating appearance of the human body as well as the cluttered scenes, pose, occlusion, and illumination variations. For such a difficult task, most of the time no single-feature algorithm is rich enough to capture all the relevant information available in the image. To improve the detection accuracy, we propose a multi hypothesis approach containing various aspects of human visual perception. We explore the effectiveness of spatial domain behavior, phase domain behavior, and neighborhood dependency of an image for describing the object region. These cues will lead to the description of the shape and texture of specific objects. Shape features are extracted based on both the gradient concept and the phase congruency in LUV color space. The Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The fusing of this complementary information yields to capture a broad range of the human appearance details that improve detection accuracy. The proposed features are formed by computing the phase congruency of the three-color channels in addition to the gradient magnitude and CSLBP value for each pixel in the image with respect to its neighborhood. Only the maximum phase congruency values are selected from the corresponding color channels. The histogram of oriented phase and gradients, as well as the histogram of CSLBP values for the local regions of the image, are determined. These histograms are concatenated to construct the proposed descriptor, which fuses the shape and texture features, and it is called the Chromatic domain Phase features with Gradient and Texture (CPGT). Human detection system based on the proposed descriptor (CPGT) is robust against illumination changes and is able to depict and detect the human objects in various scales, viewpoints, and postures as well as detection under partial occlusion and realistic environments. Several experiments were conducted to test and evaluate the detection performance of the proposed descriptor. The challenging and the well-known INRIA, DaimlerChrysler, and NICTA datasets are used in these experiments. A Support Vector Machine (SVM) classifier is used in these experiments to classify the CPGT features. The results show that the proposed algorithm has better detection performance in comparison with the state of art feature extraction methodologies

Multi-Hypothesis Approach for Efficient Human Detection in Complex Environment

Over the last decade, detection of human beings become one of the most significant tasks in computer vision due to its extended applications that include human computer interaction, visual surveillance, person identification, event detection, gender classification, robotics, automatic navigation, and safety systems. However, this task is rather challenging because of the fluctuating appearance of the human body as well as the cluttered scenes, pose, occlusion, and illumination variations. For such a difficult task, most of the time no single-feature algorithm is rich enough to capture all the relevant information available in the image. To improve the detection accuracy, we propose a multi hypothesis approach containing various aspects of human visual perception. We explore the effectiveness of spatial domain behavior, phase domain behavior, and neighborhood dependency of an image for describing the object region. These cues will lead to the description of the shape and texture of specific objects. Shape features are extracted based on both the gradient concept and the phase congruency in LUV color space. The Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The fusing of this complementary information yields to capture a broad range of the human appearance details that improve detection accuracy. The proposed features are formed by computing the phase congruency of the three-color channels in addition to the gradient magnitude and CSLBP value for each pixel in the image with respect to its neighborhood. Only the maximum phase congruency values are selected from the corresponding color channels. The histogram of oriented phase and gradients, as well as the histogram of CSLBP values for the local regions of the image, are determined. These histograms are concatenated to construct the proposed descriptor, which fuses the shape and texture features, and it is called the Chromatic domain Phase features with Gradient and Texture (CPGT). Human detection system based on the proposed descriptor (CPGT) is robust against illumination changes and is able to depict and detect the human objects in various scales, viewpoints, and postures as well as detection under partial occlusion and realistic environments. Several experiments were conducted to test and evaluate the detection performance of the proposed descriptor. The challenging and the well-known INRIA, DaimlerChrysler, and NICTA datasets are used in these experiments. A Support Vector Machine (SVM) classifier is used in these experiments to classify the CPGT features. The results show that the proposed algorithm has better detection performance in comparison with the state of art feature extraction methodologies

Ensemble lung segmentation system using deep neural networks

Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as ResNet18, ResNet50, Mo-bilenetv2, Xception, and inceptionresnetv2. We improved the encoder module of DeepLabV3+ by adjusting the receptive field of the Spatial Pyramid Pooling (ASPP). We also studied our algorithm\u27s performance on a publicly available dataset provided by Shenzhen Hospital, that contains 566 CRs with manually segmented lungs (ground truth). The experimental result demonstrate the effectiveness of the proposed model on the dataset, achieving an Intersection-Over-Union (IoU, Jaccard Index) score of 0.97 on the test set

Hybrid GRU-LSTM Recurrent Neural Network-Based Model for Real Estate Price Prediction

Real estate prices are an important reflection of the economy and their prices are great interest to both buyers and sellers. Hundreds of houses are sold every day and the buyer asks himself what is the reasonable price that this house deserves. In this paper, a new regression model is proposed for the accurate prediction of house prices. This model is based on the hybrid recurrent neural network where the Gated Recurrent Unit (GRU) is fused with the Long Short-Term Memory (LSTM) and applied to a particular dataset that characterizes houses in Boston. Massachusetts dataset from Scikit-learn is used in this research to train and evaluate this regression model using the data on Boston housing. Several experiments were conducted on the proposed algorithm and evaluated with the commonly used metrics. The results of these experiments showed that the proposed model has better performance when the networks are used in the fusion process than when they act individually. It also has better accuracy and lower Root Mean Square Error when compared to several states of art methodologies.</p