Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods.

Continuous frames for unbounded operators

Few years ago Găvruţa gave the notions of K-frame and atomic system for a linear bounded operator K in a Hilbert space $$\mathcal {H}$$ H in order to decompose $$\mathcal {R}(K)$$ R ( K ) , the range of K, with a frame-like expansion. These notions are here generalized to the case of a densely defined and possibly unbounded operator A on a Hilbert space in a continuous setting, thus extending what have been done in a previous paper in a discrete framework.

Randomized Signal Processing with Continuous Frames

This paper focuses on signal processing tasks in which the signal is transformed from the signal space to a higher dimensional coefficient space (also called phase space) using a continuous frame, processed in the coefficient space, and synthesized to an output signal. We show how to approximate such methods, termed phase space signal processing methods, using a Monte Carlo method. As opposed to standard discretizations of continuous frames, based on sampling discrete frames from the continuous system, the proposed Monte Carlo method is directly a quadrature approximation of the continuous frame. We show that the Monte Carlo method allows working with highly redundant continuous frames, since the number of samples required for a certain accuracy is proportional to the dimension of the signal space, and not to the dimension of the phase space. Moreover, even though the continuous frame is highly redundant, the Monte Carlo samples are spread uniformly, and hence represent the coefficient space more faithfully than standard frame discretizations.

Survey Analysis of Robust and Real-Time Multi-Lane and Single Lane Detection in Indian Highway Scenarios

Studies on lane detection Lane identification methods, integration, and evaluation strategies square measure all examined. The system integration approaches for building a lot of strong detection systems are then evaluated and analyzed, taking into account the inherent limits of camera-based lane detecting systems. Present deep learning approaches to lane detection are inherently CNN's semantic segmentation network the results of the segmentation of the roadways and the segmentation of the lane markers are fused using a fusion method. By manipulating a huge number of frames from a continuous driving environment, we examine lane detection, and we propose a hybrid deep architecture that combines the convolution neural network (CNN) and the continuous neural network (CNN) (RNN). Because of the extensive information background and the high cost of camera equipment, a substantial number of existing results concentrate on vision-based lane recognition systems. Extensive tests on two large-scale datasets show that the planned technique outperforms rivals' lane detection strategies, particularly in challenging settings. A CNN block in particular isolates information from each frame before sending the CNN choices of several continuous frames with time-series qualities to the RNN block for feature learning and lane prediction.

The Hardy Cross method and its implementation in Spain

In May 1930, Hardy Cross (1885-1959) published an article called ‘Analysis of continuous frames by distributing fixed-end moments’ in the American Society of Civil Engineers (ASCE). This article proposed a new approach to Structural Theory, and its relevance could be compared to that of the Three Moments Theorem (also known as the Clapeyron Theorem). The Cross method, as this calculation methodology has been often called, had remarkable significance from the moment it came out until the 70s, when new calculation methods became popular. In the present article, we will be trying to evaluate its impact in locations far from its origins; in particular, how it was understood and formulated in Spain. As will be demonstrated, the importance of this method was extremely relevant for theconstruction of new buildings and the implementation of new industries, which started to appear in a decisive moment for the development of the country. Even though the Hardy Cross method was the most widely used methodology at the time, two other procedures were also available; namely, the Kani and the Takabeya methods, methods that would also appear in the technical bibliography of the time. Despite the infrequent implementation of these other methods, we have briefly referred to both of them in the present paper. This article aims to show the relevance of the Cross method as well as its early implementation in Spain, by using academic bibliography of that time.

Accident Detection and Number Plate Recognition using Image Processing and Machine Learning

The purpose of this project is to detect the accident before it happens along with theextraction the number plate. Different image processing techniques along with morphological operators and Canny Edge Detection are used for image enhancements and object outline detections. With analysis of continuous frames, the relative velocity and the distance from which the leading vehicles are moving could be computed which is further helpful in accident detection and thus prevention too. Histogram of Oriented Gradients (HOG features) are used for feature extraction. Different machine learning classification algorithms like SVM, MLP, and XGBoost are used for classification of the object. Different standard OCR tools like Pytesseract, PyOCR, TesserOCR are used for the retrieval of the vehicle number from the extracted licence plate sub-image.

Safety Issues in the Seismic Design of Secondary Frameless Glass Structures

Glass is largely used in buildings, in the form of an innovative and versatile material. Both for novel and existing constructions, secondary glass systems are frequently realized to interact with primary components of different materials. In most cases, the structural challenge deriving from the intrinsic brittleness and vulnerability of glass is efficiently controlled via laminated (LG) multi-layer sections. However, further potential risks for people should be properly minimized, like for example, in the presence of extreme loads. This is the case of seismic regions, where dedicated calculation methods are required to accommodate displacement and resistance demands, but design specifications are rarely provided by existing standards for earthquake resistant buildings. Even more attention is needed for frameless glass systems in which the bracing members (i.e., continuous frames, cable-nets, etc.,) are reduced to a minimum, in favour of metal point connections (i.e., bolts and mechanical fixings, friction clamps, etc.). This paper aims at discussing the current design requirements for the seismic performance assessment of these relatively simple but challenging structural solutions, with careful consideration for the Italian scenario, where a practical support for design can be found in the CNR-DT 210/2013 technical document. Based on a case-study system, major issues, open questions and uncertainties or critical aspects for the seismic analysis and design of secondary frameless glass assemblies are thus emphasized.