Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing

Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density of the haze or fog in the scene and very lightweight as the total size of the model is around 21.7 MB. It also provides faster runtime compared to current multi-scale methods with an average runtime of 0.0145s to process 1200x1600 HD quality image. Finally, we show the superiority of this network on Dense Haze Removal to other state-of-the-art models.Comment: CVPR Workshops Proceedings 202

Generative adversarial networks with multi-scale and attention mechanisms for underwater image enhancement

The images captured underwater are usually degraded due to the effects of light absorption and scattering. Degraded underwater images exhibit color distortion, low contrast, and blurred details, which in turn reduce the accuracy of marine biological monitoring and underwater object detection. To address this issue, a generative adversarial network with multi-scale and an attention mechanism is proposed to improve the quality of underwater images. To extract more effective features within the generative network, several modules are introduced: a multi-scale dilated convolution module, a novel attention module, and a residual module. These modules are utilized to design a generative network with a U-shaped structure. The multi-scale dilated convolution module is designed to extract features at multiple scales and expand the receptive field to capture more global information. The attention module directs the network’s focus towards important features, thereby reducing the interference from redundant feature information. To improve the discriminative power of the adversarial network, a multi-scale discriminator is designed. It has two output feature maps with different scales. Additionally, an improved loss function for the generative adversarial network is proposed. This improvement involves incorporating the total variation loss into the traditional loss function. The performance of different methods for enhancing underwater images is evaluated using the EUVP dataset and UIEB dataset. The experimental results demonstrate that the enhanced underwater images exhibit better quality and visual effects compared to other methods

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Measurement model of brass plated tyre steel cord based on wave feature extraction

In the production of Truck and Bus Radial (TBR) vehicle tyres, one of the essential components is the wire that supports the tyre. There are several types of tyre wire, one of which is Brass Plated Tyre Steel Cord (BPTSC), produced by Bekaert Indonesia Company. BPTSC object has a micro-size with a diameter of 0.230 mm and has a wave shape. In checking the quality of steel straps, brass-coated tyres are usually measured manually by experienced experts by measuring instruments to measure the diameter using a micrometre, wave amount, and wavelength using a profile projector. The manual measurement process results in inaccuracy due to fatigue in employees' eyes and low lighting and must be repeated, thus, consuming more time. Technological developments that use computer vision are increasingly widespread. Moreover, from the results of studies in various literature, it is proposed to combine the models obtained to find new models to solve this problem. The objectives of this study were to implement and evaluate an automatic segmentation method for obtaining regions of interest, to propose a BPTSC diameter, wave amount, and wavelength measurement model based on its edge, and to evaluate the proposed model by comparing the results with standard and industrial measurement results. The technique to prepare the brass plated tyre steel cord was done in two ways: image acquisition techniques with enhanced image quality, noise removal, and edge detection. Secondly, ground truth techniques were utilised to find the truth about the stages of the image acquisition process. Finally, sensitivity testing was conducted to find the similarity between the acquired images and the ground truth data using Jaccard, Dice, and Cosine similarity method. From 148 wire samples, the average similarity value was 93% by Jaccard, 96% by Dice, and 91% by the Cosine method. Thus, it can be concluded that the acquisition stage of the brass-coated steel tyre cable with image processing techniques can be carried out. For the subsequent process, the pixel distance and the sliding windows model applied can correctly detect the diameter of the BPTSC properly. The wave amount and wavelength of BPTSC objects in the form of waves were measured using several local minima and maxima approaches. This included maxima of local minima maxima distance, the average of local minima maxima distance, and perpendicular shape to centre distance for measuring wave amounts. While for wavelength measurements, the midpoint of local maxima minima distance and the intersection of local maxima minima with a central line were used. Measurement results were evaluated to determine the accuracy and efficiency of the measurement process compared to standard production values using the accuracy, precision, recall, and Root Mean Square Error (RMSE) test. From the evaluation results of the two methods, the accuracy rate of diameter measurement is 97%, wave rate measurement is 95%, and wavelength measurement is 90%. A new model was formed from the evaluation results that could solve these problems and provide scientific and beneficial contributions to society in general and the companies related to this industry

Real-time Ultrasound Signals Processing: Denoising and Super-resolution

Ultrasound acquisition is widespread in the biomedical field, due to its properties of low cost, portability, and non-invasiveness for the patient. The processing and analysis of US signals, such as images, 2D videos, and volumetric images, allows the physician to monitor the evolution of the patient's disease, and support diagnosis, and treatments (e.g., surgery). US images are affected by speckle noise, generated by the overlap of US waves. Furthermore, low-resolution images are acquired when a high acquisition frequency is applied to accurately characterise the behaviour of anatomical features that quickly change over time. Denoising and super-resolution of US signals are relevant to improve the visual evaluation of the physician and the performance and accuracy of processing methods, such as segmentation and classification. The main requirements for the processing and analysis of US signals are real-time execution, preservation of anatomical features, and reduction of artefacts. In this context, we present a novel framework for the real-time denoising of US 2D images based on deep learning and high-performance computing, which reduces noise while preserving anatomical features in real-time execution. We extend our framework to the denoise of arbitrary US signals, such as 2D videos and 3D images, and we apply denoising algorithms that account for spatio-temporal signal properties into an image-to-image deep learning model. As a building block of this framework, we propose a novel denoising method belonging to the class of low-rank approximations, which learns and predicts the optimal thresholds of the Singular Value Decomposition. While previous denoise work compromises the computational cost and effectiveness of the method, the proposed framework achieves the results of the best denoising algorithms in terms of noise removal, anatomical feature preservation, and geometric and texture properties conservation, in a real-time execution that respects industrial constraints. The framework reduces the artefacts (e.g., blurring) and preserves the spatio-temporal consistency among frames/slices; also, it is general to the denoising algorithm, anatomical district, and noise intensity. Then, we introduce a novel framework for the real-time reconstruction of the non-acquired scan lines through an interpolating method; a deep learning model improves the results of the interpolation to match the target image (i.e., the high-resolution image). We improve the accuracy of the prediction of the reconstructed lines through the design of the network architecture and the loss function. %The design of the deep learning architecture and the loss function allow the network to improve the accuracy of the prediction of the reconstructed lines. In the context of signal approximation, we introduce our kernel-based sampling method for the reconstruction of 2D and 3D signals defined on regular and irregular grids, with an application to US 2D and 3D images. Our method improves previous work in terms of sampling quality, approximation accuracy, and geometry reconstruction with a slightly higher computational cost. For both denoising and super-resolution, we evaluate the compliance with the real-time requirement of US applications in the medical domain and provide a quantitative evaluation of denoising and super-resolution methods on US and synthetic images. Finally, we discuss the role of denoising and super-resolution as pre-processing steps for segmentation and predictive analysis of breast pathologies

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Operational Design Domain Monitoring and Augmentation for Autonomous Driving

Recent technological advances in Autonomous Driving Systems (ADS) show promise in increasing traffic safety. One of the critical challenges in developing ADS with higher levels of driving automation is to derive safety requirements for its components and monitor the system's performance to ensure safe operation. The Operational Design Domain (ODD) for ADS confines ADS safety to the context of its function. The ODD represents the operating environment within which an ADS operates and satisfies the safety requirements. To reach a state of "informed safety", the system's ODD must be explored and well-tested in the development phase. At the same time, the ADS must monitor the operating conditions and corresponding risks in real-time. Existing research and technologies do not directly express the ODD quantitatively, nor do they have a general monitoring strategy designed to handle the learning-based system, which is heavily used in the recent ADS technologies. The safety-critical nature of the ADS requires us to provide thorough validation, continual improvement, and safety monitoring of these data-driven dependent modules. In this dissertation, the ODD extraction, augmentation, and real-time monitoring of the ADS with machine learning components are investigated. There are three major components for the ODD of the ADS with machine learning components for general safety issues. In the first part, we propose a framework to systematically specify and extract the ODD, including the environment modeling and formal and quantitative safety specifications for models with machine learning parts. An empirical demonstration of the ODD extraction process based on predefined specifications is presented with the proposed environment model. In the second part, the ODD augmentation in the development phase is modelled as an iterative engineering problem solved by robust learning to handle unseen future natural variations. The vision tasks in ADS are the major focus here, and the effectiveness of model-based robustness training is demonstrated, which can improve model performance and the application of extracting edge cases during the iterative process. Furthermore, the testing procedure provides us with valuable priors on the probability of failures in the known testing environment, which can be further utilized in the real-time monitoring procedure. Finally, a solution for online ODD monitoring that utilizes the knowledge from the offline validation process as Bayesian graphical models to improve safety warning accuracy is provided. While the algorithms and techniques proposed in this dissertation can be applied to many safety-critical robotic systems with machine learning components, in this dissertation the main focus lies on the implications for autonomous driving

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered