/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately-designed training process. In contrast, our JL-DCF learns from both RGB and depth inputs through a Siamese network. To this end, we propose two effective components: joint learning (JL), and densely-cooperative fusion (DCF). The JL module provides robust saliency feature learning, while the latter is introduced for complementary feature discovery. Comprehensive experiments on four popular metrics show that the designed framework yields a robust RGB-D saliency detector with good generalization. As a result, JL-DCF significantly advances the top-1 D3Net model by an average of ~1.9% (S-measure) across six challenging datasets, showing that the proposed framework offers a potential solution for real-world applications and could provide more insight into the cross-modality complementarity task. The code will be available at https://github.com/kerenfu/JLDCF/.

/pdf/jl-dcf-joint-learning-and-densely-cooperative-fusion-2fda0x3jqn.pdf

JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection

This article describes research and development on car noise and vibration by the German car manufacturer Opel. Opel now has one of the world's largest anechoic chambers, 22m long by 16m wide. It can measure 'pass-by' noise in any weather, and has a powertrain test rig, which allows the whole powertrain and exhaust system to be examined away from the bodywork. By making vibrations visible, holography is helping to reduce the development time of a new car model by up to two months; it uses the interference principle to analyse structures without actual contact. Using holographic patterns, acoustic experts can work with design engineers on modifications to eliminate vibration. Psychoacoustics is perhaps the most exciting area of research. It applies the theory that sound can counter behaviour as well as influence it. If a driver is being aggressive or driving too fast, sound inside the car can be used to alter his driving style. By 'learning' a driver's personality, a car could take appropriate action to ensure safety and comfort by producing pleasant sounds in the car. However, these methods are still in the experimental stage. Opel is also conducting research on the stressful effects of noise and vibrations in cars.

The science of sound

Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately designed training process. Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture. In this paper, we propose two effective components: joint learning (JL), and densely cooperative fusion (DCF). The JL module provides robust saliency feature learning by exploiting cross-modal commonality via a Siamese network, while the DCF module is introduced for complementary feature discovery. Comprehensive experiments using five popular metrics show that the designed framework yields a robust RGB-D saliency detector with good generalization. As a result, JL-DCF significantly advances the state-of-the-art models by an average of ~2.0% (F-measure) across seven challenging datasets. In addition, we show that JL-DCF is readily applicable to other related multi-modal detection tasks, including RGB-T (thermal infrared) SOD and video SOD (VSOD), achieving comparable or even better performance against state-of-the-art methods. This further confirms that the proposed framework could offer a potential solution for various applications and provide more insight into the cross-modal complementarity task. The code will be available at this https URL

Siamese Network for RGB-D Salient Object Detection and Beyond.

Fractures of bone are a common affliction in orthopedic wards at any given time. Trained radiologists generally identify abnormal pathologies including fractures with a relatively high level of accuracy. However studies examining reader accuracy have shown that in some cases the miss rate can be as high when reading x-rays containing multiple abnormalities. Accurate diagnosis of fractures is vital to the effective management of patient injuries. As a result, detection of long-bone fractures is an important orthopedics and radiologic problem, and it is proposed that a novel computer aided detection system could help lowering the miss rate. This paper examines the development of such a system, for the detection of long-bone fractures. This project fully employed MATLAB 7.8.0 (.r2009a) as the programming tool for loading image, image processing and user interface development. Results obtained demonstrate the performance of the femur's long bone fracture detection system with some limitations.

/pdf/gray-level-co-occurrence-matrix-bone-fracture-detection-28nkakqwyn.pdf

Gray-level co-occurrence matrix bone fracture detection

Noises degrade image quality which causes information losing and unsatisfying visual effects. Salt and Pepper noise is one of the most popular noises that affect image quality. In RGB color image Salt and pepper noise changes the number of occurrences of colors combination depending on the noise ratio. Many methods have been proposed to eliminate Salt and Pepper noise from color image with minimum loss of information. In this paper we will investigate the effects of adding salt and pepper noise to RGB color image, the experimental noise ratio will be calculated and the color combination with maximum and minimum numbers of occurrence will be calculated and detected in RGB color image. In addition this paper proposed a methodology of salt and pepper noise elimination for color images using median filter providing the reconstruction of an image in order to accept result with minimum loss of information. The proposed methodology is to be implemented, tested and experimental results will be analyzed using the calculated values of RMSE and PSNR.

/pdf/salt-and-pepper-noise-effects-and-removal-3e7tv5ivir.pdf

Salt and Pepper Noise: Effects and Removal

A digital color images are the most important types of data currently being traded; they are used in many vital and important applications. Hence, the need for a small data representation of the image is an important issue. This paper will focus on analyzing different methods used to extract texture features for a color image. These features can be used as a primary key to identify and recognize the image. The proposed discrete wave equation DWE method of generating color image key will be presented, implemented and tested. This method showed that the percentage of reduction in the key size is 85% compared with other methods.

Analysis of color image features extraction using texture methods

We introduce a simple and efficient method for coloring the gray-scale images into the color images with the existing color map or with creating a new one. The general problem of adding chromatic values to a gray-scale image has no exact solution, the current approach attempts to provide a method to minimize the amount of human labor required for this task, rather than choosing RGB colors to individual components, we convert the entire gray-scale image to color by creating a color map then the color map is to be used to color the gray scale image. We show that this simple method can be successfully applied to a variety of gray scale images and it can be applicable in deferent applications.

https://www.ijcaonline.org/archives/volume153/number2/azzeh-2016-ijca-911975.pdf

Creating a Color Map to be used to Convert a Gray Image to Color Image

The design of an effective and robust data gathering algorithm is crucial to the overall performance of wireless sensor networks (WSN). However, using traditional routing algorithms for data gathering is energy-inefficient for sensor nodes with limited power resources and multi-hop communication protocols. Data gathering with mobile sinks provided an effective solution to this problem. The major drawback of this approach is the time and path constraints of the mobile sink, which limit the mobile sink to collect data from all sensor nodes and, then, data routing is still required for these unreachable parts by the mobile sink. This paper presents a new data gathering algorithm called Connectivity-Based Data Collection (CBDC). The CBDC algorithm utilizes the connectivity between sensor nodes so as to determine the trajectory of the mobile sink whilst satisfying its path constraint and minimizing the number of multi-hop communications. The presented results show that CBDC, in comparison with the LEACH-C algorithm, prolongs the network life time at different connectivity levels of sensor networks, varying number of sensor nodes and at different path constraints of the mobile sink.

/pdf/connectivity-based-data-gathering-with-path-constrained-26agsk0a0r.pdf

Connectivity-Based Data Gathering with Path-Constrained Mobile Sink in Wireless Sensor Networks

Color image was defined as a 3 dimensional array and consisted of 3 two dimensional arrays, one for the red component, the second for the blue component and the third for green component. So it was needed to separate red, blue and green components in order to get the gray equivalent of the color image. The gray image then will be processed using one or more image processing techniques, after that the processed color image will be converted back to enhanced color image. Converting color image to gray and converting the processed gray image back to color image were the tasks that needed a lot of calculation and so a lot of processing time. Decreasing this time to the minimum is a focal task in order to achieve an optimized processing. HSI model of conversion was studied and a new R'G'I model also was studied and the experimental results shows that using R'G'I model speed up the conversion and thus minimize the time at least 8 times, thus R'G'I model can replace the HSI model in order to achieve efficient manipulation of true color image.

/pdf/optimized-true-color-image-processing-xyfiyx5qiq.pdf

Ziad Alqadi

Papers

Salt and Pepper Noise: Effects and Removal

Analysis of color image features extraction using texture methods

Creating a Color Map to be used to Convert a Gray Image to Color Image

Connectivity-Based Data Gathering with Path-Constrained Mobile Sink in Wireless Sensor Networks

Optimized True-Color Image Processing