To maintain the serviceability of buildings, the owners need to be informed about the current condition of the water supply and waste disposal systems. Therefore, timely and accurate detection of corrosion on pipe surface is a crucial task. The conventional manual surveying process performed by human inspectors is notoriously time consuming and labor intensive. Hence, this study proposes an image processing-based method for automating the task of pipe corrosion detection. Image texture including statistical measurement of image colors, gray-level co-occurrence matrix, and gray-level run length is employed to extract features of pipe surface. Support vector machine optimized by differential flower pollination is then used to construct a decision boundary that can recognize corroded and intact pipe surfaces. A dataset consisting of 2000 image samples has been collected and utilized to train and test the proposed hybrid model. Experimental results supported by the Wilcoxon signed-rank test confirm that the proposed method is highly suitable for the task of interest with an accuracy rate of 92.81%. Thus, the model proposed in this study can be a promising tool to assist building maintenance agents during the phase of pipe system survey.

/pdf/image-processing-based-detection-of-pipe-corrosion-using-1a9f1ave7z.pdf

Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach.

Development of an early warning fire detection system based on a laser spectroscopic carbon monoxide sensor using a 32-bit system-on-chip

The embodiment of the invention discloses a method and a device for adjusting a shooting parameter, a storage medium and a mobile terminal. The method comprises the steps of obtaining a first previewimage of a to-be-shot object; identifying a shooting scene of the first preview image through a preset scene identification model; when the shooting scene is the preset scene, adjusting the first shooting parameter of a camera based on a second shooting parameter corresponding to the preset scene so as to shoot a to-be-shot object based on the second shooting parameter. The method for adjusting the shooting parameter can improve the scene identification accuracy, can extend the scene identification type, accordingly can solve the problems of low scene identification accuracy and scene type identification restriction in related technologies, and improves the shooting effect.

Method and device for adjusting photographing parameter, storage medium, and mobile terminal

An electronic apparatus and a method by which the electronic apparatus provides recommendation information related to photography are provided. The method includes detecting, by an electronic device, a face of a subject in a preview screen viewed by a camera of the electronic device and displayed on a display of the electronic device, identifying a current composition information of the preview screen based on the detected face of the subject within the preview screen, determining a recommended photographing composition based at least in part on the identified current composition information of the preview screen and a central composition information, and providing a visual composition guide on the display of the electronic device based on the determined recommended photographing composition, the visual composition guide including a current composition indicator and a recommended photographing composition indicator.

Method and system for providing recommendation information related to photography

Systems and method for generating photorealistic images include training a generative adversarial network (GAN) model by jointly learning a first generator, a first discriminator, and a set of predictors through an iterative process of optimizing a minimax objective. The first discriminator learns to determine a synthetic-to-real image from a real image. The first generator learns to generate the synthetic-to-real image from a synthetic image such that the first discriminator determines the synthetic-to-real image is real. The set of predictors learn to predict at least one of a semantic segmentation labeled data and a privileged information from the synthetic-to-real image based on at least one of a known semantic segmentation labeled data and a known privileged information corresponding to the synthetic image. Once trained, the GAN model may generate one or more photorealistic images using the trained GAN model.

Adversarial learning of photorealistic post-processing of simulation with privileged information

Methods, apparatuses and systems may provide for operating a machine learning device by obtaining training image data, conducting an offline prediction analysis of the training image data with respect to one or more real-time parameters of an image capture device, and generating one or more parameter detection models based on the offline prediction analysis. Additionally, methods, apparatuses and systems may provide for operating the image capture device by obtaining a candidate image associated with the image capture device, determining that the candidate image corresponds to a particular type of scene represented in a parameter prediction model, and adjusting one or more real-time parameters of the image capture device based at least in part on one or more parameter values associated with the particular type of scene.

Machine learning of real-time image capture parameters

Key challenge for embedded system companies is management of product configurations over lifecycle. Either new functionality is implemented on an old platform, legacy code on a new one, or both at the same time. We propose Kactus2, a product integration environment suitable for small and mid-size enterprises (SME) utilizing FPGAs. We combine IP-XACT for HW integration and Multicore Association Communications API (MCAPI) for SW integration. The programmer has a uniform view of the system as MCAPI nodes regardless of their implementation in SW or HW. Kactus2 is a work-in-progress open source project implemented in C++ and QT 4.7 currently containing over 40k lines of code and gSOAP as TGI IP-XACT interface.

Kactus2: Environment for Embedded Product Development Using IP-XACT and MCAPI

The majority of the current affordable mobile devices contain a camera with simple optics and a low-cost camera sensor. In these devices, the quality of the captured images is made acceptable with various image processing algorithms that together form an image reconstruction pipeline. The best performance is often achieved with a hardware pipeline, but software implementations can be preferred to minimize production costs and to maximize flexibility. This paper presents a generic software framework for a line-buffer-based image reconstruction pipeline. The presented framework is capable of operating in low-memory environments and significantly eases algorithm insertions, changes of processing order, and other pipeline management tasks. The savings in development time can be even months. In addition, our experiments show that it offers over 99% memory savings compared with traditional implementations using a ping-pong buffer scheme with full-sized image buffers. The implemented framework also enhances processing performance due to better cache usage and increases flexibility with various pipeline configurations.

Generic software framework for a line-buffer-based image processing pipeline

Typical MPSoC FPGA product design is a rigid waterfall process proceeding one-way from HW to SW design. Any changes to HW trigger the SW project re-creation from the beginning. When several product variations or speculative development time exploration is required, the disk bloats easily with hundreds of Board Support Package (BSP), configuration and SW project files. In this paper, we present an IP-XACT based design flow that solves the problems by agile re-use of HW and SW components, automation and single golden reference source for information. We also present new extensions to IP-XACT since the standard lacks SW related features. Three use cases demonstrate how the BSP is changed, an application is moved to another processor and a function is moved from SW implementation to a HW accelerator. Our flow reduces the design time to one third compared to the conventional FPGA flow, the number of automated design phases is doubled and any manual error prone data transfer between HW and SW tools is completely avoided.

http://ieeexplore.ieee.org/iel7/6663741/6675254/06675264.pdf

Extending IP-XACT to embedded system HW/SW integration

IP-XACT, the recent IEEE1685 standard, defines metadata format for IP packing and integration in System-on-Chip designs. It was originally proposed for hardware descriptions, but we have extended it for software, HW/SW mappings and application communication abstraction. The latter is realized with Multicore Association MCAPI that is a lightweight message passing interface. In this paper we present as a work-in-progress how we utilize all these to deploy and move application tasks between different platforms for FPGA prototyping, execution acceleration or verification. The focus is on the metadata format since it is a foundation for automation and tool development. The design flow is illustrated with two case studies: A motion JPEG encoder and a 12-node workload model of video object plane decoder (VOPD). These are deployed to PC and Altera and Xilinx FPGA boards in five variations. The results are reported as the deployment time for both non-recurring and deployment specific tasks. Setting up a new deployment is a matter of hours when there is an IP-XACT library of HW and SW components.

Joni-Matti Maatta

Papers

Machine learning of real-time image capture parameters

Kactus2: Environment for Embedded Product Development Using IP-XACT and MCAPI

Generic software framework for a line-buffer-based image processing pipeline

Extending IP-XACT to embedded system HW/SW integration

System-on-Chip deployment with MCAPI abstraction and IP-XACT metadata