Image-based Detection System Research Articles

Parameter selection and experimental study of the rock particle crushing effect using an image-based method

When maging is used to detect the crushing effect of rock particles, the selected characterization parameters are important factors affecting the results. The image-based detection system is composed of three parts: an image acquisition system, a storage platform, and a digital image processing system.The influence of loading mode and feeding particle size on rock crushing degree and rock morphology characteristics after crushing are analyzed respectively; The crushing ratio and sand-forming ratio of limestone, limestone and granite under shear and extrusion loads are analyzed. The experimental results show that the crushing ratio and sand formation rate play a key role in the crushing of rocks composed of different materials under shear compression loading. The effect analysis of crushing under the feed particle size of 9.5 mm to 16mm shows that there is a great correlation between edges and corners, roundness and overall contour. It provides a basis for the follow-up research on intelligent mine construction and equipment optimization, and is worthy of further popularization and application.

A Survey on a Skin Disease Detection System

Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of 28 common skin diseases. Furthermore, it has discussed the available image databases and the evaluation metrics for the performance analysis of various diagnosis systems. This is vital for figuring out the implementation framework as well as the efficacy of the diagnosis methods for the neophyte. Based on the performance accuracy, the state-of-the-art method for the diagnosis of a particular disease is figured out. It also highlights challenges and shows future research directions.

A Survey on Skin Disease Detection System

Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of 28 common skin diseases. Furthermore, it has discussed the available image databases and the evaluation metrics for the performance analysis of various diagnosis systems. This is vital for figuring out the implementation framework as well as the efficacy of the diagnosis methods for the neophyte. Based on the performance accuracy, the state-of-the-art method for the diagnosis of a particular disease is figured out. It also highlights challenges and shows future research directions.

A Computer Vision Based Detection System for Trash Bins Identification during Trash Classification

In this study, an image based detection system is designed and applied to identify different trash bins for classification. The CCD industrial camera is used to obtain the RGB format images on the trash disposal line in real time. In order to reduce the effect of environmental brightness, the obtained RGB format images are transformed into HSI color format. According to the calculated saturation and hue values, the threshold of saturation and hue values is set for the following image segmentation, respectively. Then, accurate position information of trash bin is obtained for the subsequent classification processing. This detection system could overcome environmental constraints and improve efficiency for trash classification.

A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots.

In order to meet the increasing demands of mobile service robot applications, a dedicated perception module is an essential requirement for the interaction with users in real-world scenarios. In particular, multi sensor fusion and human re-identification are recognized as active research fronts. Through this paper we contribute to the topic and present a modular detection and tracking system that models position and additional properties of persons in the surroundings of a mobile robot. The proposed system introduces a probability-based data association method that besides the position can incorporate face and color-based appearance features in order to realize a re-identification of persons when tracking gets interrupted. The system combines the results of various state-of-the-art image-based detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robot’s user, even in complex, cluttered environments with long-lasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearance-based re-identification are combined. The tracking system was applied in a real world application with a mobile rehabilitation assistant robot in a public hospital. The estimated states of persons are used for the user-centered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments.

Detection of surface cutting defect on magnet using Fourier image reconstruction

A magnet is an important component of a speaker, as it makes the coil move back forth, and it is commonly used in mobile information terminals. Defects may appear on the surface of the magnet while cutting it into smaller slices, and hence, automatic detection of surface cutting defect detection becomes an important task for magnet production. In this work, an image-based detection system for magnet surface defect was constructed, a Fourier image reconstruction based on the magnet surface image processing method was proposed. The Fourier transform was used to get the spectrum image of the magnet image, and the defect was shown as a bright line in it. The Hough transform was used to detect the angle of the bright line, and this line was removed to eliminate the defect from the original gray image; then the inverse Fourier transform was applied to get the background gray image. The defect region was obtained by evaluating the gray-level differences between the original image and the background gray image. Further, the effects of several parameters in this method were studied and the optimized values were obtained. Experiment results show that the proposed method can detect surface cutting defects in a magnet automatically and efficiently.

Detection and monitoring of early caries lesions: a review.

To review the current evidence base of detecting and monitoring early carious lesions in children and adolescents and a rationale proposed to ensure that such lesions are identified and appropriately managed. The systematic literature search identified initially a review by Gomez and co-workers from 2013 and this still represents the current state of the science in relation to caries detection and monitoring. The review described among others, visible detection systems, image-based detection systems and point-measurement approaches. The current evidence base suggests that while there are numerous devices or technology-enabled detection systems, the use of a careful, methodical visual inspection of clean, dry teeth, supplemented where indicated by radiographic views, remains the standard of care in caries detection and diagnostics. Further, it is possible by means of existing visible and radiographical systems to monitor lesions over time. Using low-cost intra-oral cameras facilitates the recording of lesion appearance in the patient record and may be of significant benefit in monitoring early lesions over time following their detection. This benefit extends to the clinician and the patient for whom it may be a useful educational and motivational tool. Recommendations are presented that can be adopted and adapted to local circumstances and that are both substantiated by evidence and promote a clear, simple and consistent approach to caries detection, diagnosis and monitoring in children and adolescents. The diagnoses (initial, active; moderate, active and extensive, active) are linked to appropriate management options within primary care.

Field evaluation of two image-based wildland fire detection systems

Rapid detection of wildfire outbreaks is a critical component of fire management because suppression activities are most effective when fires are small. One method of fire detection and location is computer analysis of images from sensors mounted on towers. In this paper we report on a trial of two image-based detection systems under operational conditions in forests and pasture in south-eastern Australia. The systems were deployed for 3 months in autumn, 2010, during which time a total of 12 experimental fires, 31 planned fires lit by public land management agencies, approximately 250 planned fires lit by private individuals, and 1 unplanned fire were recorded. Both image-based systems were able to detect and locate fires. They performed well for larger planned fires but poorly for small fires ( < 1 ha area) at moderate distances (10–20 km). System performance was compared to a human observer for a subset of fires. For these fires the human observer had a higher detection rate and shorter reporting time than the image-based systems. All methods of detection had a similar level of error for locating fires in the landscape once they had been detected. Operator skill was an important factor in the performance of all systems. Image-based fire detection could be a useful complement to other detection methods already in use in Australia.

Multimedia Data Model for Video-Detection Research

Traffic monitoring and detection systems are important for constructing closed-loop traffic-signal control, freeway-control, and congestion-management systems. Most detection and monitoring systems depend on inductive loops. Recently, many agencies have been investigating video-image–based detection systems because cameras can collect data on several traffic lanes and can be installed quite rapidly on an adjacent pole or bridge. User consensus is that the basic hardware architecture of these systems is well defined. However, there is room for significant improvements in performance and capability of video-detection algorithms. Several researchers are currently working to develop new algorithms. However, due to technology limitations these algorithms have traditionally been evaluated at a macroscopic level by comparing counts obtained by loop detectors with the image-based detection system. This paper discusses the development of a multimedia database-management system for investigating the microscopic performance of video-detection algorithms. The paper describes the process of acquiring the proper data, a definition for a suitable database schema, and the peripheral database clients required to evaluate detection algorithms.