A Distance Measuring Method Using Visual Image Processing

Zhang, Hong; Wang, Lei; Jia, Ruiming; Li, Junwei

doi:10.1109/cisp.2009.5303258

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…In [4] Outlined a visual method for determining distances between two points. In the first step, photographs are taken from a variety of angles.…”

Section: Literature Reviewmentioning

confidence: 99%

Application access for chromatic color detection

Umarani,

Dimpusowjanya,

Poojitha

et al. 2023

J. Phys.: Conf. Ser.

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The process of identifying specific examples of an item inside an image or video is referred to as “object detection,” and it is one of the computer vision techniques. When individuals look at pictures or movies, they are able to recognize and find things that are of interest to them. The primary goal of the field of object recognition is to achieve this level of intelligence via the use of a computer. Image segmentation and blob analysis, which uses simple object properties such as size, shape, or color, and Feature-based object detection, which uses feature extraction, matching, and RANSAC to estimate the location of an object, are two of the many common techniques that are used in conjunction with deep learning and machine learning-based object detection. There are also many other common techniques that are used in conjunction with deep learning and machine learning-based object detection. The most challenging aspects of object detection are the processes of categorizing things and pinpointing their locations. When seen from a different perspective, the same object might seem to be completely different. Detection algorithms have a tough time distinguishing between various things at different scales and perspectives since the sizes and proportions of items almost always alter. The camera input would be used to determine an object’s shape, according to this project’s proposal. Circle, square, and triangle are the three distinct shapes that we are determining an item to be right now. The finished result is shown in MATLAB’s graphical user interface (GUI). The productive identification process is carried out with the assistance of MATLAB. Using the SFTA Algorithm, the application extracts features from the specific image of a video. The neural network algorithm is used to classify the image of video. Image segmentation and image detection are two examples of related computer vision methods that are inextricably intertwined with object recognition. These techniques allow us to better comprehend and analyze the situations shown in movies and still photographs. It has been determined whether or not the suggested system is suitable. Because of this, the testing has shown that the suggested system generates generally accurate indications of real performance at a rate of 99.4%.

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“…In [4] Outlined a visual method for determining distances between two points. In the first step, photographs are taken from a variety of angles.…”

Section: Literature Reviewmentioning

confidence: 99%

Application access for chromatic color detection

Umarani,

Dimpusowjanya,

Poojitha

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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“…Therefore, the technique is applicable to incoherent illumination of objects as well. The principle of the filter relates to the scale and rotation invariance properties of the FourierMellin transform, used as an image processing technique for image recognition and distance measurements [18].…”

Section: Discussionmentioning

confidence: 99%

Spatial filtering velocimetry of objective speckles for measuring out-of-plane motion

2012

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This paper analyzes the dynamics of objective laser speckles as the distance between the object and the observation plane continuously changes. With the purpose of applying optical spatial filtering velocimetry to the speckle dynamics, in order to measure out-of-plane motion in real time, a rotational symmetric spatial filter is designed. The spatial filter converts the speckle dynamics into a photocurrent with a quasi-sinusoidal response to the out-of-plane motion. The spatial filter is here emulated with a CCD camera, and is tested on speckles arising from a real application. The analysis discusses the selectivity of the spatial filter, the nonlinear response between speckle motion and observation distance, and the influence of the distance-dependent speckle size. Experiments with the emulated filters illustrate performance and potential applications of the technology.

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“…The boundaries of the field are considered from the nodes as obstacles. For nodes to measure the relative distances with other nodes or obstacles is possible through the measurement of the round trip time or by infrared [23], ultrasonic [24] or visual techniques [25]. In order to guarantee connectivity, we could also assume that the communication range of the nodes is at least twice their sensing range, as in [26], nonetheless this assumption is not strictly necessary for the coverage algorithm.…”

Section: Background and Assumptionsmentioning

confidence: 99%

Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network

Costanzo

Loscrì

Natalizio

et al. 2012

Computer Communications

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. Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network. Computer Communications, Elsevier, 2012, 35 (9) AbstractThere are many critical issues arising in Wireless Sensor and Robot Networks (WSRN). Based on the specific application, different objectives can be taken into account such as energy consumption, throughput, delay, coverage, etc. Also many schemes have been proposed in order to optimize a specific Quality of Service (QoS) parameter. With the focus on the self-organizing capabilities of nodes in WSRN, we propose a movement-assisted technique for nodes self-deployment. Specifically, we propose to use a neural network as a controller for nodes mobility and a genetic algorithm for the training of the neural network through reinforcement learning [27]. This kind of scheme is extremely adaptive, since it can be easily modified in order to consider different objective and/or QoS parameters. In fact, it is sufficient to consider a different input for the neural network to aim to a different objective. All things considered, we propose a new method for programming a WSRN and we will show practically how the technique works, when the coverage of the network is the QoS parameter to optimize. Simulation results show the flexibility and effectiveness of this approach even when the application scenario changes (e.g., by introducing physical obstacles).

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A Distance Measuring Method Using Visual Image Processing

Cited by 7 publications

References 13 publications

Application access for chromatic color detection

Application access for chromatic color detection

Spatial filtering velocimetry of objective speckles for measuring out-of-plane motion

Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network

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