Research on intelligent visual image feature region acquisition algorithm in Internet of Things framework

2020 ◽  
Vol 151 ◽  
pp. 299-305
Author(s):  
Xin Liu
Keyword(s):  
Internet Of Things ◽  
Visual Image ◽  
Image Feature

scholarly journals Infrared with Visual Image Fusion Based on Two-Scale Decomposition and Sturdy Guided Filtering Technique

2019 ◽  
Vol 8 (12) ◽  
pp. 5353-5359
Keyword(s):  
Image Fusion ◽  
Visual Image ◽  
Image Feature ◽  
Preconditioned Conjugate Gradient ◽  
Phase Congruency ◽  
Efficient Manner ◽  
Fusion Technique ◽  
High Convergence Rate ◽  
Guided Filtering ◽  
Image Fusion Technique

Several Infrared (IR) and Visual (VIS) image fusion techniques have been widely used to acquire a novel image which may characterize the image accurately, completely and reliably. This process can serve an essential part in image processing applications. In this article, an enhanced IR and VIS image fusion technique is proposed by Two-Scale Decomposition (TSD) and Sturdy Guided Filtering (SGF) together to further increase the robustness of fusion process. Initially, IR and VIS images are decomposed for creating the base and detail layers. Then, Phase Congruency (PC) and Sum Modified Laplacian(SML) are applied to get saliency maps of base and detail layers, respectively. Also, Iteratively Reweighted Least Squares (IRLS) algorithm with GF, namely SGF is included instead of GF method in an efficient manner to properly smooth the weighting maps by preserving the depth edges that correspond to weak color edges and small structures. In this SGF technique, Enhanced Preconditioned Conjugate Gradient (EPCG) method is applied to optimize the RLS iteratively and select the conjugate paths for each iteration efficiently. This SGF can achieve high convergence rate and handle the structure inconsistency while properly preserving the edges. Experimental outcomes exhibit that the proposed TSD-PS-SGF based image fusion technique has higher performance over state-of-the-art techniques in terms of image feature-based, information theory-based and image structure-based metrics.

scholarly journals An Effective Contour Detection based Image Retrieval using Multi-Fusion Method and Neural Network

2021 ◽  
Author(s):  
Rohit Raja ◽  
Sandeep Kumar ◽  
Shilpa Choudhary ◽  
Hemlata Dalmia
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Texture Features ◽  
Visual Image ◽  
Image Features ◽  
Contour Detection ◽  
Image Feature ◽  
Digital Platforms ◽  
Color Descriptor ◽  
Dominant Color Descriptor

Abstract Day by day, rapidly increasing the number of images on digital platforms and digital image databases has increased. Generally, the user requires image retrieval and it is a challenging task to search effectively from the enormous database. Mainly content-based image retrieval (CBIR) algorithm considered the visual image feature such as color, texture, shape, etc. The non-visual features also play a significant role in image retrieval, mainly in the security concern and selection of image features is an essential issue in CBIR. Performance is one of the challenging tasks in image retrieval, according to current CBIR studies. To overcome this gap, the new method used for CBIR using histogram of gradient (HOG), dominant color descriptor (DCD) & hue moment (HM) features. This work uses color features and shapes texture in-depth for CBIR. HOG is used to extract texture features. DCD on RGB and HSV are used to improve efficiency and computation. A neural network (NN) is used to extract the image features, which improves the computation using the Corel dataset. The experimental results evaluated on various standard benchmarks Corel-1k, Corel-5k datasets, and outcomes of the proposed work illustrate that the proposed CBIR is efficient for other state-of-the-art image retrieval methods. Intensive analysis of the proposed work proved that the proposed work has better precision, recall, accuracy

scholarly journals Contextual Masking of Oriented Lines: Interactions Between Surface Segmentation Cues

2005 ◽  
Vol 94 (1) ◽  
pp. 576-589 ◽  
Author(s):  
Maarten J. van der Smagt ◽  
Christian Wehrhahn ◽  
Thomas D. Albright
Keyword(s):  
Time Course ◽  
Neuronal Response ◽  
Receptive Fields ◽  
Visual Image ◽  
Image Feature ◽  
V1 Neurons ◽  
Surface Segmentation ◽  
Single Feature ◽  
Cortex Area ◽  
Perceptual Masking

The ability of human observers to detect and discriminate a single feature of a visual image deteriorates markedly when the targeted feature is surrounded by others of a similar kind. This perceptual masking is mirrored by the suppressive effects of surround stimulation on the responses of neurons in primary visual cortex (area V1). Both perceptual and neuronal masking effects are partially relieved, however, if the targeted image feature is distinguished from surrounding features along some dimension, such as contour orientation. Masking relief is likely to play an important role in perceptual segmentation of complex images. Because dissimilar surfaces usually differ along multiple feature dimensions, we tested the possibility that those differences may influence segmentation in an invariant manner. As expected, we found that the presence of surrounding features resulted in perceptual masking and neuronal response suppression in area V1, but that either orientation or contrast polarity differences between the target and surrounding features was sufficient to partially relieve these effects. Simultaneous differences along both dimensions, however, yielded no greater relief from masking than did either difference alone. Although the averaged neuronal effects of orientation polarity cues were thus invariant, the time course over which these effects emerged after each stimulus appearance was different for the two cues. These findings refine our understanding of the functions of nonclassical receptive fields, and they support a key role for V1 neurons in surface segmentation.

High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

1986 ◽  
Vol 44 ◽  
pp. 414-415
Author(s):  
W. Krakow ◽  
D. A. Smith
Keyword(s):  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Image Features ◽  
Image Feature ◽  
Resolution Electron ◽  
Tilt Boundaries ◽  
Processing Techniques ◽  
Atom Position ◽  
Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

Multimedia system and database simulation based on internet of things and cloud service platform

2020 ◽  
pp. 1-12
Author(s):  
Zhang Caiqian ◽  
Zhang Xincheng
Keyword(s):  
Internet Of Things ◽  
Cloud Service ◽  
Multimedia System ◽  
The Internet ◽  
Multimedia Database ◽  
Cloud Platform ◽  
Service Platform ◽  
Device Management ◽  
Cloud Service Platform ◽  
The Internet Of Things

The existing stand-alone multimedia machines and online multimedia machines in the market have certain deficiencies, so they cannot meet the actual needs. Based on this, this research combines the actual needs to design and implement a multi-media system based on the Internet of Things and cloud service platform. Moreover, through in-depth research on the MQTT protocol, this study proposes a message encryption verification scheme for the MQTT protocol, which can solve the problem of low message security in the Internet of Things communication to a certain extent. In addition, through research on the fusion technology of the Internet of Things and artificial intelligence, this research designs scheme to provide a LightGBM intelligent prediction module interface, MQTT message middleware, device management system, intelligent prediction and push interface for the cloud platform. Finally, this research completes the design and implementation of the cloud platform and tests the function and performance of the built multimedia system database. The research results show that the multimedia database constructed in this paper has good performance.

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