Building Recognition Using Gist Feature Based on Locality Sensitive Histograms of Oriented Gradients

Li, Bin; Sun, Fuqiang; Zhang, Yonghan

doi:10.1134/s1054661819020044

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Uniformity is the frequency where adjacent pixels have the same value, and the feature value is large when the greyscale symbiotic matrix is diagonally large, as shown in Figure 6 (d). The calculation formula is expressed as follows: (13) (4) GIST Features: Represent a low-dimensional scene that captures a set of perceived dimension characteristics, which are naturality, openness, roughness, dilation and severities through a Gabor filter [19] .…”

Section: Original Imagementioning

confidence: 99%

Coal quality prediction based on multi-feature fusion of flotation foam images

Bai

2020

Preprint

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Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

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Section: Original Imagementioning

confidence: 99%

Coal quality prediction based on multi-feature fusion of flotation foam images

Bai

2020

Preprint

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“…Though they perform well in particular visual tasks, they cannot manage all activities since each feature is created from a distinct component of the image. After the rise of deep learning, the approach has made the development of numerous visual tasks and the segmentation algorithm of medical rehabilitation photos possible [3].…”

Section: Introductionmentioning

confidence: 99%

Health System for Exercise Rehabilitation Detection Retina Images and IoT Blockchain

Sriman,

et al. 2023

ijmst

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Smart Healthcare which is based on deep learning is becoming increasingly popular because of its practical applications and has grown popular after its incorporation with IoT. Degenerative eye disease is the main factor of blindness in people of working age. Asian nations with large populations, like India and China, are on the edge of a diabetes epidemic. In terms of medical screening and diagnosis, a large number of diabetes patients posed a huge problem for skilled clinicians. The idea is to employ deep learning algorithms to detect blind spots in the eye and estimate the severity of the stage. We present an optimum approach for detecting blindness in retinal images based on recently released pre-trained EfficientNet models, as well as a comparative assessment of many innovative neural network models, in this study. On a benchmark dataset of retina images obtained by diagnostic imaging at various imaging phases, our EfficientNet-B5-based model assessment performs better than CNN and ResNet50 models.

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“…Before the popularity of deep learning, many researchers were looking for features that could better express the essential properties of images. The better features proposed at the time were Gist [3], HoG [4], SIFT [5], SURF [6], etc. Although these features yield good results on some visual tasks, because each feature is constructed from some aspect of the image, it cannot accommodate all visual tasks.…”

Section: Introductionmentioning

confidence: 99%

Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

Ding

Ren

2019

IEEE Access

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Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans. Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image. Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN). The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature. This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express. Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm. The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel. Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed. The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved. Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.

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Building Recognition Using Gist Feature Based on Locality Sensitive Histograms of Oriented Gradients

Cited by 7 publications

References 19 publications

Coal quality prediction based on multi-feature fusion of flotation foam images

Coal quality prediction based on multi-feature fusion of flotation foam images

Health System for Exercise Rehabilitation Detection Retina Images and IoT Blockchain

Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

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