Visual saliency object detection using sparse learning

Nasiripour, Reza; Farsi, Hassan; Mohamadzadeh, Sajad

doi:10.1049/iet-ipr.2018.6613

Cited by 10 publications

(6 citation statements)

References 60 publications

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“…In an artificial neural network [25][26][27], each neuron is connected to all the neurons in the adjacent layer. The output y of each latent neuron is obtained from the sum of each of the weighted inputs w • x plus a bias b from the following equation [25]:…”

Section: -The Proposed Methods 4-1-edge Detection Using a Deep Convol...mentioning

confidence: 99%

Edge Detection and Identification using Deep Learning to Identify Vehicles

Dorrani¹,

Farsi²,

Mohammadzadeh³

2022

jist

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A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract information of different levels from each layer to the pixel space of the edge, and then re-extract the feature, and perform sampling. The attributes are mapped to the pixel space of the edge and a threshold extractor of the edges. It is then compared with a background model. Using background subtraction, foreground objects are detected. The Gaussian mixture model is used to detect the vehicle. This method is performed on three videos, and compared with other methods; the results show higher accuracy. Therefore, the proposed method is stable against sharpness, light, and traffic. Moreover, to improve the detection accuracy of the vehicle, shadow removal conducted, which uses a combination of color and contour features to identify the shadow. For this purpose, the moving target is extracted, and the connected domain is marked to be compared with the background. The moving target contour is extracted, and the direction of the shadow is checked according to the contour trend to obtain shadow points and remove these points. The results show that the proposed method is very resistant to changes in light, high-traffic environments, and the presence of shadows, and has the best performance compared to the current methods.

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Section: -The Proposed Methods 4-1-edge Detection Using a Deep Convol...mentioning

confidence: 99%

Edge Detection and Identification using Deep Learning to Identify Vehicles

Dorrani¹,

Farsi²,

Mohammadzadeh³

2022

jist

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“…Li et al [ 24 ] conducted research on the multi-scale difference of Gaussian fusion in the frequency domain and reduced the computation required in determining the proper scale of salient objects. Nasiripour et al [ 25 ] proposed a new method to extract an object saliency map, which can integrate extracted features based on K-means singular-value decomposition. Qi et al [ 26 ] used a graph algorithm based on the ranking method to detect and segment the most salient objects from the background, which is designed as a two-stage ranking salient object detection method.…”

Section: Related Researchmentioning

confidence: 99%

A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network

Zhao

Min

2021

Sensors

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An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can help security supervisors locate the abnormal regions automatically. The existing related methods lack an efficient means to find important area candidates from a scene and have failed to judge whether or not a candidate attracts people’s attention. To realize the detection of an important area, this study proposes a two-stage method with a novel multi-input attention network (MAN). The first stage, called important area candidate generation, aims to generate candidate important areas with an image-processing algorithm (i.e., K-means++, image dilation, median filtering, and the RLSA algorithm). The candidate areas can be selected automatically for further analysis. The second stage, called important area candidate classification, aims to detect an important area from candidates with MAN. In particular, MAN is designed as a multi-input network structure, which fuses global and local image features to judge whether or not an area attracts people’s attention. To enhance the representation of candidate areas, two modules (i.e., channel attention and spatial attention modules) are proposed on the basis of the attention mechanism. These modules are mainly based on multi-layer perceptron and pooling operation to reconstruct the image feature and provide considerably efficient representation. This study also contributes to a new dataset called gathering place important area detection for testing the proposed two-stage method. Lastly, experimental results show that the proposed method has good performance and can correctly detect an important area.

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“…These areas are segmented based on pixels with similar brightness intensities. An important process in this type of segmentation is to examine each pixel and assign it to the area most closely associated with it [13].…”

Section: Related Workmentioning

confidence: 99%

“…Based on the type of curve, these models can be divided into two parametric and non‐parametric groups. In the following, we will describe these methods [13].…”

Section: Related Workmentioning

confidence: 99%

Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks

et al. 2020

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Medical image segmentation plays a key role in identifying the disease type. In the last decade, various methods have been proposed for medical images segmentation. Despite many efforts made in medical imaging, segmentation of medical images still faces challenges, concerning the variety of shape, location, and texture quality. According to recent studies and magnetic resonance imaging, segmentation of brain images at around 6 months of age is a challenging issue in brain image segmentation due to low tissue contrast between white matter (WM) and grey matter (GM) regions. In this study, using the deep learning model, the convolutional network for the brain fragmentation is presented. First, the image quality is improved using the pre‐processing method. The number of layers utilised in the proposed method is less than that of known models. In the pooling layer, instead of using the maximum function, the averaging function is employed. Sixty‐four batches are also considered to improve the performance of the proposed method. The method is evaluated on the iSeg‐2017 database. The DISC and ASC measures of the proposed method for the three classes of GM, WM, and cerebrovascular fluid are 0.902, 0.594, 0.930, 0.481, 0.971, and 0.231, respectively.

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Visual saliency object detection using sparse learning

Cited by 10 publications

References 60 publications

Edge Detection and Identification using Deep Learning to Identify Vehicles

Edge Detection and Identification using Deep Learning to Identify Vehicles

A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network

Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks

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