Saliency Detection by Multilevel Deep Pyramid Model

Wang, Hai; Dai, Lei; Cai, Yingfeng; Chen, Long; Zhang, Yong

doi:10.1155/2018/8249180

Cited by 3 publications

(3 citation statements)

References 28 publications

(31 reference statements)

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“…After the learning feature ends in the deep convolutional network, a small number of tagged data input networks are used to fine-tune the parameters, and the network is continuously optimized. 17,21,26,27,28 The output of the fully connected layer k can be obtained by weighting the inputs and by the effect of the activation function…”

Section: Deep Convolutional Networkmentioning

confidence: 99%

Vehicle license plate recognition method based on deep convolution network in complex road scene

Liu

Cai

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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The license plate robust recognition algorithm in complex road scene has both theoretical and practical values. The existing license plate recognition algorithm can achieve better recognition results under ideal road scenes such as moderate light intensity, good shooting angle, and clear license plate target, but in complex road scenes such as fast speed, blurred aging of license plates, and low illumination such as rainy days, the effectiveness of the license plate recognition algorithm still needs to be improved. Based on the realistic requirements of license plate recognition algorithm and in-depth analysis of the principle of deep convolution network, we designed a deep convolution network for Chinese characters, letters, and numbers in the license plate to automatically learn the essential features of the image to make up for the limitation of the artificial feature recognition of the traditional license plate recognition algorithm. At the same time, according to the convolution kernel, downsampling, and nonlinear operation of the deep convolution network, the nonlinear abstraction ability of the license plate character feature is improved. The experimental results show that the proposed method can quickly and accurately identify the license plate character in complex road scenes. The recognition accuracy is better than the existing license plate recognition algorithm, which improves the accuracy of license plate recognition and achieves an ideal license plate recognition effect.

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Section: Deep Convolutional Networkmentioning

confidence: 99%

Vehicle license plate recognition method based on deep convolution network in complex road scene

Liu

Cai

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…ere are several ways to extract the highlighted area of the image, including [15][16][17][18][19][20][21][22]. In [15], a new classification scheme is presented which combines CNN and visual attention mechanism; Shariatmadar and Faez [16] proposed a model that combines the bottom-up and top-down features to extract the prominent part of the image; He et al, in [17], extended Itti's model by using structure tensor; Luo et al [18] identified the salient object based on backbone enhanced network; Yang et al [19] detected the salient part of the image by introducing the double-random-walks; in [20], the BU and TD features of a single image are used to detect salient region; Wang et al, in [21], proposed a model of saliency detection related to multilevel deep pyramid (MLDP), and finally, in [22], the diver target detection is performed based on a saliency detection method.…”

Section: Introductionmentioning

confidence: 99%

A Data‐Driven and Biologically Inspired Preprocessing Scheme to Improve Visual Object Recognition

Shariatmadar

Faez

2021

Computational Intelligence and Neuroscience

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Autonomous object recognition in images is one of the most critical topics in security and commercial applications. Due to recent advances in visual neuroscience, the researchers tend to extend biologically plausible schemes to improve the accuracy of object recognition. Preprocessing is one part of the visual recognition system that has received much less attention. In this paper, we propose a new, simple, and biologically inspired pre processing technique by using the data-driven mechanism of visual attention. In this part, the responses of Retinal Ganglion Cells (RGCs) are simulated. After obtaining these responses, an efficient threshold is selected. Then, the points of the raw image with the most information are extracted according to it. Then, the new images with these points are created, and finally, by combining these images with entropy coefficients, the most salient object is located. After extracting appropriate features, the classifier categorizes the initial image into one of the predefined object categories. Our system was evaluated on the Caltech-101 dataset. Experimental results demonstrate the efficacy and effectiveness of this novel method of preprocessing.

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“…Saliency object detection can provide the mask of wildlife for the progressive transmission process, which is utilized to separate the object and background information. Conventional saliency detection algorithms, such as the human-computer interaction [ 18 ], visual attention model [ 19 ] and multilevel deep pyramid model [ 20 ] have undesired algorithm complexity issues.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images

Feng

Wang

et al. 2019

Sensors

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In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.

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Saliency Detection by Multilevel Deep Pyramid Model

Cited by 3 publications

References 28 publications

Vehicle license plate recognition method based on deep convolution network in complex road scene

Vehicle license plate recognition method based on deep convolution network in complex road scene

A Data‐Driven and Biologically Inspired Preprocessing Scheme to Improve Visual Object Recognition

A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images

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