This paper proposes Graph Grouping (GG) loss for metric learning and its application to face verification. GG loss predisposes image embeddings of the same identity to be close to each other, and those of different id...
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ISBN:
(纸本)9781728180687
This paper proposes Graph Grouping (GG) loss for metric learning and its application to face verification. GG loss predisposes image embeddings of the same identity to be close to each other, and those of different identities to be far from each other by constructing and optimizing graphs representing the relation between images. Further, to reduce the computational cost, we propose an efficient way to compute GG loss for cases where embeddings are L-2 normalized. In experiments, we demonstrate the effectiveness o(f) the proposed method for face verification on the VoxCeleb dataset. The results show that the proposed GG loss outperforms conventional losses for metric learning.
The digital fish provenance and quality tracking system is essential for the seafood supply chain. As a part of this system, we develop a vision-based fish processing system to automatically perform fish freshness est...
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ISBN:
(纸本)9781728180687
The digital fish provenance and quality tracking system is essential for the seafood supply chain. As a part of this system, we develop a vision-based fish processing system to automatically perform fish freshness estimation, size measurement and species classification. Under the constrained illumination environment, our system is able to auto-process the fish selection, thus greatly reduce the human labour and bring trust and efficiency to the seafood supply chain from catch to market.
In the age of digital content creation and distribution, steganography, that is, hiding of secret data within another data is needed in many applications, such as in secret communication between two parties, piracy pr...
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ISBN:
(纸本)9781728185514
In the age of digital content creation and distribution, steganography, that is, hiding of secret data within another data is needed in many applications, such as in secret communication between two parties, piracy protection, etc. In image steganography, secret data is generally embedded within the image through an additional step after a mandatory image enhancement process. In this paper, we propose the idea of embedding data during the image enhancement process. This saves the additional work required to separately encode the data inside the cover image. We used the Alpha-Trimmed mean filter for image enhancement and XOR of the 6 MSBs for embedding the two bits of the bitstream in the 2 LSBs whereas the extraction is a reverse process. Our obtained quantitative and qualitative results are better than a methodology presented in a very recent paper.
RDPlot is an open source GUI application for plotting Rate-Distortion (RD)-curves and calculating Bjontegaard Delta (BD) statistics [1]. It supports parsing the output of commonly used reference software packages, par...
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Learning-based image compression has reached the performance of classical methods such as BPG. One common approach is to use an autoencoder network to map the pixel information to a latent space and then approximate t...
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ISBN:
(纸本)9781728185514
Learning-based image compression has reached the performance of classical methods such as BPG. One common approach is to use an autoencoder network to map the pixel information to a latent space and then approximate the symbol probabilities in that space with a context model. During inference, the learned context model provides symbol probabilities, which are used by the entropy encoder to obtain the bitstream. Currently, the most effective context models use autoregression, but autoregression results in a very high decoding complexity due to the serialized data processing. In this work, we propose a method to parallelize the autoregressive process used for image compression. In our experiments, we achieve a decoding speed that is over 8 times faster than the standard autoregressive context model almost without compression performance reduction.
In recent years, with the popularization of 3D technology, stereoscopic image quality assessment (SIQA) has attracted extensive attention. In this paper, we propose a two-stage binocular fusion network for SIQA, which...
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ISBN:
(纸本)9781728185514
In recent years, with the popularization of 3D technology, stereoscopic image quality assessment (SIQA) has attracted extensive attention. In this paper, we propose a two-stage binocular fusion network for SIQA, which takes binocular fusion, binocular rivalry and binocular suppression into account to imitate the complex binocular visual mechanism in the human brain. Besides, to extract spatial saliency features of the left view, the right view, and the fusion view, saliency generating layers (SGLs) are applied in the network. The SGL apply multi-scale dilated convolution to emphasize essential spatial information of the input features. Experimental results on four public stereoscopic image databases demonstrate that the proposed method outperforms the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.
With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has gradually been more and more important, and how to design a method in line with human visual perception is full...
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ISBN:
(纸本)9781728185514
With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has gradually been more and more important, and how to design a method in line with human visual perception is full of challenges due to the complex relationship between binocular views. In this article, firstly, convolutional neural network (CNN) based on the visual pathway of human visual system (HVS) is built, which simulates different parts of visual pathway such as the optic chiasm, lateral geniculate nucleus (LGN), and visual cortex. Secondly, the two pathways of our method simulate the 'what' and 'where' visual pathway respectively, which are endowed with different feature extraction capabilities. Finally, we find a different application way for 3D-convolution, employing it fuse the information from left and right view, rather than just extracting temporal features in video. The experimental results show that our proposed method is more in line with subjective score and has good generalization.
Neural compression has benefited from technological advances such as convolutional neural networks (CNNs) to achieve advanced bitrates, especially in image compression. In neural image compression, an encoder and a de...
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ISBN:
(纸本)9781728185514
Neural compression has benefited from technological advances such as convolutional neural networks (CNNs) to achieve advanced bitrates, especially in image compression. In neural image compression, an encoder and a decoder can run in parallel on a GPU, so the speed is relatively fast. However, the conventional entropy coding for neural image compression requires serialized iterations in which the probability distribution is estimated by multi-layer CNNs and entropy coding is processed on a CPU. Therefore, the total compression and decompression speed is slow. We propose a fast, practical, GPU-intensive entropy coding framework that consistently executes entropy coding on a GPU through highly parallelized tensor operations, as well as an encoder, decoder, and entropy estimator with an improved network architecture. We experimentally evaluated the speed and rate-distortion performance of the proposed framework and found that we could significantly increase the speed while maintaining the bitrate advantage of neural image compression.
Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increase...
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ISBN:
(纸本)9781728185514
Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increased because of ocean engineering and underwater robotics. Existing underwater image enhancement methods are based on various assumptions. However, it is almost impossible to define appropriate assumptions for underwater images due to the diversity of underwater images. Therefore, they are only effective for specific types of underwater images. Recently, underwater image enhancement algorisms using CNNs and GANS have been proposed, but they are not as advanced as other imageprocessing methods due to the lack of suitable training data sets and the complexity of the issues. To solve the problems, we propose a novel underwater image enhancement method which combines the residual feature attention block and novel combination of multi-scale and multi-patch structure. Multi-patch network extracts local features to adjust to various underwater images which are often Non-homogeneous. In addition, our network includes multi-scale network which is often effective for image restoration. Experimental results show that our proposed method outperforms the conventional method for various types of images.
In this paper, we propose an optimized dual stream convolutional neural network (CNN) considering binocular disparity and fusion compensation for no-reference stereoscopic image quality assessment (SIQA). Different fr...
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ISBN:
(纸本)9781728185514
In this paper, we propose an optimized dual stream convolutional neural network (CNN) considering binocular disparity and fusion compensation for no-reference stereoscopic image quality assessment (SIQA). Different from previous methods, we extract both disparity and fusion features from multiple levels to simulate hierarchical processing of the stereoscopic images in human brain. Given that the ocular dominance plays an important role in quality evaluation, the fusion weights assignment module (FWAM) is proposed to assign weight to guide the fusion of the left and the right features respectively. Experimental results on four public stereoscopic image databases show that the proposed method is superior to the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.
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