Joint Reflectance Field Estimation and Sparse Representation for Face Image Illumination Preprocessing and Recognition

Zhang, Jian; Liu, Weihua; Bo, Liling; Zhang, Heng; Li, Hongran; Xu, Shuai

doi:10.1007/s11063-020-10316-6

Cited by 2 publications

(4 citation statements)

References 32 publications

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“…Firstly, sample sets are divided into training samples and test samples. The original sample generates a virtual sample by (1). Virtual samples and original samples constitute the sample set.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Step 2. The original sample generates a virtual sample by (1). The virtual sample and the original sample make up the experimental dataset.…”

Section: 𝑤 = 𝐻 𝐻mentioning

confidence: 99%

“…It can effectively analyze the content of the image to obtain the key information in the image and give the correct judgment, which is of great significance to real work life and social development. Generally, image classification includes image preprocessing [1], image feature extraction [2,3], feature location, feature selection [4] and classifier design [5,6]. Image feature extraction is an important algorithm and faces many challenges in image classification, such as intra-class change, scale change, viewpoint change, illumination conditions and background interference.…”

Section: Introductionmentioning

confidence: 99%

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Improved Multiple Vector Representations of Images and Robust Dictionary Learning

et al. 2022

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Each sparse representation classifier has different classification accuracy for different samples. It is difficult to achieve good performance with a single feature classification model. In order to balance the large-scale information and global features of images, a robust dictionary learning method based on image multi-vector representation is proposed in this paper. First, this proposed method generates a reasonable virtual image for the original image and obtains the multi-vector representation of all images. Second, the same dictionary learning algorithm is used for each vector representation to obtain multiple sets of image features. The proposed multi-vector representation can provide a good global understanding of the whole image contour and increase the content of dictionary learning. Last, the weighted fusion algorithm is used to classify the test samples. The introduction of influencing factors and the automatic adjustment of the weights of each classifier in the final decision results have a significant indigenous effect on better extracting image features. The study conducted experiments on the proposed algorithm on a number of widely used image databases. A large number of experimental results show that it effectively improves the accuracy of image classification. At the same time, to fully dig and exploit possible representation diversity might be a better way to lead to potential various appearances and high classification accuracy concerning the image.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Step 2. The original sample generates a virtual sample by (1). The virtual sample and the original sample make up the experimental dataset.…”

Section: 𝑤 = 𝐻 𝐻mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Multiple Vector Representations of Images and Robust Dictionary Learning

et al. 2022

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show abstract

“…Then, neighbouring bone meshes are connected to obtain a mesh with a complete topology. Finally, we incorporate the object representation technique from NeRS, 3 which represents the assembled mesh as a Multilayer Perceptron (MLP) network. This network is capable of predicting 3D deformations for given continuous coordinates, thereby facilitating mesh optimization.…”

Section: Introductionmentioning

confidence: 99%

S‐LASSIE: Structure and smoothness enhanced learning from sparse image ensemble for 3D articulated shape reconstruction

Feng,

He,

Wang

et al. 2024

Computer Animation & Virtual

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In computer vision, the task of 3D reconstruction from monocular sparse images poses significant challenges, particularly in the field of animal modelling. The diverse morphology of animals, their varied postures, and the variable conditions of image acquisition significantly complicate the task of accurately reconstructing their 3D shape and pose from a monocular image. To address these complexities, we propose S‐LASSIE, a novel technique for 3D reconstruction of quadrupeds from monocular sparse images. It requires only 10–30 images of similar breeds for training. To effectively mitigate depth ambiguities inherent in monocular reconstructions, S‐LASSIE employs a multi‐angle projection loss function. In addition, our approach, which involves fusion and smoothing of bone structures, resolves issues related to disjointed topological structures and uneven connections at junctions, resulting in 3D models with comprehensive topologies and improved visual fidelity. Our extensive experiments on the Pascal‐Part and LASSIE datasets demonstrate significant improvements in keypoint transfer, overall 2D IOU and visual quality, with an average keypoint transfer and overall 2D IOU of 59.6% and 86.3%, respectively, which are superior to existing techniques in the field.

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Joint Reflectance Field Estimation and Sparse Representation for Face Image Illumination Preprocessing and Recognition

Cited by 2 publications

References 32 publications

Improved Multiple Vector Representations of Images and Robust Dictionary Learning

Improved Multiple Vector Representations of Images and Robust Dictionary Learning

S‐LASSIE: Structure and smoothness enhanced learning from sparse image ensemble for 3D articulated shape reconstruction

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