Fast point cloud registration in multidirectional affine transformation

Shu, Qin; He, Xiuli; Wang, Chang; Yang, Yunxiu; Cui, Zhongma

doi:10.1016/j.ijleo.2020.165884

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…The Affine Transformation [33], [34] is a linear transformation method of two-dimensional coordinates, which can maintain the straightness and parallelism of two-dimensional graphs at the same time. In other words, affine transformation allows the image to be inclined at will and can be contracted at will in two directions.…”

Section: Image Expansionmentioning

confidence: 99%

A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition

Zhang

Tian

2021

ELEKTRON ELEKTROTECH

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Facial expression is one of the important ways of transferring emotion in interpersonal communication, and it has been widely used in many interpersonal communication systems. The traditional facial expression recognition methods are not intelligent enough to manage the model uncertainty. The deep learning method has obvious ability to deal with model uncertainty in the image recognition. The deep learning method is able to complete the facial expression work, but the recognition rate can be further improved by a hybrid learning strategy. In this paper, a Fuzzy optimized convolutional neural network-recurrent neural network (CNN-RNN) method for facial expression recognition is proposed to solve the problems of direct image convolution without image enhancement and simple convolution stack ignoring feature layer-by-layer convolution resulting in information loss. Firstly, each face image is scaled by the bilinear interpolation and the affine transformation is adopted to expand the image data to avoid the shortage of the data set. Then the feature map of the facial expression is extracted by the CNN with small information loss. To deal with the uncertainty in the feature map, the Fuzzy logic is employed to reduce the uncertainty by recognizing the highly nonlinear relationship between the features. Then the output of the Fuzzy model is fed with the RNN to classify different facial expression images. The recognition results based on the open datasets CK, Jaffe, and FER2013 show that the proposed Fuzzy optimized CNN-RNN method has a certain improvement in the recognition effect of different facial expression data sets compared with current popular algorithms.

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Section: Image Expansionmentioning

confidence: 99%

A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition

Zhang

Tian

2021

ELEKTRON ELEKTROTECH

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“…Without changing the meaning associated with customary norms and rules, Songket can be used as guidelines in life because the motif is very complex in the object [8]- [10]. Therefore, it is necessary to develop the value of the Affine transformation matrix in which the Affine transformation has definitely symmetric positive manifold matrix value [11]- [13]. It belongs to the Lie group structure and does not obey Euclidean space.…”

Section: Introductionmentioning

confidence: 99%

Development of Affine Transformation Method in the Reconstruction of Songket Motif

Ramadhanu¹,

Na’am²,

Nurcahyo³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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Songket is one of the clothes with a high artistic value besides Batik originating from Indonesia. Songket is widely used at weddings and other traditional formal events. Songket is the result of a traditional craft consisting of the main ingredients of cloth made of cotton and silk threads by inserting the feed (basic material) transversely on the Lunsing (yeast cloth). The quality of Songket art is found in the motifs on the Songket. This research has a conceptual outline in terms of improving the quality of Songket cloth and making it easier for craftsmen and artists to spare their art for the reconstruction process of the Silungkang Songket motif. This research aims to get a new Songket motif with a more modern and aesthetic motif but still has its original characteristics and identity. Based on these objectives, the researchers focused and motivated to develop the motif of one of the types of Songket in Indonesia, namely Silungkang Songket, in the form of a more modernized motif. This research develops a digital image transformation method named Affine Segmentation Point (ASP) to produce modernized Songket motifs. The basis of the developed ASP method is the Affine transformation. Songket image input was used as data and created a new motif in this study: a Songket image with the Rangkiang Lumbung Padi Besar motif. The results of this study can create excellent Songket motifs and modernization. The new motifs produced have been tested by Songket experts, Songket observers, Songket craftsmen, and Songket sellers with an interest rate of 97.5%. The method developed is very precise and can be used to develop other high-value artistic motifs on traditional cloth.

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“…

Point matching plays an important role in computer vision, such as object matching [1,2], image registration [3,4], autonomous navigation [5], 3D modeling [6] and so on. It aims to find the correspondences between two point sets and/or recover the transformation that map one point set to the other.

…”

mentioning

confidence: 99%

Point matching based on affine invariant centroid trees

Wang

Yan

Zhao³

et al. 2022

EURASIP J. Adv. Signal Process.

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Object detection can be formulated as a point matching problem when objects are modeled by point sets. Moments, which have been widely used for point matching, are limited to affine transformations as their support point sets cannot keep invariant. To address this problem, we developed an affine invariant centroid tree (AICT) to obtain a rigorous affine invariant support point set (SPS). The algorithm is constructed by a recursive process: the point set is first divided by the vector from the certain point to the centroid of the point set, and the centroids of subsets are used to generate vectors for renewed partitions. In addition, the centroids of the subsets are stored to form an AICT. The AICT represents the inherent structure of the point set. It is highly tolerant to noise and outliers due to the partitions on the whole point set. More importantly, it is affine invariant owing to the affine invariance of partition. Therefore, we can get rigorous affine invariant descriptors while moments are combined with AICT. The experimental results on synthesized and real data verify that our proposed algorithm outperforms the state-of-the-art point matching methods including shape context, iterative closet point, and the method adopting thin plate spline for rigid robust point matching (TPS-RPM).

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Fast point cloud registration in multidirectional affine transformation

Cited by 6 publications

References 28 publications

A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition

A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition

Development of Affine Transformation Method in the Reconstruction of Songket Motif

Point matching based on affine invariant centroid trees

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