Image Preprocessing-based Generalization and Transfer of Learning for Grasping in Cluttered Environments

Ahn, Kuk Hyun; Song, Jae-Bok

doi:10.1007/s12555-019-9618-z

Cited by 10 publications

(8 citation statements)

References 5 publications

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“…Randomized changing of the orientation, intensity, or size of an input picture, for instance, necessitates that a model evaluates what such a picture subject appears like in several conditions. ere are many color image processing methods, most of which can be divided into the following two types: first, there is a component image in the color image, which can decompose the module image in the color picture, and procedure the module image to synthesize it into a complete color image to obtain a processed color image [15]. Second, there are a large number of pixels in the color image, which can be processed to get the processed image [16].…”

Section: Image Preprocessingmentioning

confidence: 99%

An Improved Oil Painting Formation Using Advanced Image Processing

Liu

2022

Mobile Information Systems

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The fast advancement of computer science and information technology has resulted in a significant revolution in the commercial painting sector. The professional painting industry has experienced a dramatic breakthrough with the rapid expansion of computer science and technology. With the rapid development and comprehensive popularization of computer technology, more and more people use computer image processing technology in oil painting created as a new way of creation. Some artists use a variety of ways to study the way of oil painting creation and use image processing technology to stimulate the creator’s imagination and improve the efficiency of drawing sketches in optimizing creative ideas, drawing, and processing original works. However, the value of art comes from creation. Oil painting creation based on computer image processing is a new type of artistic production that still requires the integration of different artists’ ideas and creative concepts. The image processing technology is a tool and a way for artists to make art while also realizing the convergence of science, technology, and art. This paper uses computer image processing technology to study oil painting creation. First, it briefly discusses computer image processing technology, focusing on picture preprocessing and image graying, and then uses this technique in formation of oil painting. Second, it establishes the oil painting mathematical, direction field, and color conversion models completing the oil painting image creation by using this model. Finally, the advantages of this technology are analyzed from the two aspects of oil painting creation, painting technique, and brush direction. The results show that 75% of artists are very satisfied with the digital painting technique, and 65% of artists are very satisfied with the painting brush direction, which shows that the application effect of computer image processing technology in oil painting creation is remarkable.

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Section: Image Preprocessingmentioning

confidence: 99%

An Improved Oil Painting Formation Using Advanced Image Processing

Liu

2022

Mobile Information Systems

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“…Speci cally, some users have interacted more with the item, and the data of these users is relatively rich and reliable; while some users have little interaction data with the item, so the data of these users are sparse and can be poor usability. In order to solve this problem, this paper uses the transfer learning method to transfer the reverse prediction model of long-sequence users obtained above to short-sequence users [38]. e long-sequence reverse prediction task is taken as the source task, and the short-sequence reverse prediction task is taken as the target task.…”

Section: Pseudo-historical Item Generationmentioning

confidence: 99%

An Improved Sequential Recommendation Algorithm based on Short‐Sequence Enhancement and Temporal Self‐Attention Mechanism

Tang

Shen

et al. 2022

Complexity

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Sequential recommendation algorithm can predict the next action of a user by modeling the user’s interaction sequence with an item. However, most sequential recommendation models only consider the absolute positions of items in the sequence, ignoring the time interval information between items, and cannot effectively mine user preference changes. In addition, existing models perform poorly on sparse data sets, which make a poor prediction effect for short sequences. To address the above problems, an improved sequential recommendation algorithm based on short-sequence enhancement and temporal self-attention mechanism is proposed in this paper. In the proposed algorithm, a backward prediction model is trained first, to predict the prior items in the user sequence. Then, the reverse prediction model is used to generate a batch of pseudo-historical items before the initial items of the short sequence, to achieve the goal of enhancing the short sequence. Finally, the absolute position information and time interval information of the user sequence are modeled, and a time-aware self-attention model is adopted to predict the user’s next action and generate a recommendation list. Various experiments are conducted on two public data sets. The experimental results show that the method proposed in this paper has excellent performance on both dense and sparse data sets, and its effect is better than that of the state of the art.

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“…In order to remedy the problems above, Kuk-Hyun et al used the mask R-CNN to pretreat image input. The RPN structure was used here to distinguish the foreground information and background information in the same image, remove the most useless information in the image, reduce the network pressure and improve the efficiency of the algorithm [18]. Since the mask R-CNN can achieve good performance only when the quality of the algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

“…The RPN structure was used here to distinguish the foreground information and background information in the same image, remove the most useless information in the image, reduce the network pressure and improve the efficiency of the algorithm [18]. Since the mask R-CNN can achieve good performance only when the quality of the algorithm [18]. Since the mask R-CNN can achieve good performance only when the quality of the training mask R-CNN can recognize only the difference between the foreground and background; therefore, large amounts of unrelated information will be fused in the subsequent training phase, making the network environment crowded.…”

Section: Introductionmentioning

confidence: 99%

Grasping Control of a Vision Robot Based on a Deep Attentive Deterministic Policy Gradient

Xiong

Kong³

et al. 2022

IEEE Access

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Reinforcement learning can achieve excellent performance in the field of robotic grasping if the grasping target is stable. However, during applications in the real world, robot needs to overcome the effects of a complex working environment with different types of target objects, so it is more difficult to maintain the quality of action planning, even in the same scene. In order to make an agent have the ability to plan actions in a more adaptive way, the deep attentive deterministic policy gradient algorithm is applied in this article. An attention region proposal network is used to select the message of the pre-exploration area. Then this message is calculated using the adaptive exploration method to regulate the strategy as the target changes. Furthermore, a stratified reward function, which is used to reduce the negative influence of miscellaneous information brought by the sparse reward matrix, is defined according to the distance between the end effector and the center of the pre-exploration area. The results show that the DADPG is able to produce a robust strategy with noise interference, and can train in a more efficient way due to the hierarchical reward function.

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Image Preprocessing-based Generalization and Transfer of Learning for Grasping in Cluttered Environments

Cited by 10 publications

References 5 publications

An Improved Oil Painting Formation Using Advanced Image Processing

An Improved Oil Painting Formation Using Advanced Image Processing

An Improved Sequential Recommendation Algorithm based on Short‐Sequence Enhancement and Temporal Self‐Attention Mechanism

Grasping Control of a Vision Robot Based on a Deep Attentive Deterministic Policy Gradient

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