Visual servoing based on efficient histogram information

Abidi, Hajer; Chtourou, Mohamed; Kaâniche, Khaled; Mekki, Hassen

doi:10.1007/s12555-016-0070-2

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Weak and insignificant links may represent spurious connections, especially in the functional and effective networks that tend to obscure the topology of strong and significant connections and are therefore often discarded by applying an absolute or a proportional weight threshold [71]. Therefore, we set the threshold value and then set the correlation coefficient values greater than the threshold value to "1" and those less than the threshold to "0" in order to binarize the connectivity matrices, similar to image thresholding in image processing [72]. Therefore, only the coefficients of the channels with significant connection strengths were retained as authentic connections.…”

Section: Hd-tdcs Stimulation Was Delivered By a Battery-drivenmentioning

confidence: 99%

Effects of HD‐tDCS on Resting‐State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

2018

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Functional connectivity is linked to several degenerative brain diseases prevalent in our aging society. Electrical stimulation is used for the clinical treatment and rehabilitation of patients with many cognitive disorders. In this study, the effects of high-definition transcranial direct current stimulation (HD-tDCS) on resting-state brain networks in the human prefrontal cortex were investigated by using functional near-infrared spectroscopy (fNIRS). The intrahemispheric as well as interhemispheric connectivity changes induced by 1 mA HD-tDCS were examined in 15 healthy subjects. Pearson correlation coefficient-based correlation matrices were generated from filtered time series oxyhemoglobin (ΔHbO) signals and converted into binary matrices. Common graph theory metrics were computed to evaluate the network changes. Systematic interhemispheric, intrahemispheric, and intraregional connectivity analyses demonstrated that the stimulation positively affected the resting-state connectivity in the prefrontal cortex. The poststimulation connectivity was increased throughout the prefrontal region, while focal HD-tDCS effects induced an increased rate of connectivity in the stimulated hemisphere. The graph theory metrics clearly distinguished the prestimulation and poststimulation networks for a range of thresholds. The results of this study suggest that HD-tDCS can be used to increase functional connectivity in the prefrontal cortex. The increase in functional connectivity can be explored clinically for neurorehabilitation of patients with degenerative brain diseases.

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Section: Hd-tdcs Stimulation Was Delivered By a Battery-drivenmentioning

confidence: 99%

Effects of HD‐tDCS on Resting‐State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

2018

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“…And when occlusion or beyond the field of view occurs, it is easy to cause servoing task failure. Some researchers proposed visual servoing methods, such as Fourier descriptors [9,10] , wavelet coefficients [11,12] , histograms [13,14] , luminance signal [15][16][17] , shape descriptors [18,19] , and so on, based on global image features. These global image features consider all image data, rather than simple geometric features, and hence have better robustness.…”

Section: Figure 1 Efficiency Comparison Between the Handling Robot An...mentioning

confidence: 99%

Image moments-based visual servoing control of bagged agricultural materials handling robot

Han

Zuo

Chi

et al. 2023

International Journal of Agricultural and Biological Engineering

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Manual handling is less efficient and sometimes even hazardous to humans in many areas, for example, agriculture. Using robots in those areas not only avoids human contact with such dangerous agricultural materials but also improves working efficiency. The motion of a robot is controlled using a technique called visual servoing that uses feedback information extracted from a vision sensor. In this study, a visual servoing method was proposed based on learning features and image moments for 3D targets to solve the problem of image moment-based visual servoing. A Gaussian process regression model was used to map the relationship between the image moment invariants and the rotational angles around the X-and Y-axes of the camera frame (denoted as γ and β). To obtain maximal decoupled structure and minimal nonlinearities of the image Jacobian matrix, it was assumed two image moment features, which are linearly proportional to γ and β. In addition to the other four standardized image moment features, a 6-DOF image moment-based visual servoing controller for the agricultural material handling robot was designed. Using this method, the problem of visual servoing task failure due to the singularity of the Jacobian matrix was solved, and it also had a better convergence effect for the part of the target image beyond the field of view and large displacement visual servoing system. The proposed algorithm was validated by carrying out experiments tracking bagged flour in a six-degree-of-freedom robotic system. The final displacement positioning accuracy reached the millimeter level and the direction angle positioning accuracy reached the level of 0.1°. The method still has a certain convergence effect when the target image is beyond the field of view. The experimental results have been presented to show the adequate behavior of the presented approach in robot handling operations. It provides reference for the application of visual servoing technology in the field of agricultural robots and has important theoretical significance and practical value.

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“…In their effort to develop a moving target tracking algorithm, Ong et al ( 2019 ) used color histograms of frame regions to locate the target object in each frame. Abidi et al ( 2017 ) used histogram of oriented gradients (HoGs) and minimized the Bhattacharyya distance between two sets of gradient orientations expressing the desired and current camera poses, in their vision-based robot control system. Doulah and Sazonov ( 2017 ) clustered food-related images using Bhattacharyya similarity.…”

Section: Image Similarity and Bhattacharyya Distancementioning

confidence: 99%

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation

2023

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Color similarity has been a key feature for content-based image retrieval by contemporary search engines, such as Google. In this study, we compare the visual content information of images, obtained through color histograms, with their corresponding hashtag sets in the case of Instagram posts. In previous studies, we had concluded that less than 25% of Instagram hashtags are related to the actual visual content of the image they accompany. Thus, the use of Instagram images' corresponding hashtags for automatic image annotation is questionable. In this study, we are answering this question through the computational comparison of images' low-level characteristics with the semantic and syntactic information of their corresponding hashtags. The main conclusion of our study on 26 different subjects (concepts) is that color histograms and filtered hashtag sets, although related, should be better seen as a complementary source for image retrieval and automatic image annotation.

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Visual servoing based on efficient histogram information

Cited by 5 publications

References 10 publications

Effects of HD‐tDCS on Resting‐State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

Effects of HD‐tDCS on Resting‐State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

Image moments-based visual servoing control of bagged agricultural materials handling robot

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation

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