Author name cluster

Zhenzhou Shao

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

IROS Conference 2021 Conference Paper

Fast and Unsupervised Non-Local Feature Learning for Direct Volume Rendering of 3D Medical Images

Xinmei Fu
Zhenzhou Shao
Ying Qu 0001
Yong Guan
Yibo Zou
Zhiping Shi 0002
Jindong Tan

To improve the efficiency of medical visualization for computer aided surgery, we propose a fast and unsupervised 3D-CNN based non-local feature learning network. The proposed network consists of an encoder structure and a decoder structure. The encoder of the network projects the cube into a high-dimensional feature space, and the decoder of the network reconstructs the cube from the feature space. The decoder of the network serves as a dictionary shared by the cube to enforce the features for similar parts to be similar although they may distribute at disjointed locations. With such structures, the network is able to extract non-local features of the entire data. Moreover, a sparse constraint is incorporated into the network to increase the discriminative of the non-local features. Then the extracted non-local features of each voxel are fused with the corresponding position matrix and Hessian matrix for the voxel classification using Random Forest. Finally, a multidimensional transfer function is designed to enable the volume rendering. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods with much less training time.

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IROS Conference 2020 Conference Paper

Batch Normalization Masked Sparse Autoencoder for Robotic Grasping Detection

Zhenzhou Shao
Ying Qu 0001
Guangli Ren
Guohui Wang
Yong Guan
Zhiping Shi 0002
Jindong Tan

To improve the accuracy of the grasping detection, this paper proposes a novel detector with batch normalization masked evaluation model. It is designed with a two-layer sparse autoencoder, and a Batch Normalization based mask is incorporated into the second layer of the model to effectively reduce the features with weak correlation. The extracted features from such model are more distinctive, which guarantees the higher accuracy of the grasping detection. Extensive experiments show that the proposed evaluation model outperforms the state-of- the-art, and the recognition accuracy can reach 95. 51% for robotic grasping detection.

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IROS Conference 2019 Conference Paper

Inverse Dynamics Modeling of Robotic Manipulator with Hierarchical Recurrent Network

Pengfei Sun
Zhenzhou Shao
Ying Qu 0001
Yong Guan
Jindong Tan

Inverse dynamics modeling is a critical problem for the computed-torque control of robotic manipulator. This paper presents a novel recurrent network based on the modified Simple Recurrent Unit (SRU) with hierarchical memory (SRU-HM), which is achieved by the nested SRU structure. In this way, it enables the capability to retain the long-term information in the distant past, compared with the conventional stacked structure. The hidden state of SRU is able to provide more complete information relevant to current prediction. Experimental results demonstrate that the proposed method can improve the accuracy of dynamics model greatly, and outperforms the state-of-the-art methods.

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IROS Conference 2018 Conference Paper

Unsupervised Trajectory Segmentation and Promoting of Multi-Modal Surgical Demonstrations

Zhenzhou Shao
Hongfa Zhao
Jiexin Xie
Ying Qu 0001
Yong Guan
Jindong Tan

To improve the efficiency of surgical trajectory segmentation for robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue. Unsupervised deep learning network, stacking convolutional auto-encoder, is employed to extract more discriminative features from videos in an effective way. To further improve the accuracy of segmentation, on one hand, wavelet transform is used to filter out the noises existed in the features from video and kinematic data. On the other hand, the segmentation result is promoted by identifying the adjacent segments with no state transition based on the predefined similarity measurements. Extensive experiments on a public dataset JIGSAWS show that our method achieves much higher accuracy of segmentation than state-of-the-art methods in the shorter time.

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IROS Conference 2017 Conference Paper

A fast search algorithm based on image pyramid for robotic grasping

Guangli Ren
Zhenzhou Shao
Yong Guan
Ying Qu 0001
Jindong Tan
Hongxing Wei
Guofeng Tong

To improve the search efficiency of robotic grasping detection, this paper presents a novel search algorithm based on the image pyramid. It significantly reduces the search space for grasping position detection using the coarse-to-fine strategy. The proposed method searches the positions from the top layer of the pyramid, and initializes the search area at the next layer. The sparse automatic encoder is employed to construct the model which is used to evaluate the grasp quality. The experimental results demonstrate that the proposed search algorithm can improve efficiency of the robotic grasping detection with the comparative performance on the grasp quality.

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ICRA Conference 2014 Conference Paper

Geometry constrained sparse embedding for multi-dimensional transfer function design in direct volume rendering

Zhenzhou Shao
Yong Guan
Hongsheng He
Jindong Tan

Direct volume rendering (DVR) is commonly employed for the medical visualization. Multi-dimensional transfer functions are used in DVR to emphasize the region of interest in details. However, it is impractical to interact directly with the functions in more than three dimension. This paper proposes a novel framework called geometry constrained sparse embedding (GCSE) for dimensionality reduction (DR). GCSE allows the conventional DR methods to be applied to a dictionary with much smaller atoms instead. The mapping derived from the dictionary feeds to the original features to obtain the ones in the reduced dimension. To obtain a good dictionary, the intrinsic structure of features is encoded in the sparse embedding based on a geometry distance. In addition, stochastic gradient descent algorithm is employed to speed up the dictionary learning. Various experiments have been conducted using both synthetic and real CT data sets. Compared with conventional methods, GCSE not only produces the comparable results, but also performs well with the capability to handle the large data set more powerfully. The rendering results using the real CT data has demonstrated the effectiveness of GCSE.

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ICRA Conference 2011 Conference Paper

Pedestrian positioning with physical activity classification for indoors

Xi Chen
Sheng Hu
Zhenzhou Shao
Jindong Tan

This paper presents a wearable Inertial Measurement Unit pedestrian positioning system for indoors. Hidden Markov Model (HMM) is introduced to pre-process the sensor data and classify common activities. HMM also complements local minimum angular rate value for capturing the onset/end of each step. ZUPT algorithm are implemented to correct the walking velocity at step stance phase when errors existed. A novel acceleration-based approach combined with gyroscope data is developed to achieve a better heading estimation. Proposed method is able to reduce drift errors from gyroscopes and avoid electromagnetic perturbance to magnetometers when estimate subject's position. Experiment results show the positioning system achieves approximately 99% accuracy.

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