Browsing by Author "Liu, Li"

Now showing 1 - 5 of 5
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    BS3D : Building-Scale 3D Reconstruction from RGB-D Images
    (Springer, 2023) Mustaniemi, Janne; Kannala, Juho; Rahtu, Esa; Liu, Li; Heikkilä, Janne; Department of Computer Science; Gade, Rikke; Felsberg, Michael; Kämäräinen, Joni-Kristian; Computer Science Professors; Computer Science - Visual Computing (VisualComputing); Computer Science - Artificial Intelligence and Machine Learning (AIML); Professorship Kannala Juho; University of Oulu; Tampere University
    Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems. Existing datasets often include small environments, have incomplete ground truth, or lack important sensor data, such as depth and infrared images. We propose an easy-to-use framework for acquiring building-scale 3D reconstruction using a consumer depth camera. Unlike complex and expensive acquisition setups, our system enables crowd-sourcing, which can greatly benefit data-hungry algorithms. Compared to similar systems, we utilize raw depth maps for odometry computation and loop closure refinement which results in better reconstructions. We acquire a building-scale 3D dataset (BS3D) and demonstrate its value by training an improved monocular depth estimation model. As a unique experiment, we benchmark visual-inertial odometry methods using both color and active infrared images.
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    Dynamic Clustering Scheme for Evolving Data Streams Based on Improved STRAP
    (2018-09-07) Sui, Jinping; Liu, Zhen; Jung, Alex; Liu, Li; Li, Xiang; Department of Computer Science; Professorship Jung Alexander; National University of Defense Technology
    A key problem within data mining is clustering of data streams. Most existing algorithms for data stream clustering are based on quite restrictive models for the cluster dynamics. In an attempt to overcome the limitations of existing methods, we propose a novel data stream clustering method, which we refer to as improved streaming affinity propagation (ISTRAP). The ISTRAP is based on an integrated evolution detection framework which ensures that new emerging clusters are recognized timely. Moreover, within ISTRAP, outdated clusters are removed and recurrent clusters are efficiently detected rather than being treated as novel clusters. The proposed ISTRAP is non-parametric in the sense of not requiring any prior information about the number or the centers of clusters. The effectiveness of ISTRAP is evaluated using numerical experiments.
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    Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams
    (IEEE, 2022) Sui, Jinping; Liu, Zhen; Liu, Li; Jung, Alex; Li, Xiang; Department of Computer Science; Professorship Jung Alexander; Helsinki Institute for Information Technology (HIIT); National University of Defense Technology
    In an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has received lots of attention because the scale of the data streams far exceeds the ability of expert human analysts. It has been observed that high-dimensional data are usually distributed in a union of low-dimensional subspaces. In this article, we propose a novel sparse representation-based DSC algorithm, called evolutionary dynamic sparse subspace clustering (EDSSC). It can cope with the time-varying nature of subspaces underlying the evolving data streams, such as subspace emergence, disappearance, and recurrence. The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering. During the first phase, a data structure for storing the statistic summary of data streams, called EDSSC summary, is proposed which can better address the dilemma between the two conflicting goals: 1) saving more points for accuracy of subspace clustering (SC) and 2) discarding more points for the efficiency of DSC. By further proposing an algorithm to estimate the subspace number, the proposed EDSSC does not need to know the number of subspaces. In the second phase, a more suitable index, called the average sparsity concentration index (ASCI), is proposed, which dramatically promotes the clustering accuracy compared to the conventionally utilized SCI index. In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted. Extinct experiments on real-world data streams show that the EDSSC outperforms the state-of-the-art online SC approaches.
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    Learning vector quantization and simulated annealing in automated feature selection for classification of voice disorders
    (1995) Liu, Li; Leinonen, Lea; Sähkötekniikan osasto; Teknillinen korkeakoulu; Helsinki University of Technology; Simula, Olli
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    Online Non-Cooperative Radar Emitter Classification from Evolving and Imbalanced Pulse Streams
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-07-15) Sui, Jinping; Liu, Zhen; Liu, Li; Peng, Bo; Liu, Tianpeng; Li, Xiang; Department of Computer Science; National University of Defense Technology; University of Oulu
    Recent research treats radar emitter classification (REC) problems as typical closed-set classification problems, i.e., assuming all radar emitters are cooperative and their pulses can be pre-obtained for training the classifiers. However, such overly ideal assumptions have made it difficult to fit real-world REC problems into such restricted models. In this paper, to achieve online REC in a more realistic way, we convert the online REC problem into dynamically performing subspace clustering on pulse streams. Meanwhile, the pulse streams have evolving and imbalanced properties which are mainly caused by the existence of the non-cooperative emitters. Specifically, a novel data stream clustering (DSC) algorithm, called dynamic improved exemplar-based subspace clustering (DI-ESC), is proposed, which consists of two phases, i.e., initialization and online clustering. First, to achieve subspace clustering on subspace-imbalanced data, a static clustering approach called the improved ESC algorithm (I-ESC) is proposed. Second, based on the subspace clustering results obtained, DI-ESC can process the pulse stream in real-time and can further detect the emitter evolution by the proposed evolution detection strategy. The typically dynamic behavior of emitters such as appearing, disappearing and recurring can be detected and adapted by the DI-ESC. Extinct experiments on real-world emitter data show the sensitivity, effectiveness, and superiority of the proposed I-ESC and DI-ESC algorithms.