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200 results on '"Laganiere, Robert"'

1. FSMDet: Vision-guided feature diffusion for fully sparse 3D detector

Author

Liu, Tianran, Pasandi, Morteza Mousa, and Laganiere, Robert

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Fully sparse 3D detection has attracted an increasing interest in the recent years. However, the sparsity of the features in these frameworks challenges the generation of proposals because of the limited diffusion process. In addition, the quest for efficiency has led to only few work on vision-assisted fully sparse models. In this paper, we propose FSMDet (Fully Sparse Multi-modal Detection), which use visual information to guide the LiDAR feature diffusion process while still maintaining the efficiency of the pipeline. Specifically, most of fully sparse works focus on complex customized center fusion diffusion/regression operators. However, we observed that if the adequate object completion is performed, even the simplest interpolation operator leads to satisfactory results. Inspired by this observation, we split the vision-guided diffusion process into two modules: a Shape Recover Layer (SRLayer) and a Self Diffusion Layer (SDLayer). The former uses RGB information to recover the shape of the visible part of an object, and the latter uses a visual prior to further spread the features to the center region. Experiments demonstrate that our approach successfully improves the performance of previous fully sparse models that use LiDAR only and reaches SOTA performance in multimodal models. At the same time, thanks to the sparse architecture, our method can be up to 5 times more efficient than previous SOTA methods in the inference process., Comment: Accepted by European Conference on Computer Vision (ECCV) 2024 workshop on VCAD

Published
2024

2. What You See Is What You Detect: Towards better Object Densification in 3D detection

Author

Liu, Tianran, Zhang, Zeping, Pasandi, Morteza Mousa, and Laganiere, Robert

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Recent works have demonstrated the importance of object completion in 3D Perception from Lidar signal. Several methods have been proposed in which modules were used to densify the point clouds produced by laser scanners, leading to better recall and more accurate results. Pursuing in that direction, we present, in this work, a counter-intuitive perspective: the widely-used full-shape completion approach actually leads to a higher error-upper bound especially for far away objects and small objects like pedestrians. Based on this observation, we introduce a visible part completion method that requires only 11.3\% of the prediction points that previous methods generate. To recover the dense representation, we propose a mesh-deformation-based method to augment the point set associated with visible foreground objects. Considering that our approach focuses only on the visible part of the foreground objects to achieve accurate 3D detection, we named our method What You See Is What You Detect (WYSIWYD). Our proposed method is thus a detector-independent model that consists of 2 parts: an Intra-Frustum Segmentation Transformer (IFST) and a Mesh Depth Completion Network(MDCNet) that predicts the foreground depth from mesh deformation. This way, our model does not require the time-consuming full-depth completion task used by most pseudo-lidar-based methods. Our experimental evaluation shows that our approach can provide up to 12.2\% performance improvements over most of the public baseline models on the KITTI and NuScenes dataset bringing the state-of-the-art to a new level. The codes will be available at \textcolor[RGB]{0,0,255}{\url{{https://github.com/Orbis36/WYSIWYD}}

Published
2023

3. T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals

Author

Giroux, James, Bouchard, Martin, and Laganiere, Robert

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems. Radar does not possess the same drawbacks seen by other emission-based sensors such as LiDAR, primarily the degradation or loss of return signals due to weather conditions such as rain or snow. However, radar does possess traits that make it unsuitable for standard emission-based deep learning representations such as point clouds. Radar point clouds tend to be sparse and therefore information extraction is not efficient. To overcome this, more traditional digital signal processing pipelines were adapted to form inputs residing directly in the frequency domain via Fast Fourier Transforms. Commonly, three transformations were used to form Range-Azimuth-Doppler cubes in which deep learning algorithms could perform object detection. This too has drawbacks, namely the pre-processing costs associated with performing multiple Fourier Transforms and normalization. We explore the possibility of operating on raw radar inputs from analog to digital converters via the utilization of complex transformation layers. Moreover, we introduce hierarchical Swin Vision transformers to the field of radar object detection and show their capability to operate on inputs varying in pre-processing, along with different radar configurations, i.e. relatively low and high numbers of transmitters and receivers, while obtaining on par or better results than the state-of-the-art.

Published
2023

5. RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users

Author

Zhang, Ao, Nowruzi, Farzan Erlik, and Laganiere, Robert

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Signal Processing
Abstract

Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public radar datasets. In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map. To build the dataset, we propose an instance-wise auto-annotation method. Furthermore, a novel Range-Azimuth-Doppler based multi-class object detection deep learning model is proposed. The algorithm is a one-stage anchor-based detector that generates both 3D bounding boxes and 2D bounding boxes on Range-Azimuth-Doppler and Cartesian domains, respectively. Our proposed algorithm achieves 56.3% AP with IOU of 0.3 on 3D bounding box predictions, and 51.6% with IOU of 0.5 on 2D bounding box prediction. Our dataset and the code can be found at https://github.com/ZhangAoCanada/RADDet.git., Comment: Accepted by 18th Conference on Robots and Vision (CRV), CRV 2021

Published
2021

6. Point Cloud based Hierarchical Deep Odometry Estimation

Author

Nowruzi, Farzan Erlik, Kolhatkar, Dhanvin, Kapoor, Prince, and Laganiere, Robert

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computational Geometry, Computer Science - Robotics
Abstract

Processing point clouds using deep neural networks is still a challenging task. Most existing models focus on object detection and registration with deep neural networks using point clouds. In this paper, we propose a deep model that learns to estimate odometry in driving scenarios using point cloud data. The proposed model consumes raw point clouds in order to extract frame-to-frame odometry estimation through a hierarchical model architecture. Also, a local bundle adjustment variation of this model using LSTM layers is implemented. These two approaches are comprehensively evaluated and are compared against the state-of-the-art.

Published
2021

7. PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain

Author

Nowruzi, Farzan Erlik, Kolhatkar, Dhanvin, Kapoor, Prince, Heravi, Elnaz Jahani, Hassanat, Fahed Al, Laganiere, Robert, Rebut, Julien, and Malik, Waqas

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Camera and Lidar processing have been revolutionized with the rapid development of deep learning model architectures. Automotive radar is one of the crucial elements of automated driver assistance and autonomous driving systems. Radar still relies on traditional signal processing techniques, unlike camera and Lidar based methods. We believe this is the missing link to achieve the most robust perception system. Identifying drivable space and occupied space is the first step in any autonomous decision making task. Occupancy grid map representation of the environment is often used for this purpose. In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation. We explore various input-output representations. Our experiments show that PolarNet is a effective way to process radar data that achieves state-of-the-art performance and processing speeds while maintaining a compact size.

Published
2021

8. Deep Open Space Segmentation using Automotive Radar

Author

Nowruzi, Farzan Erlik, Kolhatkar, Dhanvin, Kapoor, Prince, Hassanat, Fahed Al, Heravi, Elnaz Jahani, Laganiere, Robert, Rebut, Julien, and Malik, Waqas

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios. A publically available dataset of radar observations called SCORP was collected. Deep models are evaluated with various radar input representations. Our proposed approach achieves low memory usage and real-time processing speeds, and is thus very well suited for embedded deployment., Comment: IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2020)

Published
2020

9. A Curious Link Between Prime Numbers, the Maundy Cake Problem and Parallel Sorting

Author

Blanchette, Jonathan and Laganière, Robert

Subjects
Computer Science - Data Structures and Algorithms, Computer Science - Computational Complexity, Mathematics - Number Theory
Abstract

We present new theoretical algorithms that sums the n-ary comparators output in order to get the permutation indices in order to sort a sequence. By analysing the parallel ranking algorithm, we found that the special comparators number of elements it processes divide the number of elements to be sorted. Using the divide and conquer method, we can express the sorting problem into summing output of comparators taking a prime number of elements, given that this prime number divides the initial disordered sequence length. The number of sums is directly related to the Maundy cake problem. Furthermore, we provide a new sequence that counts the number of comparators used in the algorithms., Comment: 10 pages, 2 figures, 3 tables, final version

Published
2019

10. How much real data do we actually need: Analyzing object detection performance using synthetic and real data

Author

Nowruzi, Farzan Erlik, Kapoor, Prince, Kolhatkar, Dhanvin, Hassanat, Fahed Al, Laganiere, Robert, and Rebut, Julien

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision. By nature, the supervised training of deep models requires a large amount of data to be available. This ideal case is usually not tractable as the data annotation is a tremendously exhausting and costly task to perform. An alternative is to use synthetic data. In this paper, we take a comprehensive look into the effects of replacing real data with synthetic data. We further analyze the effects of having a limited amount of real data. We use multiple synthetic and real datasets along with a simulation tool to create large amounts of cheaply annotated synthetic data. We analyze the domain similarity of each of these datasets. We provide insights about designing a methodological procedure for training deep networks using these datasets., Comment: Accepted in International Conference on Machine Learning (ICML 2019) Workshop on AI for Autonomous Driving

Published
2019

11. Sensor Fusion Operators for Multimodal 2D Object Detection

Author

Pasandi, Morteza Mousa, Liu, Tianran, Massoud, Yahya, Laganière, Robert, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bebis, George, editor, Li, Bo, editor, Yao, Angela, editor, Liu, Yang, editor, Duan, Ye, editor, Lau, Manfred, editor, Khadka, Rajiv, editor, Crisan, Ana, editor, and Chang, Remco, editor

Published
2022
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13. Application of DenseNet in Camera Model Identification and Post-processing Detection

Author

Rafi, Abdul Muntakim, Kamal, Uday, Hoque, Rakibul, Abrar, Abid, Das, Sowmitra, Laganière, Robert, and Hasan, Md. Kamrul

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

Camera model identification has earned paramount importance in the field of image forensics with an upsurge of digitally altered images which are constantly being shared through websites, media, and social applications. But, the task of identification becomes quite challenging if metadata are absent from the image and/or if the image has been post-processed. In this paper, we present a DenseNet pipeline to solve the problem of identifying the source camera-model of an image. Our approach is to extract patches of 256*256 from a labeled image dataset and apply augmentations, i.e., Empirical Mode Decomposition (EMD). We use this extended dataset to train a Neural Network with the DenseNet-201 architecture. We concatenate the output features for 3 different sizes (64*64, 128*128, 256*256) and pass them to a secondary network to make the final prediction. This strategy proves to be very robust for identifying the source camera model, even when the original image is post-processed. Our model has been trained and tested on the Forensic Camera-Model Identification Dataset provided for the IEEE Signal Processing (SP) Cup 2018. During testing we achieved an overall accuracy of 98.37%, which is the current state-of-the-art on this dataset using a single model. We used transfer learning and tested our model on the Dresden Database for Camera Model Identification, with an overall test accuracy of over 99% for 19 models. In addition, we demonstrate that the proposed pipeline is suitable for other image-forensic classification tasks, such as, detecting the type of post-processing applied to an image with an accuracy of 96.66% -- which indicates the generality of our approach.

Published
2018

14. Multi-scale prediction for robust hand detection and classification

Author

Lu, Ding, Wang, Yong, Laganiere, Robert, Luo, Xinbin, and Fu, Shan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we present a multi-scale Fully Convolutional Networks (MSP-RFCN) to robustly detect and classify human hands under various challenging conditions. In our approach, the input image is passed through the proposed network to generate score maps, based on multi-scale predictions. The network has been specifically designed to deal with small objects. It uses an architecture based on region proposals generated at multiple scales. Our method is evaluated on challenging hand datasets, namely the Vision for Intelligent Vehicles and Applications (VIVA) Challenge and the Oxford hand dataset. It is compared against recent hand detection algorithms. The experimental results demonstrate that our proposed method achieves state-of-the-art detection for hands of various sizes.

Published
2018

17. A Scale-Aware YOLO Model for Pedestrian Detection

Author

Yang, Xingyi, Wang, Yong, Laganière, Robert, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bebis, George, editor, Yin, Zhaozheng, editor, Kim, Edward, editor, Bender, Jan, editor, Subr, Kartic, editor, Kwon, Bum Chul, editor, Zhao, Jian, editor, Kalkofen, Denis, editor, and Baciu, George, editor

Published
2020
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18. FA3D: Fast and Accurate 3D Object Detection

Author

Demilew, Selameab S., Aghdam, Hamed H., Laganière, Robert, Petriu, Emil M., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bebis, George, editor, Yin, Zhaozheng, editor, Kim, Edward, editor, Bender, Jan, editor, Subr, Kartic, editor, Kwon, Bum Chul, editor, Zhao, Jian, editor, Kalkofen, Denis, editor, and Baciu, George, editor

Published
2020
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19. A Deep Neural Network for Counting Vessels in Sonar Signals

Author

Aghdam, Hamed H., Bouchard, Martin, Laganiere, Robert, Petriu, Emil M., Wort, Philip, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goutte, Cyril, editor, and Zhu, Xiaodan, editor

Published
2020
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22. Gradient Boundary Histograms for Action Recognition

Author

Shi, Feng, Laganiere, Robert, and Petriu, Emil

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper introduces a high efficient local spatiotemporal descriptor, called gradient boundary histograms (GBH). The proposed GBH descriptor is built on simple spatio-temporal gradients, which are fast to compute. We demonstrate that it can better represent local structure and motion than other gradient-based descriptors, and significantly outperforms them on large realistic datasets. A comprehensive evaluation shows that the recognition accuracy is preserved while the spatial resolution is greatly reduced, which yields both high efficiency and low memory usage.

Published
2014

23. VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results

Author

Zhu, Pengfei, Wen, Longyin, Du, Dawei, Bian, Xiao, Ling, Haibin, Hu, Qinghua, Nie, Qinqin, Cheng, Hao, Liu, Chenfeng, Liu, Xiaoyu, Ma, Wenya, Wu, Haotian, Wang, Lianjie, Schumann, Arne, Brown, Chase, Qian, Chen, Li, Chengzheng, Li, Dongdong, Michail, Emmanouil, Zhang, Fan, Ni, Feng, Zhu, Feng, Wang, Guanghui, Zhang, Haipeng, Deng, Han, Liu, Hao, Wang, Haoran, Qiu, Heqian, Qi, Honggang, Shi, Honghui, Li, Hongliang, Xu, Hongyu, Lin, Hu, Kompatsiaris, Ioannis, Cheng, Jian, Wang, Jianqiang, Yang, Jianxiu, Zhou, Jingkai, Zhao, Juanping, Joseph, K. J., Duan, Kaiwen, Suresh, Karthik, Ke, Bo, Wang, Ke, Avgerinakis, Konstantinos, Sommer, Lars, Zhang, Lei, Yang, Li, Cheng, Lin, Ma, Lin, Lu, Liyu, Ding, Lu, Huang, Minyu, Vedurupaka, Naveen Kumar, Mamgain, Nehal, Bansal, Nitin, Acatay, Oliver, Giannakeris, Panagiotis, Wang, Qian, Zhao, Qijie, Huang, Qingming, Liu, Qiong, Cheng, Qishang, Sun, Qiuchen, Laganière, Robert, Jiang, Sheng, Wang, Shengjin, Wei, Shubo, Wang, Siwei, Vrochidis, Stefanos, Wang, Sujuan, Lee, Tiaojio, Sajid, Usman, Balasubramanian, Vineeth N., Li, Wei, Zhang, Wei, Wu, Weikun, Ma, Wenchi, He, Wenrui, Yang, Wenzhe, Chen, Xiaoyu, Sun, Xin, Luo, Xinbin, Lian, Xintao, Li, Xiufang, Kuai, Yangliu, Li, Yali, Luo, Yi, Zhang, Yifan, Liu, Yiling, Li, Ying, Wang, Yong, Wang, Yongtao, Wu, Yuanwei, Fan, Yue, Wei, Yunchao, Zhang, Yuqin, Wang, Zexin, Wang, Zhangyang, Xia, Zhaoyue, Cui, Zhen, He, Zhenwei, Deng, Zhipeng, Guo, Zhiyao, Song, Zichen, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Leal-Taixé, Laura, editor, and Roth, Stefan, editor

Published
2019
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24. VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results

Author

Wen, Longyin, Zhu, Pengfei, Du, Dawei, Bian, Xiao, Ling, Haibin, Hu, Qinghua, Liu, Chenfeng, Cheng, Hao, Liu, Xiaoyu, Ma, Wenya, Nie, Qinqin, Wu, Haotian, Wang, Lianjie, Perera, Asanka G., Zhang, Baochang, Heo, Byeongho, Liu, Chunlei, Li, Dongdong, Michail, Emmanouil, Chen, Hanlin, Liu, Hao, Li, Haojie, Kompatsiaris, Ioannis, Cheng, Jian, Fan, Jiaqing, Zhang, Jie, Choi, Jin Young, Li, Jing, Yang, Jinyu, Choi, Jongwon, Zhao, Juanping, Han, Jungong, Zhang, Kaihua, Duan, Kaiwen, Song, Ke, Avgerinakis, Konstantinos, Lee, Kyuewang, Ding, Lu, Lauer, Martin, Giannakeris, Panagiotis, Zhang, Peizhen, Wang, Qiang, Xu, Qianqian, Huang, Qingming, Liu, Qingshan, Laganière, Robert, Zhang, Ruixin, Yun, Sangdoo, Zhu, Shengyin, Wu, Sihang, Vrochidis, Stefanos, Tian, Wei, Zhang, Wei, Chen, Weidong, Hu, Weiming, Wang, Wenhao, Zhang, Wenhua, Ding, Wenrui, He, Xiaohao, Li, Xiaotong, Zhang, Xin, Luo, Xinbin, Hu, Xixi, Meng, Yang, Kuai, Yangliu, Zhao, Yanyun, Li, Yaxuan, Yang, Yifan, Zhang, Yifan, Wang, Yong, Qi, Yuankai, Deng, Zhipeng, He, Zhiqun, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Leal-Taixé, Laura, editor, and Roth, Stefan, editor

Published
2019
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26. Deep Convolutional Correlation Filters for Forward-Backward Visual Tracking

Author

Wang, Yong, Laganière, Robert, Laroche, Daniel, Ors, Ali Osman, Xu, Xiaoyin, Zhu, Changyun, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Turek, Matt, editor, Ramalingam, Srikumar, editor, Xu, Kai, editor, Lin, Stephen, editor, Alsallakh, Bilal, editor, Yang, Jing, editor, Cuervo, Eduardo, editor, and Ventura, Jonathan, editor

Published
2018
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27. Scale-Aware RPN for Vehicle Detection

Author

Ding, Lu, Wang, Yong, Laganière, Robert, Luo, Xinbin, Fu, Shan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Turek, Matt, editor, Ramalingam, Srikumar, editor, Xu, Kai, editor, Lin, Stephen, editor, Alsallakh, Bilal, editor, Yang, Jing, editor, Cuervo, Eduardo, editor, and Ventura, Jonathan, editor

Published
2018
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30. Creating Immersive Virtual Reality Scenes Using a Single RGB-D Camera

Author

Lai, Po Kong, Laganière, Robert, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Karray, Fakhri, editor, Campilho, Aurélio, editor, and Cheriet, Farida, editor

Published
2017
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32. The Visual Object Tracking VOT2016 Challenge Results

Author

Kristan, Matej, Leonardis, Aleš, Matas, Jiři, Felsberg, Michael, Pflugfelder, Roman, Čehovin, Luka, Vojír̃, Tomáš, Häger, Gustav, Lukežič, Alan, Fernández, Gustavo, Gupta, Abhinav, Petrosino, Alfredo, Memarmoghadam, Alireza, Garcia-Martin, Alvaro, Solís Montero, Andrés, Vedaldi, Andrea, Robinson, Andreas, Ma, Andy J., Varfolomieiev, Anton, Alatan, Aydin, Erdem, Aykut, Ghanem, Bernard, Liu, Bin, Han, Bohyung, Martinez, Brais, Chang, Chang-Ming, Xu, Changsheng, Sun, Chong, Kim, Daijin, Chen, Dapeng, Du, Dawei, Mishra, Deepak, Yeung, Dit-Yan, Gundogdu, Erhan, Erdem, Erkut, Khan, Fahad, Porikli, Fatih, Zhao, Fei, Bunyak, Filiz, Battistone, Francesco, Zhu, Gao, Roffo, Giorgio, Subrahmanyam, Gorthi R. K. Sai, Bastos, Guilherme, Seetharaman, Guna, Medeiros, Henry, Li, Hongdong, Qi, Honggang, Bischof, Horst, Possegger, Horst, Lu, Huchuan, Lee, Hyemin, Nam, Hyeonseob, Chang, Hyung Jin, Drummond, Isabela, Valmadre, Jack, Jeong, Jae-chan, Cho, Jae-il, Lee, Jae-Yeong, Zhu, Jianke, Feng, Jiayi, Gao, Jin, Choi, Jin Young, Xiao, Jingjing, Kim, Ji-Wan, Jeong, Jiyeoup, Henriques, João F., Lang, Jochen, Choi, Jongwon, Martinez, Jose M., Xing, Junliang, Gao, Junyu, Palaniappan, Kannappan, Lebeda, Karel, Gao, Ke, Mikolajczyk, Krystian, Qin, Lei, Wang, Lijun, Wen, Longyin, Bertinetto, Luca, Rapuru, Madan Kumar, Poostchi, Mahdieh, Maresca, Mario, Danelljan, Martin, Mueller, Matthias, Zhang, Mengdan, Arens, Michael, Valstar, Michel, Tang, Ming, Baek, Mooyeol, Khan, Muhammad Haris, Wang, Naiyan, Fan, Nana, Al-Shakarji, Noor, Miksik, Ondrej, Akin, Osman, Moallem, Payman, Senna, Pedro, Torr, Philip H. S., Yuen, Pong C., Huang, Qingming, Martin-Nieto, Rafael, Pelapur, Rengarajan, Bowden, Richard, Laganière, Robert, Stolkin, Rustam, Walsh, Ryan, Krah, Sebastian B., Li, Shengkun, Zhang, Shengping, Yao, Shizeng, Hadfield, Simon, Melzi, Simone, Lyu, Siwei, Li, Siyi, Becker, Stefan, Golodetz, Stuart, Kakanuru, Sumithra, Choi, Sunglok, Hu, Tao, Mauthner, Thomas, Zhang, Tianzhu, Pridmore, Tony, Santopietro, Vincenzo, Hu, Weiming, Li, Wenbo, Hübner, Wolfgang, Lan, Xiangyuan, Wang, Xiaomeng, Li, Xin, Li, Yang, Demiris, Yiannis, Wang, Yifan, Qi, Yuankai, Yuan, Zejian, Cai, Zexiong, Xu, Zhan, He, Zhenyu, Chi, Zhizhen, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hua, Gang, editor, and Jégou, Hervé, editor

Published
2016
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33. The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results

Author

Felsberg, Michael, Kristan, Matej, Matas, Jiři, Leonardis, Aleš, Pflugfelder, Roman, Häger, Gustav, Berg, Amanda, Eldesokey, Abdelrahman, Ahlberg, Jörgen, Čehovin, Luka, Vojír̃, Tomáš, Lukežič, Alan, Fernández, Gustavo, Petrosino, Alfredo, Garcia-Martin, Alvaro, Montero, Andrés Solís, Varfolomieiev, Anton, Erdem, Aykut, Han, Bohyung, Chang, Chang-Ming, Du, Dawei, Erdem, Erkut, Khan, Fahad Shahbaz, Porikli, Fatih, Zhao, Fei, Bunyak, Filiz, Battistone, Francesco, Zhu, Gao, Seetharaman, Guna, Li, Hongdong, Qi, Honggang, Bischof, Horst, Possegger, Horst, Nam, Hyeonseob, Valmadre, Jack, Zhu, Jianke, Feng, Jiayi, Lang, Jochen, Martinez, Jose M., Palaniappan, Kannappan, Lebeda, Karel, Gao, Ke, Mikolajczyk, Krystian, Wen, Longyin, Bertinetto, Luca, Poostchi, Mahdieh, Maresca, Mario, Danelljan, Martin, Arens, Michael, Tang, Ming, Baek, Mooyeol, Fan, Nana, Al-Shakarji, Noor, Miksik, Ondrej, Akin, Osman, Torr, Philip H. S., Huang, Qingming, Martin-Nieto, Rafael, Pelapur, Rengarajan, Bowden, Richard, Laganière, Robert, Krah, Sebastian B., Li, Shengkun, Yao, Shizeng, Hadfield, Simon, Lyu, Siwei, Becker, Stefan, Golodetz, Stuart, Hu, Tao, Mauthner, Thomas, Santopietro, Vincenzo, Li, Wenbo, Hübner, Wolfgang, Li, Xin, Li, Yang, Xu, Zhan, He, Zhenyu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hua, Gang, editor, and Jégou, Hervé, editor

Published
2016
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37. Training Binary Descriptors for Improved Robustness and Efficiency in Real-Time Matching

Author

Akhoury, Sharat Saurabh, Laganière, Robert, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Petrosino, Alfredo, editor

Published
2013
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38. A Visual Blindspot Monitoring System for Safe Lane Changes

Author

Saboune, Jamal, Arezoomand, Mehdi, Martel, Luc, Laganiere, Robert, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Maino, Giuseppe, editor, and Foresti, Gian Luca, editor

Published
2011
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40. Automatic Text Segmentation for Movie Subtitles

Author

Scaiano, Martin, Inkpen, Diana, Laganière, Robert, Reinhartz, Adele, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Farzindar, Atefeh, editor, and Kešelj, Vlado, editor

Published
2010
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42. Feature Based Cut Detection with Automatic Threshold Selection

Author

Whitehead, Anthony, Bose, Prosenjit, Laganiere, Robert, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Enser, Peter, editor, Kompatsiaris, Yiannis, editor, O’Connor, Noel E., editor, Smeaton, Alan F., editor, and Smeulders, Arnold W. M., editor

Published
2004
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44. A 3D interpretation system based on consistent labeling of a set of propositions. Application to the interpretation of straight line correspondences

Author

Laganière, Robert, Mitiche, Amar, Goos, G., editor, Hartmanis, J., editor, Barstow, D., editor, Brauer, W., editor, Brinch Hansen, P., editor, Gries, D., editor, Luckham, D., editor, Moler, C., editor, Pnueli, A., editor, Seegmüller, G., editor, Stoer, J., editor, Wirth, N., editor, and Faugeras, O., editor

Published
1990
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48. Confidence-based Local Feature Selection for Material Classification

Author

Muselet Damien, Xu Sixiang, Tremeau Alain, and Laganiere Robert

Subjects
Contextual image classification, Computer science, business.industry, Feature vector, 05 social sciences, Pooling, Feature extraction, Pattern recognition, Feature selection, 010501 environmental sciences, Overfitting, 01 natural sciences, Convolutional neural network, 050105 experimental psychology, Field (computer science), 0501 psychology and cognitive sciences, Artificial intelligence, business, 0105 earth and related environmental sciences
Abstract

With the rise of Convolutional Neural Network (CNN) in the recent years, image classification has shown outstanding performances in the computer vision field. Many wellknown state of the art’s CNN architectures such as the ResNet family are applying a Global Average Pooling (GAP) to reduce the number of parameters of the fully connected layers. Most of the time, this pooling operation helps to prevent overfitting but we claim that it has a serious weakness for specific images where small details are crucial to predict their category, such as material images. In this case, the details are lost in the global average, providing non accurate global features. In this paper, we propose to select the most important local features before applying the GAP. In this aim, we add a branch in the classification network that predicts the confidence the network should have in each local feature vector. The less confident features are filtered out before applying the GAP. Experimental results on three datasets show that our approach outperforms recent alternatives in terms of classification accuracy and output probability calibration.

Published
2020

50. Joint multiregion segmentation and parametric estimation of image motion by basis function representation and level set evolution

Author

Vazquez, Carlos, Mitiche, Amar, and Laganiere, Robert

Subjects
Image processing -- Analysis, Estimation theory -- Analysis, Spatial analysis (Statistics)
Abstract

The purpose of this study is to investigate a variational method for joint segmentation and parametric estimation of image motion by basis function representation of motion and level set evolution. The functional contains three terms. One term is of classic regularization to bias the solution toward a segmentation with smooth boundaries. A second term biases the solution toward a segmentation with boundaries which coincide with motion discontinuities, following a description of motion discontinuities by a function of the image spatio-temporal variations. The third term refers to region information and measures conformity of the parametric representation of the motion of each region of segmentation to the image spatio-temporal variations. The components of motion in each region of segmentation are represented as functions in a space generated by a set of basis functions. The coefficients of the motion components considered combinations of the basis functions are the parameters of representation. The necessary conditions for a minimum of the functional, which are derived taking into consideration the dependence of the motion parameters on segmentation, lead to an algorithm which condenses to concurrent curve evolution, implemented via level sets, and estimation of the parameters by least squares within each region of segmentation. The algorithm and its implementation are verified on synthetic and real images using a basis of cosine transforms. Index Terms--Motion estimation, motion segmentation, basis function representation of motion, parametric motion model, curve evolution, level sets.

Published
2006

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