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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

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Facial Emotion recognition Using computer vision 1

Facial Emotion Recognition Using Computer Vision

引用

1st International conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Jonathan Lim, Andreas Pangestu Paoline Kusuma, Gede Putra Zahra, Amalia Bina Nusantara Univ Sch Comp Sci Dept Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Dept Comp Sci Comp Sci Jakarta 11480 Indonesia

ISBN: (纸本)9781538694220

This paper examines how human emotion, which is often expressed by face expression, could be recognized using computer vision. The study is performed by analyzing journals and researches related to this topic, ranging from psychological to technological journals. A number of algorithms and techniques have been reviewed, and at the end of this paper, a summary of recommendation for performing facial emotion recognition based on the reviews of the techniques/methods is given.

关键词： artificial intelligence computer vision facial emotion recognition

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Stratified approach to metric self-calibration

Stratified approach to metric self-calibration

引用

Proceedings of the 1997 ieee computer society conference on computer vision and pattern recognition

作者： Pollefeys, Marc Van Gool, Luc K.U. Leuven-MI2 Leuven Belgium

Camera calibration is essential to many computer vision applications. In practice this often requires cumbersome calibration procedures to be carried out regularly. In the last few years a lot of work has been done on self-calibration of cameras, ranging from weak calibration to metric calibration. It has been shown that a metric calibration of the camera setup (up to scale) was possible based on the rigidity of the scene only. In this paper a stratified approach is proposed which gradually retrieves the metric calibration of the camera setup. Starting from an uncalibrated image sequence the projective calibration is retrieved first. In projective space the plane at infinity is then identified yielding the affine calibration. This is achieved using a constraint which can be formulated between any two arbitrary images of the sequence. Once the affine calibration is known the upgrade to metric is easily obtained through linear equations.

关键词： computer vision

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Replacing Labeled Real-image Datasets with Auto-generated Contours

Replacing Labeled Real-image Datasets with Auto-generated Co...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kataoka, Hirokatsu Hayamizu, Ryo Yamada, Ryosuke Nakashima, Kodai Takashima, Sora Zhang, Xinyu Martinez-Noriega, Edgar Josafat Inoue, Nakamasa Yokota, Rio Natl Inst Adv Ind Sci & Technol Tokyo Japan Tokyo Inst Technol Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k without the use of real images, human-, and self-supervision during the pre-training of vision Transformers (ViTs). For example, ViT-Base pre-trained on ImageNet-21k shows 81.8% top-1 accuracy when fine-tuned on ImageNet-1k and FDSL shows 82.7% top-1 accuracy when pre-trained under the same conditions (number of images, hyperparameters, and number of epochs). Images generated by formulas avoid the privacy/copyright issues, labeling cost and errors, and biases that real images suffer from, and thus have tremendous potential for pre-training general models. To understand the performance of the synthetic images, we tested two hypotheses, namely (i) object contours are what matter in FDSL datasets and (ii) increased number of parameters to create labels affects performance improvement in FDSL pre-training. To test the former hypothesis, we constructed a dataset that consisted of simple object contour combinations. We found that this dataset can match the performance of fractals. For the latter hypothesis, we found that increasing the difficulty of the pre-training task generally leads to better fine-tuning accuracy.

关键词： computer vision Costs Shape Supervised learning Transformers Fractals pattern recognition

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New linear method for Euclidean motion/structure from three calibrated affine views

New linear method for Euclidean motion/structure from three ...

引用

Proceedings of the 1998 ieee computer society conference on computer vision and pattern recognition

作者： Quan, Long Ohta, Yuichi CNRS-GRAVIR-INRIA Montbonnot France

We introduce a unified framework for developing matching constraints of multiple affine views and rederive 2-view (affine epipolar geometry) and 3-view (affine image transfer) constraints within this framework. We then describe a new linear method for Euclidean motion and structure from 3 calibrated affine images, based on insight into the particular structure of these multiple-view constraints. Compared with the existing linear method of Huang and Lee [7], the new method uses different and more appropriate constraints. It has no failure mode of the Euclidean factorisation method of Tomasi and Kanade [18]. We demonstrate the method on real image sequences.

关键词： pattern matching

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Guided Proofreading of Automatic Segmentations for Connectomics 31

Guided Proofreading of Automatic Segmentations for Connectom...

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Haehn, Daniel Kaynig, Verena Tompkin, James Lichtman, Jeff W. Pfister, Hanspeter Harvard Univ Cambridge MA 02138 USA Brown Univ Providence RI 02912 USA

ISBN: (纸本)9781538664209

Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual search for these errors as the bottleneck in interactive proofreading. To aid error correction, we develop two classifiers that automatically recommend candidate merges and splits to the user. These classifiers use a convolutional neural network (CNN) that has been trained with errors in automatic segmentations against expert-labeled ground truth. Our classifiers detect potentially-erroneous regions by considering a large context region around a segmentation boundary. Corrections can then be performed by a user with yes/no decisions, which reduces variation of information 7.5x faster than previous proofreading methods. We also present a fully-automatic mode that uses a probability threshold to make merge/split decisions. Extensive experiments using the automatic approach and comparing performance of novice and expert users demonstrate that our method performs favorably against state-of-the-art proofreading methods on different connectomics datasets.

关键词： Image segmentation Three-dimensional displays Tools Error correction computer architecture Visualization Task analysis

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What's in your hands? 3D Reconstruction of Generic Objects in Hands

What's in your hands? 3D Reconstruction of Generic Objects i...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ye, Yufei Gupta, Abhinav Tulsiani, Shubham Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta AI Menlo Pk CA 94025 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Our work aims to reconstruct hand-held objects given a single RGB image. In contrast to prior works that typically assume known 3D templates and reduce the problem to 3D pose estimation, our work reconstructs generic handheld object without knowing their 3D templates. Our key insight is that hand articulation is highly predictive of the object shape, and we propose an approach that conditionally reconstructs the object based on the articulation and the visual input. Given an image depicting a hand-held object, we first use off-the-shelf systems to estimate the underlying hand pose and then infer the object shape in a normalized hand-centric coordinate frame. We parameterized the object by signed distance which are inferred by an implicit network which leverages the information from both visual feature and articulation-aware coordinates to process a query point. We perform experiments across three datasets and show that our method consistently outperforms baselines and is able to reconstruct a diverse set of objects. We analyze the benefits and robustness of explicit articulation conditioning and also show that this allows the hand pose estimation to further improve in test-time optimization.

关键词： Training Visualization Three-dimensional displays Shape Pose estimation Rendering (computer graphics) Robustness

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FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing

FLOAT: Factorized Learning of Object Attributes for Improved...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Singh, Rishubh Gupta, Pranav Shenoy, Pradeep Sarvadevabhatla, Ravikiran Google Res Mountain View CA 94043 USA IIIT Hyderabad Hyderabad Telangana India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multi-object multi-part scene parsing is a challenging task which requires detecting multiple object classes in a scene and segmenting the semantic parts within each object. In this paper, we propose FLOAT, a factorized label space framework for scalable multi-object multi-part parsing. Our framework involves independent dense prediction of object category and part attributes which increases scalability and reduces task complexity compared to the monolithic label space counterpart. In addition, we propose an inference-time 'zoom' refinement technique which significantly improves segmentation quality, especially for smaller objects/parts. Compared to state of the art, FLOAT obtains an absolute improvement of 2.0% for mean IOU (mIOU) and 4.8% for segmentation quality IOU (sqIOU) on the Pascal-Part-58 dataset. For the larger Pascal-Part-108 dataset, the improvements are 2.1% for mIOU and 3.9% for sqIOU. We incorporate previously excluded part attributes and other minor parts of the Pascal-Part dataset to create the most comprehensive and challenging version which we dub Pascal-Part-201. FLOAT obtains improvements of 8.6% for mIOU and 7.5% for sqIOU on the new dataset, demonstrating its parsing effectiveness across a challenging diversity of objects and parts. The code and datasets are available at ***.

关键词： computer vision Three-dimensional displays Codes Scalability Semantics Focusing Benchmark testing

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A Unified Query-based Paradigm for Point Cloud Understanding

A Unified Query-based Paradigm for Point Cloud Understanding

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Zetong Jiang, Li Sun, Yanan Schiele, Bernt Jia, Jiaya CUHK Hong Kong Peoples R China MPI Informat Saarbrucken Germany HKUST Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

3D point cloud understanding is an important component in autonomous driving and robotics. In this paper, we present a novel Embedding-Querying paradigm (EQ-Paradigm) for 3D understanding tasks including detection, segmentation and classification. EQ-Paradigm is a unified paradigm that enables combination of existing 3D backbone architectures with different task heads. Under the EQ-Paradigm, the input is first encoded in the embedding stage with an arbitrary feature extraction architecture, which is independent of tasks and heads. Then, the querying stage enables the encoded features for diverse task heads. This is achieved by introducing an intermediate representation, i.e., Q-representation, in the querying stage to bridge the embedding stage and task heads. We design a novel Q-Net as the querying stage network. Extensive experimental results on various 3D tasks show that EQ-Paradigm in tandem with Q-Net is a general and effective pipeline, which enables flexible collaboration of backbones and heads. It further boosts performance of state-of-the-art methods.

关键词： Point cloud compression Three-dimensional displays Shape Pipelines Semantics computer architecture Feature extraction

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FlowNet3D: Learning Scene Flow in 3D Point Clouds 32

FlowNet3D: Learning Scene Flow in 3D Point Clouds

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Xingyu Qi, Charles R. Guibas, Leonidas J. Stanford Univ Stanford CA 94305 USA Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781728132938

Many applications in robotics and human-computer interaction can benefit from understanding 3D motion of points in a dynamic environment, widely noted as scene flow. While most previous methods focus on stereo and RGB-D images as input, few try to estimate scene flow directly from point clouds. In this work, we propose a novel deep neural network named FlowNet3D that learns scene flow from point clouds in an end-to-end fashion. Our network simultaneously learns deep hierarchical features of point clouds and flow embeddings that represent point motions, supported by two newly proposed learning layers for point sets. We evaluate the network on both challenging synthetic data from FlyingThings3D and real Lidar scans from KITTI. Trained on synthetic data only, our network successfully generalizes to real scans, outperforming various baselines and showing competitive results to the prior art. We also demonstrate two applications of our scene flow output (scan registration and motion segmentation) to show its potential wide use cases.

关键词： Deep Learning Low-level vision Motion and Tracking Robotics + Driving

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PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

PiCIE: Unsupervised Semantic Segmentation using Invariance a...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cho, Jang Hyun Mall, Utkarsh Bala, Kavita Hariharan, Bharath Univ Texas Austin Austin TX 78712 USA Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and object-centric images yet real-world data are dominantly uncurated, multi-label, and scene-centric. We extend clustering from images to pixels and assign separate cluster membership to different instances within each image. However, solely relying on pixel-wise feature similarity fails to learn high-level semantic concepts and overfits to low-level visual cues. We propose a method to incorporate geometric consistency as an inductive bias to learn invariance and equivariance for photometric and geometric variations. With our novel learning objective, our framework can learn high-level semantic concepts. Our method, PiCIE (Pixel-level feature Clustering using Invariance and Equivariance), is the first method capable of segmenting both things and stuff categories without any hyperparameter tuning or task-specific pre-processing. Our method largely outperforms existing baselines on COCO [31] and Cityscapes [8] with +17.5 Acc. and +4.5 mIoU. We show that PiCIE gives a better initialization for standard supervised training.

关键词： Training Visualization Image segmentation computer vision Codes Clustering methods Semantics

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