一个快速的人脸检测架构用于智能手机和数码相机的自动对焦

Ouyang, Peng; Yin, Shouyi; Deng, Chenchen; Liu, Leibo; Wei, Shaojun

doi:10.1007/s11432-015-5312-z

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…In recent years, research related to "humans" in the computer vision community has become increasingly active because of the high demand for real-life applications. There has been much good research in the fields of human pose estimation [1,2,6,14,20,26,40], pedestrian detection [25,41,42], portrait segmentation [35,36,37], and face recognition [18,23,24,27,39,43,44], much of which has already produced practical value in real life. This paper focuses on multi-person pose estimation and human instance segmentation, and proposes a pose-based human instance segmentation framework.…”

Section: Introductionmentioning

confidence: 99%

Pose2Seg: Detection Free Human Instance Segmentation

Zhang

Dong

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

170

119

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The standard approach to image instance segmentation is to perform the object detection first, and then segment the object from the detection bounding-box. More recently, deep learning methods like Mask R-CNN [14] perform them jointly. However, little research takes into account the uniqueness of the "human" category, which can be well defined by the pose skeleton. Moreover, the human pose skeleton can be used to better distinguish instances with heavy occlusion than using bounding-boxes. In this paper, we present a brand new pose-based instance segmentation framework 1 for humans which separates instances based on human pose, rather than proposal region detection. We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detectionbased approach on the human instance segmentation problem, and can moreover better handle occlusion. Furthermore, there are few public datasets containing many heavily occluded humans along with comprehensive annotations, which makes this a challenging problem seldom noticed by researchers. Therefore, in this paper we introduce a new benchmark "Occluded Human (OCHuman)" 2 , which focuses on occluded humans with comprehensive annotations including bounding-box, human pose and instance masks. This dataset contains 8110 detailed annotated human instances within 4731 images. With an average 0.67 Max-IoU for each person, OCHuman is the most complex and challenging dataset related to human instance segmentation. Through this dataset, we want to emphasize occlusion as a challenging problem for researchers to study.

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Section: Introductionmentioning

confidence: 99%

Pose2Seg: Detection Free Human Instance Segmentation

Zhang

Dong

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

170

119

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“…In an EOTS, in order to provide extremely accurate results within a short response time, one of the priorities is to have timely and accurate auto-focus technology (AFT) to capture the image information of the moving objects [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Accurate and Rapid Auto-Focus Methods Based on Image Quality Assessment for Telescope Observation

Yang

Chen

Zhou³

et al. 2020

Applied Sciences

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Aiming at improving the speed and accuracy of auto-focus for telescope observation, algorithms for image estimation and auto-focus were investigated and are discussed in this article. Based on the image quality assessment, the auto-focusing process of the telescope system is realized by using the mountain-climb search method. Several evaluation functions were tested in different scenarios. It is demonstrated that the Tenengrad image estimation function (IEF) is suitable for an instant and accurate auto-focus process of the telescope. Furthermore, we implemented sampling and dynamic adaptive focusing window (ES-DAFW) methods with the Tenengrad IEF to enhance the sensitivity and accuracy of the auto-focus process. The experimental results showed that our ES-DATW method can provide more accurate results in less time for the auto-focus process compared to the conventional approaches, especially for a sparse image. These results promise significant applications to the auto-focusing of other telescopes with image quality assessment.

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Human Instance Segmentation

Wang

2021

Communications in Computer and Information Science

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一个快速的人脸检测架构用于智能手机和数码相机的自动对焦

Abstract: Integrated lens auto-focus system driven by a nut-type ultrosonic motor (USM) Science in China Series E-Technological Sciences 52, 2591 (2009); An auto-focus algorithm for imaging of objects under a lossy earth from multi-frequency and multi-monostatic data

Cited by 4 publications

References 16 publications

Pose2Seg: Detection Free Human Instance Segmentation

Pose2Seg: Detection Free Human Instance Segmentation

Accurate and Rapid Auto-Focus Methods Based on Image Quality Assessment for Telescope Observation

Human Instance Segmentation

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