We introduce a method for unsupervised clustering of images of 3D objects. Our method examines the space of all images and partitions the images into sets that form smooth and parallel surfaces in this space. Ii furth...
详细信息
ISBN:
(纸本)0818684976
We introduce a method for unsupervised clustering of images of 3D objects. Our method examines the space of all images and partitions the images into sets that form smooth and parallel surfaces in this space. Ii further uses sequences of images to obtain more reliable clustering. Finally, since our method relies on a non-Euclidean similarity measure we introduce algebraic techniques for estimating local properties of these surfaces without first embedding the images in a Euclidean space. We demonstrate our method by applying it to a large database of images.
Action recognition in still images is closely related to various other computervision tasks such as pose estimation, object recognition, image retrieval, video action recognition and frame tagging in videos. This pro...
详细信息
ISBN:
(纸本)9781728193601
Action recognition in still images is closely related to various other computervision tasks such as pose estimation, object recognition, image retrieval, video action recognition and frame tagging in videos. This problem is focused on recognizing a person's action or behavior using a single frame. Unlike action recognition in videos - a relatively very well established area of research where spatio-temporal features are used, these are not available for still images, making the problem more challenging. In the present work only actions that involve objects are considered. A complex action is broken down into components based on semantics. The importance of each of these components in action recognition is systematically studied.
Contextual information can greatly improve both the speed and accuracy of object recognition. Context is most often viewed as a static concept, learned from large image databases. We build upon this concept by explori...
详细信息
ISBN:
(纸本)9781479943098
Contextual information can greatly improve both the speed and accuracy of object recognition. Context is most often viewed as a static concept, learned from large image databases. We build upon this concept by exploring cognitive context, demonstrating how rich dynamic context provided by computational cognitive models can improve object recognition. We demonstrate the use cognitive context to improve recognition using a small database of objects.
Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computervision applications aimed at making high stakes de...
详细信息
ISBN:
(纸本)9781665448994
Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computervision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computervision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computervision applications and recommendations.
We present WiCV 2018 - Women in computervision Workshop to increase the visibility and inclusion of women researchers in computervision field, organized in conjunction with cvpr 2018. computervision and machine lea...
详细信息
ISBN:
(数字)9781538661000
ISBN:
(纸本)9781538661000
We present WiCV 2018 - Women in computervision Workshop to increase the visibility and inclusion of women researchers in computervision field, organized in conjunction with cvpr 2018. computervision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration, and to provide mentorship and give opportunities to female-identifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
Symmetry is a pervasive phenomenon presenting itself in all forms and scales in natural and manmade environments. Its detection plays an essential role at all levels of human as well as machine perception. The recent ...
详细信息
ISBN:
(纸本)9780769549903
Symmetry is a pervasive phenomenon presenting itself in all forms and scales in natural and manmade environments. Its detection plays an essential role at all levels of human as well as machine perception. The recent resurging interest in computational symmetry for computervision and computer graphics applications has motivated us to conduct a US NSF funded symmetry detection algorithm competition as a workshop affiliated with the computervision and patternrecognition (cvpr) conference, 2013. This competition sets a more complete benchmark for computervision symmetry detection algorithms. In this report we explain the evaluation metric and the automatic execution of the evaluation workflow. We also present and analyze the algorithms submitted, and show their results on three test sets of real world images depicting reflection, rotation and translation symmetries respectively. This competition establishes a performance baseline for future work on symmetry detection.
This paper reports novel algorithms for the efficient localisation and recognition of vehicles in traffic scenes, which eliminate the need for explicit symbolic feature extraction and matching. The algorithms make use...
详细信息
ISBN:
(纸本)0818672587
This paper reports novel algorithms for the efficient localisation and recognition of vehicles in traffic scenes, which eliminate the need for explicit symbolic feature extraction and matching. The algorithms make use of two a priori sources of knowledge about the scene and the objects: (i) the ground-plane constraint, and (ii) the fact that road vehicles are strongly rectilinear. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles demonstrates the efficiency and robustness of context-based computervision in road traffic scenes. The limitations of the algorithms are also addressed in the paper.
Pain is a critical sign in many medical situations and its automatic detection and recognition using computervision techniques is of great importance. Utilizes this fact that pain is a spatiotemporal process, the pro...
详细信息
ISBN:
(纸本)9781467367592
Pain is a critical sign in many medical situations and its automatic detection and recognition using computervision techniques is of great importance. Utilizes this fact that pain is a spatiotemporal process, the proposed system in this paper employs steerable and separable filters to measures energies released by the facial muscles during the pain process. The proposed system not only detects the pain but recognizes its level. Experimental results on the publicly available pain database of UNBC show promising outcome for automatic pain detection and recognition.
This paper introduces the Neurodata Lab's approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within cvpr2020. The RePSS challenge was focused on measuring the average ...
详细信息
ISBN:
(纸本)9781728193601
This paper introduces the Neurodata Lab's approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within cvpr2020. The RePSS challenge was focused on measuring the average heart rate from color facial videos, which is one of the most fundamental problems in the field of computervision. Our deep learning-based approach includes 3D spatio-temporal attention convolutional neural network for photoplethysmogram extraction and 1D convolutional neural network pre-trained on synthetic data for time series analysis. It provides state-of-the-art results outperforming those of other participants on a mixture of VIPL and OBF databases: MAE=6.94 (12.3% improvement compared to the top-2 result), RMSE=10.68 (24.6% improvement), Pearson R = 0.755 (28.2% improvement).
暂无评论