Computer Vision: A Modern Approach

By D.A. Forsyth, Jean Ponce. 2002

4,443 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation (2021, background): “A central principle in image processing, signal processing, graphics, and vision [24, 25, 27, 53, 70] is to blur or “prefilter” before subsampling, as a means of removing highfrequency information (thus preventing its misrepresentation downstream).”
Is data clustering in adversarial settings secure? (2013, background): “Applications of clustering span across a large number of different domains, such as market segmentation [14, 26], classification of web pages [10], and image segmentation [12].”
An empirical study on evaluation metrics of generative adversarial networks (2018, background): “It is well-established that pixel representations of images do not induce meaningful Euclidean distances (Forsyth & Ponce, 2011).”
Image Segmentation Using Deep Learning: A Survey (2020, background): “and plays a central role in a broad range of applications [3] (Part VI), including medical image analysis (e.”
Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study (2007, background): “after 40 photographs. Therefore each flocking event lasts at most 8 seconds. Stereo Matching. After subtraction of the background, a segmentation algorithm locates birds’ positions on the photographs (34). To perform the 3D reconstruction, each bird’s image on the left photo must be matched to its c”
A comprehensive survey of recent trends in deep learning for digital images augmentation (2021, background): “DL is the secret to computer vision (Ponce and Forsyth 2012), image classifications, object recognition (Papageorgiou et al. 1998), image segmentation (Pal and Pal 1993), and more.”
mm-Pose: Real-Time Human Skeletal Posture Estimation Using mmWave Radars and CNNs (2019, neutral): “W ITH the advent in computing resources and advanced machine learning (ML) techniques, computer vision (CV) has emerged as an exciting ﬁeld of research to provide Artiﬁcal Intelligence (AI) and autonomous machines with information about the visual representation of the real world [1], [2].”
PIP: Physical Interaction Prediction via Mental Imagery with Span Selection (2021, background): “*These authors contributed equally to this work With the rapid advancements in computer vision, deep learning and embodied AI [6, 7, 8], there is an increase in intuitive physics models that aim to predict physical interaction outcomes.”
Boundary Extraction in Natural Images Using Ultrametric Contour Maps (2006, background): “The ascending construction of a family of nested partitions is one of the earliest approaches to segmentation [10, 4].”
Perceiving, learning, and exploiting object affordances for autonomous pile manipulation (2013, background): “Segmentation algorithms [4], [5] process an image and divide it into spatially contiguous regions sharing a particular property.”
Review of Stereo Vision Algorithms: From Software to Hardware (2008, background): “Stereo vision is a flourishing field, attracting the attention of many researchers (Forsyth and Ponce 2002; Hartley and Zisserman 2004).”
Extracting lines of curvature from noisy point clouds (2009, methodology): “For a robust estimation, we use the Geman-McLure cost function (GM), which is a proven choice in computer vision (11) and”
Occlusion Aware Unsupervised Learning of Optical Flow (2017, neutral): “With the accurate optical flow prediction, one could estimate the 3D structure of a scene [18], segment moving objects based on motion cues [38], track objects in a complicated environment [11], and build important visual cues for many high level vision tasks such as video action recognition [45] an”
Sparse Modeling for Image and Vision Processing (2014, background): “Finally, von Helmholtz [1852], Grassmann [1854], and Maxwell [1860] proposed rigorous rules of color compositions, paving the way to the modern treatment of color in vision processing [see Forsyth and Ponce, 2012, Chapter 3].”
Convolutional Neural Network Architecture for Geometric Matching (2017, background): “Estimating correspondences between images is one of the fundamental problems in computer vision [20, 26] with applications ranging from large-scale 3D reconstruction [2] to image manipulation [22] and semantic segmentation [44].”