Image analysis and computer vision: 1990

doi:10.1016/1049-9660(91)90020-P

CVGIP: Image Understanding

Volume 53, Issue 3, May 1991, Pages 322-365

CVGIP: Image Underst...

https://doi.org/10.1016/1049-9660(91)90020-P Get rights and content

Abstract

This paper presents a bibliography of over 1600 references related to computer vision and image analysis, arranged by subject matter. The topics covered include architectures; computational techniques; feature detection, segmentation, and image analysis; matching, stereo, and time-varying imagery; shape and pattern; color and texture; and three-dimensional scene analysis. A few references are also given on related topics, such as computational geometry, computer graphics, image input/output and coding, image processing, optical processing, visual perception, neural nets, pattern recognition, and artificial intelligence.

References (54)

K. Kanatani

Cited by (11)

Blind quality-based pairwise ranking of contrast changed color images using deep networks
2024, Signal Processing: Image Communication
Next-generation multimedia networks are expected to provide systems and applications with top Quality of Experience (QoE) to users. To this end, robust quality evaluation metrics are critical. Unfortunately, most current research focuses only on modeling and evaluating mainly distortions across the pipeline of multimedia networks. While distortions are important, it is also as important to consider the effects of enhancement and other manipulations of multimedia content, especially images and videos. In contrast to most existing works dedicated to evaluating image/video quality in its traditional context, very few research efforts have been devoted to Image Quality Enhancement Assessment (IQEA) and more specifically, Contrast Enhancement Evaluation (CEE). Our contribution fills this gap by proposing a pairwise ranking scheme for estimating and evaluating the perceptual quality of image contrast change (contrast enhancement and/or contrast-distorted images) process. We propose a novel Deep Learning-based Blind Quality pairwise Ranking scheme for Contrast-Changed (Deep-BQRCC) images. This method provides an automatic pairwise ranking of a set of contrast-changed images. The proposed framework is based on using a pair of Convolutional Neural Networks (CNN) together with a saliency-based attention model and a color-difference visual map. Extensive experiments were conducted to validate the effectiveness of the proposed workflow through an ablation analysis. Different combinations of CNN models and pooling strategies were analyzed. The proposed Deep-BQRCC approach was evaluated over three dedicated publicly available datasets. The experimental results showed an increase in performance within a range of $3 – 10$ % compared to state-of-the-art IQEA measures.
Wavelet transforms on two-dimensional images
2002, Mathematical and Computer Modelling
This paper studies the two-dimensional wavelet transform applied to two-dimensional images. The classical technique oftentimes implements the Fourier transform. This paper offers a brief discussion regarding the comparison of the two transforms on a single alphabet. It provides a comparison of the global properties present in the Fourier transform technique versus a more localized analysis when the wavelet transform is applied to the same image. The wavelet selected in this study is the derivative of the Gaussian since in some sense offers a nice comparison to the Fourier method.
A retrospective on real-time imaging, a new taxonomy and a roadmap for the future
2002, Real-Time Imaging
In this paper, the history and current state of research in real-time imaging is discussed and a review of previous taxonomies for classifying real-time imaging research is presented. Next, a new taxonomy is proposed followed by a review of the state of research with respect to that taxonomy. Finally, future research in these areas is suggested. The objective is to provide a clear vision of the past and a roadmap for future researchers in this field.
Classifying the literature related to computer vision and image analysis
2000, Computer Vision and Image Understanding
Publications related to computer vision and image analysis can be classified by subject and type in various ways. A publication often needs to be assigned to multiple subject classes. In most cases, key words that occur in the title of a publication can be used to assign it to appropriate classes. It should be possible to semiautomatically compile bibliographies of the current literature by collecting references from existing databases or publishers' web pages, standardizing their format, and classifying them using key words, but to ensure that the resulting bibliography is of good stylistic quality, manual postediting will be necessary.
Image analysis and computer vision: 1991
1992, CVGIP: Image Understanding
This paper presents a bibliography of nearly 1200 references related to computer vision and image analysis, arranged by subject matter. The topics covered include architectures; computational techniques; feature detection and segmentation; image analysis; two-dimensional shape; pattern; color and texture; matching and stereo; three-dimensional recovery and analysis; three-dimensional shape; and motion. A few references are also given on related topics, such as geometry, graphics, image input/output and coding, image processing, optical processing, visual perception, neural nets, pattern recognition and artificial intelligence, as well as on applications.
Principles of Electron Optics, Volume 4: Advanced Wave Optics
2022, Principles of Electron Optics, Volume 4: Advanced Wave Optics

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Image analysis and computer vision: 1990