Electronics Invades Photography: Digital Cameras

Williams, J. B.

doi:10.1007/978-3-319-49088-5_26

Cited by 3 publications

(3 citation statements)

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“…In the pre-photography era, ecologists and evolutionary biologists used drawings to capture the shapes and patterns of life, later to be replaced by analog photography, which allowed for qualitative assessment and simple, often only qualitative analysis of phenotypic variation. With the advent of digital photography, biologists could collect phenotypic data at unprecedented rates using camera stands, camera traps, microscopes, scanners, video cameras, or any other instrument with semiconductor image sensors (Goesele, 2004;Williams, 2017). Image sensors produce two-dimensional raster images (also known as bitmap images), which store incoming visible light or other electromagnetic signals into discrete, locatable picture elements -in short: pixels (Figure 2; Fossum and Hondongwa, 2014).…”

Section: The Structure Of Digital Imagesmentioning

confidence: 99%

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

et al. 2021

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For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.

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Section: The Structure Of Digital Imagesmentioning

confidence: 99%

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

et al. 2021

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“…In the pre-photography era, ecologists and evolutionary biologists used drawings to capture the shapes and patterns of life, later to be replaced by analog photography, which allowed for qualitative assessment and simple, often only qualitative analysis of phenotypic variation. With the advent of digital photography, biologists could collect phenotypic data at unprecedented rates using camera stands, camera traps, microscopes, scanners, video cameras, or any other instrument with semiconductor image sensors (Goesele, 2004;Williams, 2017) . Image sensors produce two-dimensional raster images (also known as bitmap images), which store incoming visible light or other electromagnetic signals into discrete, locatable picture elements -in short: pixels (Fig.…”

Section: The Structure Of Digital Imagesmentioning

confidence: 99%

Computer vision, machine learning, and the promise of phenomics in ecology and evolutionary biology

Lürig¹,

Donoughe²,

Svensson³

et al. 2020

Preprint

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For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings, and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic trait diversity, population dynamics, mechanisms of divergence and adaptation and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from the images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics - the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, is a way to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV for fast, comprehensive, and reproducible image analysis in ecology and evolution. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can most effectively capture phenomic-level data by using CV. Next, we describe the primary types of image-based data, and review CV approaches for extracting them (including techniques that entail machine learning and others that do not). We identify common hurdles and pitfalls, and then highlight recent successful implementations of CV in the study of ecology and evolution. Finally, we outline promising future applications for CV in biology. We anticipate that CV will become a basic component of the biologist’s toolkit, further enhancing data quality and quantity, and sparking changes in how empirical ecological and evolutionary research will be conducted.

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“…Unlike analog video, digital video can be copied without generation loss and can easily be stored, shared, and streamed with unprecedented image quality and ease of editing. 9 With recent advances in electronics and the birth of the internet, SM, and high-bandwidth telecommunication networks, the capture, storage, and editing of digital video for use in the clinical practice of facial plastic surgery has suddenly become well within the reach of surgeons.…”

Section: Discussionmentioning

confidence: 99%

The Future of Visual Documentation? Assessing the Use of Videography in Facial Plastic Surgery

Soares

Yamamoto

Liebertz³

2022

Facial Plast Surg

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Background: Recent technological advancements in the field of portable electronics have facilitated the use of videography as a form of visual documentation in facial plastic surgery. Currently, the degree of video adoption and perceptions relating to its use in plastic surgery are not known. Objective: To evaluate the current use, perceptions, and barriers regarding the adoption of video in the clinical practice of facial plastic surgery. Methods: A cross-sectional study of all AAFPRS members was conducted through an email-disseminated 24-item online survey. Results: A total of 164 surgeons responded to the survey. Nearly all of surgeons reported routinely employing photography for the documentation and marketing of surgical results. Fewer than 25% of respondents acknowledged using video to document surgical outcomes. Younger surgeons (< 10 years in practice) and those in academic practices were significantly more likely to adopt videography (32% vs 17%, p = 0.042 and 38% vs 18%, p = 0.027, respectively). Most surgeons regarded video as the superior visual documentation format for dynamic facial expression and as more difficult to deceptively manipulate. Most frequently cited barriers to adoption included time-consuming capture, file editing/storage requirements, and lack of clear standards. Conclusions: Videography holds favorable potential as the future format of visual documentation in facial plastic surgery due to its ability to capture the full range of dynamic facial expression. Establishing standards and setup guidelines for video capture will be essential in increasing its adoption.

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Electronics Invades Photography: Digital Cameras

Cited by 3 publications

References 0 publications

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

Computer vision, machine learning, and the promise of phenomics in ecology and evolutionary biology

The Future of Visual Documentation? Assessing the Use of Videography in Facial Plastic Surgery

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