Dataset Information

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review.

ABSTRACT: The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

SUBMITTER: Hwang SW

PROVIDER: S-EPMC8082842 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review.

Hwang Sung-Wook SW Sugiyama Junji J

Plant methods 20210428 1

The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is sti ...[more]

PMID: 33910606

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Project description:Children begin to develop self-awareness when they associate images and abilities with themselves. Such "construction of self" continues throughout adult life as we constantly cycle through different forms of self-awareness, seeking, to redefine ourselves. Modern technologies like screens and artificial intelligence threaten to alter our development of self-awareness, because children and adults are exposed to machines, tele-presences, and displays that increasingly become part of human identity. We use avatars, invent digital lives, and augment ourselves with digital imprints that depart from reality, making the development of self-identification adjust to digital technologies that blur the boundary between us and our devices. To empower children and adults to see themselves and artificially intelligent machines as separately aware entities, we created the persona of a salvaged supermarket security camera refurbished and enhanced with the power of computer vision to detect human faces, and project them on a large-scale 3D face sculpture. The surveillance camera system moves its head to point to human faces at times, but at other times, humans have to get its attention by moving to its vicinity, creating a dynamic where audiences attempt to see their own faces on the sculpture by gazing into the machine's eye. We found that audiences began attaining an understanding of machines that interpret our faces as separate from our identities, with their own agendas and agencies that show by the way they serendipitously interact with us. The machine-projected images of us are their own interpretation rather than our own, distancing us from our digital analogs. In the accompanying workshop, participants learn about how computer vision works by putting on disguises in order to escape from an algorithm detecting them as the same person by analyzing their faces. Participants learn that their own agency affects how machines interpret them, gaining an appreciation for the way their own identities and machines' awareness of them can be separate entities that can be manipulated for play. Together the installation and workshop empower children and adults to think beyond identification with digital technology to recognize the machine's own interpretive abilities that lie separate from human being's own self-awareness.

Project description:IntroductionUniversities in non-Anglophone countries are increasingly implementing English as the medium of instruction (EMI) lectures. There seems to be an assumption that students' performance on standardized English examinations can be equated with the lexical knowledge needed to comprehend EMI lectures regardless of discipline. For unknown words students encounter, it is assumed that they can be picked up through listening to these lectures. This potential for students to acquire unknown words incidentally while listening to these lectures has yet to be fully explored.MethodsThis study addresses the potential of students incidentally acquiring vocabulary from listening to EMI lectures through corpus analyses of computer science lectures at one public university in Macau. Taking into consideration frequency, range, and lecturer explanation, corpus analyses of the transcripts of 28 computer science lectures (40 h 36 min) were conducted to determine the lexical knowledge needed for students to comprehend the lectures. The potential number of words these students could acquire through listening to the lectures was also uncovered through further analyses.ResultsResults showed that L2 students need to have receptive knowledge of the most frequent 3,000 word families plus proper nouns and marginal words to reach beyond 95% lexical coverage. To reach 98% lexical coverage, 5,000 word families are needed. Considering frequency, range, and teacher explanation, we concluded that 30 new words could reasonably be incidentally acquired after listening to the 28 lectures.DiscussionThese results indicate a need for EMI lecturers to consider the lexical knowledge of students and whether additional pedagogical techniques (i.e., vocabulary explanation) should be employed in content classrooms when lectures are delivered in English, especially for specialized fields such as computer science. Our results also draw attention to the importance of field specific vocabulary and the potential pitfalls of using blanket English language admissions criteria when admitting students to different academic programs.

Dataset Information

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review.

Publications

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review.

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