ImageLab: Simplifying Image Processing Exploration for Novices and
Experts Alike
- URL: http://arxiv.org/abs/2401.03157v1
- Date: Sat, 6 Jan 2024 08:27:28 GMT
- Title: ImageLab: Simplifying Image Processing Exploration for Novices and
Experts Alike
- Authors: Sahan Dissanayaka, Oshan Mudanayaka, Thilina Halloluwa, Chameera De
Silva
- Abstract summary: "ImageLab" is a novel tool designed to democratize image processing, catering to both novices and experts.
ImageLab not only serves as a valuable educational resource but also offers a practical testing environment for seasoned practitioners.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Image processing holds immense potential for societal benefit, yet its full
potential is often accessible only to tech-savvy experts. Bridging this
knowledge gap and providing accessible tools for users of all backgrounds
remains an unexplored frontier. This paper introduces "ImageLab," a novel tool
designed to democratize image processing, catering to both novices and experts
by prioritizing interactive learning over theoretical complexity. ImageLab not
only serves as a valuable educational resource but also offers a practical
testing environment for seasoned practitioners. Through a comprehensive
evaluation of ImageLab's features, we demonstrate its effectiveness through a
user study done for a focused group of school children and university students
which enables us to get positive feedback on the tool. Our work represents a
significant stride toward enhancing image processing education and practice,
making it more inclusive and approachable for all.
Related papers
- Supporting Experts with a Multimodal Machine-Learning-Based Tool for
Human Behavior Analysis of Conversational Videos [40.30407535831779]
We developed Providence, a visual-programming-based tool based on design considerations derived from a formative study with experts.
It enables experts to combine various machine learning algorithms to capture human behavioral cues without writing code.
Our study showed its preferable usability and satisfactory output with less cognitive load imposed in accomplishing scene search tasks of conversations.
arXiv Detail & Related papers (2024-02-17T00:27:04Z) - CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update [69.59482029810198]
CLOVA is a Closed-Loop Visual Assistant that operates within a framework encompassing inference, reflection, and learning phases.
Results demonstrate that CLOVA surpasses existing tool-usage methods by 5% in visual question answering and multiple-image reasoning, by 10% in knowledge tagging, and by 20% in image editing.
arXiv Detail & Related papers (2023-12-18T03:34:07Z) - Empowering Visually Impaired Individuals: A Novel Use of Apple Live
Photos and Android Motion Photos [3.66237529322911]
We advocate for the use of Apple Live Photos and Android Motion Photos technologies.
Our findings reveal that both Live Photos and Motion Photos outperform single-frame images in common visual assisting tasks.
arXiv Detail & Related papers (2023-09-14T20:46:35Z) - Improving Human-Object Interaction Detection via Virtual Image Learning [68.56682347374422]
Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects.
In this paper, we propose to alleviate the impact of such an unbalanced distribution via Virtual Image Leaning (VIL)
A novel label-to-image approach, Multiple Steps Image Creation (MUSIC), is proposed to create a high-quality dataset that has a consistent distribution with real images.
arXiv Detail & Related papers (2023-08-04T10:28:48Z) - Identifying Professional Photographers Through Image Quality and
Aesthetics in Flickr [0.0]
This study reveals the lack of suitable data sets in photo and video sharing platforms.
We created one of the largest labelled data sets in Flickr with the multimodal data which has been open sourced.
We examined the relationship between the aesthetics and technical quality of a picture and the social activity of that picture.
arXiv Detail & Related papers (2023-07-04T14:55:37Z) - Procedural Image Programs for Representation Learning [62.557911005179946]
We propose training with a large dataset of twenty-one thousand programs, each one generating a diverse set of synthetic images.
These programs are short code snippets, which are easy to modify and fast to execute using.
The proposed dataset can be used for both supervised and unsupervised representation learning, and reduces the gap between pre-training with real and procedurally generated images by 38%.
arXiv Detail & Related papers (2022-11-29T17:34:22Z) - Automatic Image Content Extraction: Operationalizing Machine Learning in
Humanistic Photographic Studies of Large Visual Archives [81.88384269259706]
We introduce Automatic Image Content Extraction framework for machine learning-based search and analysis of large image archives.
The proposed framework can be applied in several domains in humanities and social sciences.
arXiv Detail & Related papers (2022-04-05T12:19:24Z) - Use Image Clustering to Facilitate Technology Assisted Review [0.5249805590164902]
Technology Assisted Review (TAR) in electronic discovery is witnessing a rising need to incorporate multimedia content in the scope.
We have developed innovative image analytics applications for TAR in the past years, such as image classification, image clustering, and object detection.
arXiv Detail & Related papers (2021-12-16T04:02:51Z) - Factors of Influence for Transfer Learning across Diverse Appearance
Domains and Task Types [50.1843146606122]
A simple form of transfer learning is common in current state-of-the-art computer vision models.
Previous systematic studies of transfer learning have been limited and the circumstances in which it is expected to work are not fully understood.
In this paper we carry out an extensive experimental exploration of transfer learning across vastly different image domains.
arXiv Detail & Related papers (2021-03-24T16:24:20Z) - From A Glance to "Gotcha": Interactive Facial Image Retrieval with
Progressive Relevance Feedback [72.29919762941029]
We propose an end-to-end framework to retrieve facial images with relevance feedback progressively provided by the witness.
With no need of any extra annotations, our model can be applied at the cost of a little response effort.
arXiv Detail & Related papers (2020-07-30T18:46:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.