scholarly journals Fully automatic segmentation of bee wing images

2020 ◽  
Vol 12 (2) ◽  
pp. 37-45
Author(s):  
João Marcos Garcia Fagundes ◽  
Allan Rodrigues Rebelo ◽  
Luciano Antonio Digiampietri ◽  
Helton Hideraldo Bíscaro
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Feature Extraction ◽  
Hausdorff Distance ◽  
Automatic Segmentation ◽  
Food Crops ◽  
Artificial Intelligence Techniques ◽  
Fully Automatic ◽  
Modified Hausdorff Distance ◽  
F Measure

Bee preservation is important because approximately 70% of all pollination of food crops is made by them and this service costs more than $ 65 billion annually. In order to help this preservation, the identification of the bee species is necessary, and since this is a costly and time-consuming process, techniques that automate and facilitate this identification become relevant. Images of bees' wings in conjunction with computer vision and artificial intelligence techniques can be used to automate this process. This paper presents an approach to do segmentation of bees' wing images and feature extraction. Our approach was evaluated using the modified Hausdorff distance and F measure. The results were, at least, 24% more precise than the related approaches and the proposed approach was able to deal with noisy images.

scholarly journals Facial and Body Feature Extraction for Emotionally-Rich HCI

2011 ◽  
pp. 2183-2192
Author(s):  
Kostas Karpouzis ◽  
Athanasios Drosopoulos ◽  
Spiros Ioannou ◽  
Amaryllis Raouzaiou ◽  
Nicolas Tsapatsoulis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Feature Extraction ◽  
Facial Expressions ◽  
Emotional State ◽  
Different Cultures ◽  
Machine Interaction

Emotionally-aware Man-Machine Interaction (MMI) systems are presently at the forefront of interest of the computer vision and artificial intelligence communities, since they give the opportunity to less technology-aware people to use computers more efficiently, overcoming fears and preconceptions. Most emotion-related facial and body gestures are considered to be universal, in the sense that they are recognized along different cultures; therefore, the introduction of an “emotional dictionary” that includes descriptions and perceived meanings of facial expressions and body gestures, so as to help infer the likely emotional state of a specific user, can enhance the affective nature of MMI applications (Picard, 2000).

scholarly journals Facial and Body Feature Extraction for Emotionally-Rich HCI

2005 ◽  
pp. 1180-1185
Author(s):  
Kostas Karpouzis ◽  
Athanasios Drosopoulos ◽  
Spiros Ioannou ◽  
Amaryllis Raouzaiou ◽  
Nicolas Tsapatsoulis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Feature Extraction ◽  
Facial Expressions ◽  
Emotional State ◽  
Different Cultures ◽  
Machine Interaction

Emotionally-aware Man-Machine Interaction (MMI) systems are presently at the forefront of interest of the computer vision and artificial intelligence communities, since they give the opportunity to less technology-aware people to use computers more efficiently, overcoming fears and preconceptions. Most emotion-related facial and body gestures are considered to be universal, in the sense that they are recognized along different cultures; therefore, the introduction of an “emotional dictionary” that includes descriptions and perceived meanings of facial expressions and body gestures, so as to help infer the likely emotional state of a specific user, can enhance the affective nature of MMI applications (Picard, 2000).

scholarly journals A Compilation of Methods and Datasets for Group and Crowd Action Recognition

2018 ◽  
pp. 2025-2041
Author(s):  
Luis Felipe Borja ◽  
Jorge Azorin-Lopez ◽  
Marcelo Saval-Calvo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Machine Learning Techniques ◽  
Challenging Problem ◽  
Artificial Intelligence Techniques ◽  
Group Behaviour ◽  
Learning Techniques ◽  
Methods And Techniques ◽  
Different Levels

The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group.

AI Based Feature Extraction Through Content Based Image Retrieval

2020 ◽  
Vol 17 (9) ◽  
pp. 4050-4054
Author(s):  
C. Gururaj ◽  
Satish Tunga
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Human Services ◽  
Research Work ◽  
Content Based Image Retrieval ◽  
Data Combination ◽  
Artificial Intelligence Techniques ◽  
Processing Strategies

Therapeutic pictures are progressively being utilized inside human services for conclusion, arranging treatment, controlling treatment and checking sickness movement. In reality, helpful imaging prevalently shapes vague, missing, dubious, essential, clashing, dull restricting, contorted data additionally, information has a strong fundamental character. The proposed approach can be used to achieve the accuracy by using the artificial intelligence techniques wherein the disease level is identified by comparing it with the artificial intelligence data. The two fold merit of this system is it provides better accuracy and also determines all the possibilities of spreading of the disease including the various stages of the disease. This research work also represents new automated strategies of the division and arrangement of therapeutic pictures utilizing computerized reasoning, i.e., delicate processing strategies, data combination and particular area information. Promising outcomes demonstrate the predominance of the delicate processing and information based approach over best customary systems as far as division mistakes. The arrangement of various structures is made by executing rules obtained by both space literature and by medical experts.

scholarly journals Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review

2021 ◽  
Vol 18 (20) ◽  
pp. 10909
Author(s):  
Federico D’Antoni ◽  
Fabrizio Russo ◽  
Luca Ambrosio ◽  
Luca Vollero ◽  
Gianluca Vadalà ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Feature Extraction ◽  
Low Back Pain ◽  
Deep Learning ◽  
Back Pain ◽  
Intervertebral Discs ◽  
Diagnosis And Treatment ◽  
Systematic Research ◽  
Low Back

Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Feature Extraction”, “Segmentation”, “Computer Vision”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Low Back Pain”, “Lumbar”. Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen–Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems’ autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.

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