Rajesh Uttam Kanthe
Biographic Data
| ID | 8968302 |
|---|---|
| NAME | Rajesh Uttam Kanthe |
| GIVEN NAMES | Rajesh Uttam |
| FAMILY NAME | Kanthe |
| SIGNATURE | KANTHE R U |
| AFFILIATIONS | Bharati Vidyapeeth Deemed University |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Ethics and Policy Challenges in the Digitization of Cultural Heritage
The process of cultural heritage digitization has turned to be one of the most important strategies of preservation of the historical artifacts, manuscripts, art pieces, and the digital age intangible traditions. Cultural institutions can preserve heritage resources, capture more, and make them more accessible and then ever before through the use of high-tech technologies such as the features of 3D scanning, artificial intelligence, digital archi…
Digital Archiving of Folk Art Through Machine Learning
Computational problems in the digital preservation of folk art are distinct because of the visual diversities, long tail distributions of motifs, and incomplete meta-data. The proposed paper presents a machine learning-based intelligent system of digital archiving folk art, which incorporates the visual feature learning, the automatic mechanisms of semantic enrichment, and the scaling of the retrieval mechanisms. The method uses both a hybrid con…
Object Detection in Photography Using Deep Learning
Object detection in photography has developed fast due to deep learning and has changed the manner in which visual content is photographed, arranged, and understood. This paper is a detailed examination of the current detection systems and how they can apply to the photographic process. Starting with the description of classical approaches like HOG, Haar cascades, and SVM-based networks, the paper compares the drawbacks of the mentioned methods w…
No prominent works on this page.
Object Detection in Photography Using Deep Learning
Object detection in photography has developed fast due to deep learning and has changed the manner in which visual content is photographed, arranged, and understood. This paper is a detailed examination of the current detection systems and how they can apply to the photographic process. Starting with the description of classical approaches like HOG, Haar cascades, and SVM-based networks, the paper compares the drawbacks of the mentioned methods w…
Ethics and Policy Challenges in the Digitization of Cultural Heritage
The process of cultural heritage digitization has turned to be one of the most important strategies of preservation of the historical artifacts, manuscripts, art pieces, and the digital age intangible traditions. Cultural institutions can preserve heritage resources, capture more, and make them more accessible and then ever before through the use of high-tech technologies such as the features of 3D scanning, artificial intelligence, digital archi…
Digital Archiving of Folk Art Through Machine Learning
Computational problems in the digital preservation of folk art are distinct because of the visual diversities, long tail distributions of motifs, and incomplete meta-data. The proposed paper presents a machine learning-based intelligent system of digital archiving folk art, which incorporates the visual feature learning, the automatic mechanisms of semantic enrichment, and the scaling of the retrieval mechanisms. The method uses both a hybrid con…
Aesthetic Perception and Analysis (2 works) · Architecture (2 works) · Deep learning (2 works) · 3D Surveying and Cultural Heritage (1 works) · Advanced Image and Video Retrieval Techniques (1 works) · Amateur (1 works) · Archaeological Research and Protection (1 works) · Computational photography (1 works) · Convolutional neural network (1 works) · Cultural heritage (1 works)