| |
Records |
Links |
|
Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
|
| |
Title |
A transformer model for boundary detection in continuous sign language |
Type |
Journal Article |
| |
Year |
2024 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Sign Language Recognition (SLR) has garnered significant attention from researchers in recent years, particularly the intricate domain of Continuous Sign Language Recognition (CSLR), which presents heightened complexity compared to Isolated Sign Language Recognition (ISLR). One of the prominent challenges in CSLR pertains to accurately detecting the boundaries of isolated signs within a continuous video stream. Additionally, the reliance on handcrafted features in existing models poses a challenge to achieving optimal accuracy. To surmount these challenges, we propose a novel approach utilizing a Transformer-based model. Unlike traditional models, our approach focuses on enhancing accuracy while eliminating the need for handcrafted features. The Transformer model is employed for both ISLR and CSLR. The training process involves using isolated sign videos, where hand keypoint features extracted from the input video are enriched using the Transformer model. Subsequently, these enriched features are forwarded to the final classification layer. The trained model, coupled with a post-processing method, is then applied to detect isolated sign boundaries within continuous sign videos. The evaluation of our model is conducted on two distinct datasets, including both continuous signs and their corresponding isolated signs, demonstrates promising results. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes  |
HUPBA |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RKE2024 |
Serial |
4016 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Marçal Rusiñol; J. Chazalon; Katerine Diaz |
|
| |
Title |
Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
| |
Volume |
77 |
Issue |
11 |
Pages |
13773-13798 |
|
| |
Keywords |
Augmented reality; Document image matching; Educational applications |
|
| |
Abstract |
This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; ADAS; 600.084; 600.121; 600.118; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RCD2018 |
Serial |
2996 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal |
|
| |
Title |
Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
| |
Volume |
72 |
Issue |
1 |
Pages |
515-539 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer US |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1380-7501 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.077 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SDT2014 |
Serial |
2357 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados |
|
| |
Title |
Classification of aesthetic natural scene images using statistical and semantic features |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
| |
Volume |
82 |
Issue |
9 |
Pages |
13507-13532 |
|
| |
Keywords |
|
|
| |
Abstract |
Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BSP2023 |
Serial |
3873 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Naveen Onkarappa; Angel Sappa |
|
| |
Title |
Synthetic sequences and ground-truth flow field generation for algorithm validation |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
| |
Volume |
74 |
Issue |
9 |
Pages |
3121-3135 |
|
| |
Keywords |
Ground-truth optical flow; Synthetic sequence; Algorithm validation |
|
| |
Abstract |
Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer US |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1380-7501 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ADAS; 600.055; 601.215; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ OnS2014b |
Serial |
2472 |
|
| Permanent link to this record |
