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Author Ariel Amato edit  openurl
  Title (down) Moving cast shadow detection Type Journal Article
  Year 2014 Publication Electronic letters on computer vision and image analysis Abbreviated Journal ELCVIA  
  Volume 13 Issue 2 Pages 70-71  
  Keywords  
  Abstract Motion perception is an amazing innate ability of the creatures on the planet. This adroitness entails a functional advantage that enables species to compete better in the wild. The motion perception ability is usually employed at different levels, allowing from the simplest interaction with the ’physis’ up to the most transcendental survival tasks. Among the five classical perception system , vision is the most widely used in the motion perception field. Millions years of evolution have led to a highly specialized visual system in humans, which is characterized by a tremendous accuracy as well as an extraordinary robustness. Although humans and an immense diversity of species can distinguish moving object with a seeming simplicity, it has proven to be a difficult and non trivial problem from a computational perspective. In the field of Computer Vision, the detection of moving objects is a challenging and fundamental research area. This can be referred to as the ’origin’ of vast and numerous vision-based research sub-areas. Nevertheless, from the bottom to the top of this hierarchical analysis, the foundations still relies on when and where motion has occurred in an image. Pixels corresponding to moving objects in image sequences can be identified by measuring changes in their values. However, a pixel’s value (representing a combination of color and brightness) could also vary due to other factors such as: variation in scene illumination, camera noise and nonlinear sensor responses among others. The challenge lies in detecting if the changes in pixels’ value are caused by a genuine object movement or not. An additional challenging aspect in motion detection is represented by moving cast shadows. The paradox arises because a moving object and its cast shadow share similar motion patterns. However, a moving cast shadow is not a moving object. In fact, a shadow represents a photometric illumination effect caused by the relative position of the object with respect to the light sources. Shadow detection methods are mainly divided in two domains depending on the application field. One normally consists of static images where shadows are casted by static objects, whereas the second one is referred to image sequences where shadows are casted by moving objects. For the first case, shadows can provide additional geometric and semantic cues about shape and position of its casting object as well as the localization of the light source. Although the previous information can be extracted from static images as well as video sequences, the main focus in the second area is usually change detection, scene matching or surveillance. In this context, a shadow can severely affect with the analysis and interpretation of the scene. The work done in the thesis is focused on the second case, thus it addresses the problem of detection and removal of moving cast shadows in video sequences in order to enhance the detection of moving object.  
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  Notes ISE Approved no  
  Call Number Admin @ si @ Ama2014 Serial 2870  
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Author Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez edit  url
openurl 
  Title (down) Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition Type Journal Article
  Year 2023 Publication ACM Transactions on Multimedia Computing, Communications, and Applications Abbreviated Journal TMCCA  
  Volume 20 Issue 2 Pages 1–20  
  Keywords  
  Abstract Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method.  
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  Notes ISE Approved no  
  Call Number Admin @ si @ GZW2023 Serial 3862  
Permanent link to this record
 
 
Author Wenjuan Gong; Zhang Yue; Wei Wang; Cheng Peng; Jordi Gonzalez edit  doi
openurl 
  Title (down) Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition Type Journal Article
  Year 2022 Publication ACM Transactions on Multimedia Computing, Communications, and Applications Abbreviated Journal ACMTMC  
  Volume Issue Pages  
  Keywords Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition  
  Abstract Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method.  
  Address May 2022  
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  Notes ISE; 600.157 Approved no  
  Call Number Admin @ si @ GYW2022 Serial 3692  
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Author Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon edit  url
openurl 
  Title (down) Looking at People Special Issue Type Journal Article
  Year 2018 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 126 Issue 2-4 Pages 141-143  
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  Notes HUPBA; ISE; 600.119 Approved no  
  Call Number Admin @ si @ EGJ2018 Serial 3093  
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Author Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez edit  url
doi  openurl
  Title (down) Logo Detection With No Priors Type Journal Article
  Year 2021 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume 9 Issue Pages 106998-107011  
  Keywords  
  Abstract In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors.  
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  Notes ISE Approved no  
  Call Number Admin @ si @ VGR2021 Serial 3664  
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