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Modelling Postures of Human Movements [chapter]

Djamila Medjahed Gamaz, Houssem Eddine Gueziri, Nazim Haouchine
2010 Lecture Notes in Computer Science  
The goal of this paper is to present a novel modelling of postures of human activities such us walk, run... Effectively, human action is, in general, characterized by a sequence of specific body postures. So, from an incoming sequence video, we determine the postures (key-frames) which will represent the movement. We construct the prototypes corresponding to these key-frames by thinning these postures, and then we use this skeleton as a starting point for building the model. Some results are presented to validate our models.
doi:10.1007/978-3-642-16687-7_30 fatcat:6k74yefgbzbmzpraizdw2aw454

Computer vision system for automatic counting of planting microsites using UAV imagery

Wassim Bouachir, Koffi Eddy Ihou, Houssem-Eddine Gueziri, Nizar Bouguila, Nicolas Belanger
2019 IEEE Access  
Mechanical site preparation by mounding is often used by the forest industry to provide optimal growth conditions for tree seedlings. Prior to planting, an essential step consists in estimating the number of mounds at each planting block, which serves as planting microsites. This task often requires long and costly field surveys, implying several forestry workers to perform manual counting procedure. This paper addresses the problem of automating the counting process using computer vision and
more » ... V imagery. We present a supervised detection-based counting framework for estimating the number of planting microsites on a mechanically prepared block. The system is trained offline to learn feature representations from semiautomatically annotated images. Mound detection and counting are then performed on multispectral UAV images captured at an altitude of 100 m. Our detection framework proceeds by generating region proposals based on local binary patterns (LBP) features extracted from near-infrared (NIR) patches. A convolutional neural network (CNN) is then used for classifying candidate regions by considering multispectral image data. To train and evaluate the proposed method, we constructed a new dataset by capturing aerial images from different planting blocks. The results demonstrate the efficiency and validity of the proposed method under challenging experimental conditions. The methods and results presented in this paper form a promising cornerstone to develop advanced decision support systems for planning planting operations. INDEX TERMS Precision forestry, computer vision, UAV imagery, artificial intelligence. VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see
doi:10.1109/access.2019.2923765 fatcat:5dprsqv66bcj3loyvlaasydqdi

A generalized graph reduction framework for interactive segmentation of large images

Houssem-Eddine Gueziri, Michael J. McGuffin, Catherine Laporte
2016 Computer Vision and Image Understanding  
The speed of graph-based segmentation approaches, such as random walker (RW) and graph cut (GC), depends strongly on image size. For high-resolution images, the time required to compute a segmentation based on user input renders interaction tedious. We propose a novel method, using an approximate contour sketched by the user, to reduce the graph before passing it on to a segmentation algorithm such as RW or GC. This enables a significantly faster feedback loop. The user first draws a rough
more » ... ur of the object to segment. Then, the pixels of the image are partitioned into "layers" (corresponding to different scales) based on their distance from the contour. The thickness of these layers increases with distance to the contour according to a Fibonacci sequence. An initial segmentation result is rapidly obtained after automatically generating foreground and background labels according to a specifically selected layer; all vertices beyond this layer are eliminated, restricting the segmentation to regions near the drawn contour. Further foreground / background labels can then be added by the user to refine the segmentation. All iterations of the graphbased segmentation benefit from a reduced input graph, while maintaining full resolution near the object boundary. A user study with 16 participants was carried out for RW segmentation of a multi-modal dataset of 22 medical images, using either a standard mouse or a stylus pen to draw the contour. Results reveal that our approach significantly reduces the overall segmentation time compared with the status quo approach (p < 0.01). The study also shows that our approach works well with both input devices. Compared to super-pixel graph reduction, our approach provides full resolution accuracy at similar speed on a high-resolution benchmark image with both RW and GC segmentation methods. However, graph reduction based on super-pixels does not allow interactive correction of clustering errors. Finally, our approach can be combined with super-pixel clustering methods for further graph reduction, resulting in even faster segmentation. are weighted as a function of their likelihood of crossing an object boundary. For interactive segmentation to be practical, the computation of the edge weights and segmentation must be fast, enabling a tight feedback loop. However, in graph-based segmentation, computation time increases with graph size, often precluding interactive segmentation of high-resolution images. The total time to perform a segmentation also depends on human factors, such as the input device used and the kind of input required. These challenges are addressed in this paper. We present a graph reduction approach that is guided by a rough drawing of the object boundary provided by the user. Our preliminary work [14] used automatically simulated input drawings to show that this approach speeds up computations for random walker segmentation. This paper further investigates the approach and extends our analysis to make the following additional contributions: • A controlled experiment compared performance with two input techniques and two input devices (mouse and stylus pen). • The graph reduction approach is extended to different interactive graph-based segmentations ensuring a precise, high-resolution segmentation.
doi:10.1016/j.cviu.2016.05.009 fatcat:o5fpfxik25dxdhja6eif47f4vi

Evaluation of an Ultrasound-Based Navigation System for Spine Neurosurgery: A Porcine Cadaver Study

Houssem-Eddine Gueziri, Oded Rabau, Carlo Santaguida, D Louis Collins
2021 Frontiers in Oncology  
Copyright © 2021 Gueziri, Rabau, Santaguida and Collins. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).  ... 
doi:10.3389/fonc.2021.619204 pmid:33763355 pmcid:PMC7982867 fatcat:ylsr2yvapfgebnp7ajyxu7lxha

Image splicing detection with local illumination estimation

Yu Fan, Philippe Carre, Christine Fernandez-Maloigne
2015 2015 IEEE International Conference on Image Processing (ICIP)  
Jan FLUSSER, Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic 14:20 TEC-O8.2 -USER-GUIDED GRAPH REDUCTION FOR FAST IMAGE SEGMENTATION Houssem-Eddine GUEZIRI  ...  Technology Guwahati Amit SETHI, Indian Institute of Technology Guwahati New software developer tools -for managing your code • Improved performance -for tackling bigger problems faster Presenter Steve Eddins  ... 
doi:10.1109/icip.2015.7351341 dblp:conf/icip/FanCF15 fatcat:7ja5gjnp5rafvedc2nman7xcru