First Canadian Conference on Computer and Robot Vision, 2004. Proceedings., 2000
Range image segmentation has many applications in computer vision areas such as computer graphics... more Range image segmentation has many applications in computer vision areas such as computer graphics and robotic vision. A generic methodology for 3D point set analysis in which planar structures play an important role is defined. It consists mainly of a specific K-means algorithm which is able to process different shapes in cluster. At the same time, within geometric and topologic considerations, a set of application-driven heuristics is designed. This helps to find out the right number of structures in point sets in order to give a good visualization and representation of a large scale environment without a priori models. Our aim is to propose a simple and generic frame for 3D scene understanding. Tests were realised on different types of environment data: natural and man-made. This research project has been realized with EADS (French Air Space Society).
IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2000
We present some results on systems for automatically detecting bridges in high-resolution satelli... more We present some results on systems for automatically detecting bridges in high-resolution satellite images. We had made some preliminary explorations on the use of geometric models to detect bridges and round-abouts in panchromatic, high-resolution ((50 cm)2 per pixel) satellite images. We had not yet systematically evaluated the system, but false alarms seemed to be its most important problem. In parallel, we had also built a system, which used local radiometric and textural features to classify terrain pixels into a number of semantically-meaningful classes, and then applied spatial relationships rules to these classes to detect and locate bridges. Validation showed a very low false alarm rate (around 5%), but also a low detection rate (around 40%).
2013 IEEE International Conference on Image Processing, 2013
The aim of the work presented in this paper is to develop a method for the automatic identificati... more The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has been evaluated using two datasets acquired in two different French forests with different terrain characteristics. The results obtained are very encouraging and promising.
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000
Algorithms are presented which allow to perform morphological transformations on unorganised sets... more Algorithms are presented which allow to perform morphological transformations on unorganised sets of points represented by their Delaunay triangulation. The results show that these algorithms could behaves as morphological operators such as erosion, dilatation, opening do. As a matter of fact, they actually act as "shape filter". These algorithms are applied to the problem of scene reconstruction by stereoscopy in which objets are represented by unstructured clouds of 3D points.
2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013
Picture-in-picture (PiP) is a feature of some television receivers and video devices, which allow... more Picture-in-picture (PiP) is a feature of some television receivers and video devices, which allows one main program to be displayed on the full screen while one or more subprogram displayed in inset windows. Currently most TV/video devices require users to specify where and how large to place the sub-program over the main program display. This process is instead not user-friendly as it involves a manual process and once specified, the size and the location of the sub-program will be fixed even when they block some key visual information from the main program. We propose an automatic and adaptive PiP technology that makes use of computational modeling of visual saliency. For each frame of the main program, a saliency map is computed efficiently which quantifies how probable a display region of the main program contains useful information and will attract humans' attention/eyes. The sub-program can thus be adaptively resized and placed to the display region that contains the least useful information. Preliminary experiments show the effectiveness of the proposed technology.
Terrestrial Laser Scanning (TLS) technique is today widely used in ground plots to acquire 3D poi... more Terrestrial Laser Scanning (TLS) technique is today widely used in ground plots to acquire 3D point clouds from which forest inventory attributes are calculated. In the case of mixed plantings where the 3D point clouds contain data from several different tree species, it is important to be able to automatically recognize the tree species in order to analyze the data of each of the species separately. Although automatic tree species recognition from TLS data is an important problem, it has received very little attention from the scientific community. In this paper we propose a method for classifying five different tree species using TLS data. Our method is based on the analysis of the 3D geometric texture of the bark in order to compute roughness measures and shape characteristics that are fed as input to a Random Forest classifier to classify the tree species. The method has been evaluated on a test set composed of 265 samples (53 samples of each of the 5 species) and the results obtained are very encouraging.
In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitos... more In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. Several studies on automatic tools to process digitized sli...
... Formalism Adina TUTAC, Daniel RACOCEANU, Nicolas LOMENIE, Wee-Kheng LEOW, Ludovic ROUX, Vladi... more ... Formalism Adina TUTAC, Daniel RACOCEANU, Nicolas LOMENIE, Wee-Kheng LEOW, Ludovic ROUX, Vladimir CRETU, Thomas PUTTI IPAL - Image Perception, Access & Language International Research Unit, Singapore http://ipal.i2r.a-star.edu.sg/ ...
Histopathological examination is a powerful method for prognosis of major diseases such as breast... more Histopathological examination is a powerful method for prognosis of major diseases such as breast cancer. Analysis of medical images largely remains the work of human experts. Current virtual microscope systems are mainly an emulation of real microscopes with annotation and some image analysis capabilities. However, the lack of effective knowledge management prevents such systems from being computer-aided prognosis platforms. The cognitive virtual microscopic framework, through an extended modeling and use of medical knowledge, has the capacity to analyse histopathological images and to perform grading of breast cancer, providing pathologists with a robust and traceable second opinion.
First Canadian Conference on Computer and Robot Vision, 2004. Proceedings., 2000
Range image segmentation has many applications in computer vision areas such as computer graphics... more Range image segmentation has many applications in computer vision areas such as computer graphics and robotic vision. A generic methodology for 3D point set analysis in which planar structures play an important role is defined. It consists mainly of a specific K-means algorithm which is able to process different shapes in cluster. At the same time, within geometric and topologic considerations, a set of application-driven heuristics is designed. This helps to find out the right number of structures in point sets in order to give a good visualization and representation of a large scale environment without a priori models. Our aim is to propose a simple and generic frame for 3D scene understanding. Tests were realised on different types of environment data: natural and man-made. This research project has been realized with EADS (French Air Space Society).
IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2000
We present some results on systems for automatically detecting bridges in high-resolution satelli... more We present some results on systems for automatically detecting bridges in high-resolution satellite images. We had made some preliminary explorations on the use of geometric models to detect bridges and round-abouts in panchromatic, high-resolution ((50 cm)2 per pixel) satellite images. We had not yet systematically evaluated the system, but false alarms seemed to be its most important problem. In parallel, we had also built a system, which used local radiometric and textural features to classify terrain pixels into a number of semantically-meaningful classes, and then applied spatial relationships rules to these classes to detect and locate bridges. Validation showed a very low false alarm rate (around 5%), but also a low detection rate (around 40%).
2013 IEEE International Conference on Image Processing, 2013
The aim of the work presented in this paper is to develop a method for the automatic identificati... more The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has been evaluated using two datasets acquired in two different French forests with different terrain characteristics. The results obtained are very encouraging and promising.
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000
Algorithms are presented which allow to perform morphological transformations on unorganised sets... more Algorithms are presented which allow to perform morphological transformations on unorganised sets of points represented by their Delaunay triangulation. The results show that these algorithms could behaves as morphological operators such as erosion, dilatation, opening do. As a matter of fact, they actually act as "shape filter". These algorithms are applied to the problem of scene reconstruction by stereoscopy in which objets are represented by unstructured clouds of 3D points.
2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013
Picture-in-picture (PiP) is a feature of some television receivers and video devices, which allow... more Picture-in-picture (PiP) is a feature of some television receivers and video devices, which allows one main program to be displayed on the full screen while one or more subprogram displayed in inset windows. Currently most TV/video devices require users to specify where and how large to place the sub-program over the main program display. This process is instead not user-friendly as it involves a manual process and once specified, the size and the location of the sub-program will be fixed even when they block some key visual information from the main program. We propose an automatic and adaptive PiP technology that makes use of computational modeling of visual saliency. For each frame of the main program, a saliency map is computed efficiently which quantifies how probable a display region of the main program contains useful information and will attract humans' attention/eyes. The sub-program can thus be adaptively resized and placed to the display region that contains the least useful information. Preliminary experiments show the effectiveness of the proposed technology.
Terrestrial Laser Scanning (TLS) technique is today widely used in ground plots to acquire 3D poi... more Terrestrial Laser Scanning (TLS) technique is today widely used in ground plots to acquire 3D point clouds from which forest inventory attributes are calculated. In the case of mixed plantings where the 3D point clouds contain data from several different tree species, it is important to be able to automatically recognize the tree species in order to analyze the data of each of the species separately. Although automatic tree species recognition from TLS data is an important problem, it has received very little attention from the scientific community. In this paper we propose a method for classifying five different tree species using TLS data. Our method is based on the analysis of the 3D geometric texture of the bark in order to compute roughness measures and shape characteristics that are fed as input to a Random Forest classifier to classify the tree species. The method has been evaluated on a test set composed of 265 samples (53 samples of each of the 5 species) and the results obtained are very encouraging.
In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitos... more In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. Several studies on automatic tools to process digitized sli...
... Formalism Adina TUTAC, Daniel RACOCEANU, Nicolas LOMENIE, Wee-Kheng LEOW, Ludovic ROUX, Vladi... more ... Formalism Adina TUTAC, Daniel RACOCEANU, Nicolas LOMENIE, Wee-Kheng LEOW, Ludovic ROUX, Vladimir CRETU, Thomas PUTTI IPAL - Image Perception, Access & Language International Research Unit, Singapore http://ipal.i2r.a-star.edu.sg/ ...
Histopathological examination is a powerful method for prognosis of major diseases such as breast... more Histopathological examination is a powerful method for prognosis of major diseases such as breast cancer. Analysis of medical images largely remains the work of human experts. Current virtual microscope systems are mainly an emulation of real microscopes with annotation and some image analysis capabilities. However, the lack of effective knowledge management prevents such systems from being computer-aided prognosis platforms. The cognitive virtual microscopic framework, through an extended modeling and use of medical knowledge, has the capacity to analyse histopathological images and to perform grading of breast cancer, providing pathologists with a robust and traceable second opinion.
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Papers by N. Lomenie