Search results for "Image Segmentation"
showing 10 items of 234 documents
Calibration of a three-dimensional reconstruction system using a structured light source
2002
We present a method for calibrating a range finder system composed of a camera and a structured light source. The system is used to reconstruct the three-dimensional (3-D) surface of an object. This is achieved by projecting a pattern, represented by a set of regularly spaced spots, on the surface of the object using the structured light source. An image of the illuminated object is next taken and by analyzing the distortion of the projected pattern, the 3-D surface of the object can be reconstructed. This reconstruction operation can be envisaged only if the system is calibrated. Instead of using a classical calibration method, which is based on the determination of the matrices that chara…
Classification Boundary Approximation by Using Combination of Training Steps for Real-Time Image Segmentation
2007
We propose a method of real-time implementation of an approximation of the support vector machine decision rule. The method uses an improvement of a supervised classification method based on hyperrectangles, which is useful for real-time image segmentation. We increase the classification and speed performances using a combination of classification methods: a support vector machine is used during a pre-processing step. We recall the principles of the classification methods and we evaluate the hardware implementation cost of each method. We present our learning step combination algorithm and results obtained using Gaussian distributions and an example of image segmentation coming from a part …
Real-time characterization of aspect flaws on warped surface by artificial vision
1997
Artificial vision is an efficient means of assuring the quality of a certain class of products. The vision system must respect the industrial constraints, in particular, the production rate. The geometrical features of flaws are pertinent information used for the acceptance of the controlled product. This article presents a real-time algorithm for the geometrical characterization of defects located on warped objects. The algorithms described enable the characterization of defects by their size and their 2-D shape. Both parameters are calculated in real time by simple reference to a look-up table. The 2-D shape is obtained by a geometrical transform and an interpolation. The efficiency of th…
Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins
2008
The detection of rotten citrus in packing lines is carried out manually under ultraviolet illumination, which is dangerous for workers. Light emitted by the rotten region of the fruit due to the ultraviolet-induced fluorescence is used by the operator to detect the damages. This procedure is required because the low contrast between the damaged and sound skin under visible illumination difficult their detection. We study a set of techniques aimed to detect rottenness in citrususing visible and near infrared lighting trough an hyperspectral imaging system. Methods for selecting a proper set of wavelengths are investigated such as correlation analysis, mutual information, stepwise or genetic …
MedAI: Transparency in Medical Image Segmentation
2021
MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems. We propose three tasks to meet specific gastrointestinal image segmentation challenges collected from experts within the field, including two separate segmentation scenarios and one scenario on transparent ML systems. The latter emphasizes the need for explainable and interpretable ML algorithms. We provide a development dataset for the participants to train their ML models, tested on a concealed test dataset.
A Microcalcification Detection System in Mammograms based on ANN Clustering
2018
Breast cancer is one of the leading causes to women mortality in the world. Clustered microcalcifications (MCs) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. In this work, we present a novel method for the detection of MCs in mammograms which consists of regions of Interest (ROIs) segmentation, based on a spatial filter that allows the detection of small and large microcalcifications, clustering and classification of MCs by Artificial Neural Network. The system has been tested on a public dataset of digital images and compared with previous approaches. The results demonstrate that the proposed approach could achie…
Breast Ultra-Sound image segmentation: an optimization approach based on super-pixels and high-level descriptors
2015
International audience; Breast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the b…
Modular Method of Detection, Localization and Counting of Mutliple-Taxon Pollen Apertures Using Bag of Words
2014
International audience; Accurate recognition of airborne pollen taxa is crucial for understanding and treating allergic diseases, which affect an important proportion of the world population. Modern computer vision techniques enables the detection of discriminant characteristics. Apertures is one of these characteristic that has been little explored up to now. In this paper, a flexible method of detection, localization and counting of apertures of different pollen taxa with varying appearances is proposed. Apertures are described based by primitive images following the Bag-of-Words strat-egy. A confidence map is estimated based on the classification of sampled regions. The method is designe…
Real-time image segmentation for anomalies detection using SVM approximation
2003
In this paper, we propose a method of implementation improvement of the decision rule of the support vector machine, applied to real-time image segmentation. We present very high speed decisions (approximately 10 ns per pixel) which can be useful for detection of anomalies on manufactured parts. We propose an original combination of classifiers allowing fast and robust classification applied to image segmentation. The SVM is used during a first step, pre-processing the training set and thus rejecting any ambiguities. The hyperrectangles-based learning algorithm is applied using the SVM classified training set. We show that the hyperrectangle method imitates the SVM method in terms of perfor…
A completely automated CAD system for mass detection in a large mammographic database
2006
Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing secon…