Search results for "ndc"
showing 10 items of 1025 documents
Comparison of perceptually uniform quantisation with average error minimisation in image transform coding
1999
An alternative transform coder design criterion based on restricting the maximum perceptual error of each coefficient is proposed. This perceptually uniform quantisation of the transform domain ensures that the perceptual error will be below a certain limit regardless of the particular input image. The results show that the proposed criterion improves the subjective quality of the conventional average error criterion even if it is weighted with the same perceptual metric.
A Novel Iris Recognition System based on Micro-Features
2007
In this paper a novel approach for iris recognition system based on iris micro-features is proposed. The proposed system follows the minutiae based approach developed for fingerprint recognition systems. The proposed system uses four iris microfeatures, considered as minutiae, for identification. The individualized characteristics are nucleus, collarette, valleys and radius. Iris recognition is divided in three main phases: image preprocessing, micro-features extraction and matching. The algorithm has been tested on CASIA v1.0 iris image database obtaining an high accuracy. The obtained experimental results have been analyzed and compared with the Daugman based approach.
Fingerprint image enhancement using directional morphological filter
2005
Fingerprint images quality enhancement is a topic phase to ensure good performance in an automatic fingerprint identification system (AFIS) based on minutiae matching. In this paper a new fingerprint enhancement algorithm based on morphological filter is introduced. The algorithm is based on three steps: directional decomposition, morphological filter and composition. The performance of the proposed approach has been evaluated on two sets of images: the first one is DB3 database from Fingerprint Verification Competition (FVC) and the second one is self collected using an optical scanner
Benchmarking Saliency Detection Methods on Multimodal Image Data
2018
Saliency detecmage processing. Most of the work is adapted to the specific application and available dataset. The present work is about a comparative analysis of saliency detection for multimodal images dataset. There were many researches on the detection of saliency on several types of images, such as multispectral, natural, 3D and so on. This work presents a first focused study on saliency detection on multimodal images. Our database was extracted from acquisitions on cultural heritage wall paintings that contain four modalities UV, IR, Visible and fluorescence. In this paper, the analysis has been performed for many methods on saliency detection. We evaluate the performance of each metho…
Forecasting the vegetation photosynthetic activity over the Sahel: a Model Output Statistics approach
2009
The predictability of the mean August–September photosynthetic activity of vegetation over the Sahel for the period 1982–2002 is explored through a Model Output Statistics approach using ECHAM4.5 retrospective forecasts. Given the poor ability of Atmospheric General Circulation Models (AGCMs) to correctly simulate rainfall over the Sahel, the stress is put on using atmospheric dynamics alone. The mean July–September predicted fields of zonal wind at 600 hPa, and humidity flux at 850 hPa, are selected because of their key role in the West African Monsoon system and their consistency in AGCMs. Coupled modes of NDVI/atmospheric dynamics are extracted using Canonical Correlation Analyses perfor…
Automatic Assessment of Depression Based on Visual Cues: A Systematic Review
2019
International audience; Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics r…
Real-Time Monocular Pose Estimation of 3D Objects Using Temporally Consistent Local Color Histograms
2017
We present a novel approach to 6DOF pose estimation and segmentation of rigid 3D objects using a single monocular RGB camera based on temporally consistent, local color histograms. We show that this approach outperforms previous methods in cases of cluttered backgrounds, heterogenous objects, and occlusions. The proposed histograms can be used as statistical object descriptors within a template matching strategy for pose recovery after temporary tracking loss e.g. caused by massive occlusion or if the object leaves the camera’s field of view. The descriptors can be trained online within a couple of seconds moving a handheld object in front of a camera. During the training stage, our approac…
High-Speed and Robust Monocular Tracking
2015
In this paper, we present a system for high-speed robust monocular tracking (HSRM-Tracking) of active markers. The proposed algorithm robustly and accurately tracks multiple markers at full framerate of current high-speed cameras. For this, we have developed a novel, nearly co-planar marker pattern that can be identified without initialization or incremental tracking. The pattern also encodes a unique ID to identify different markers. The individual markers are calibrated semi-automatically, thus no time-consuming and error-prone manual measurement is needed. Finally we show that the minimal spatial structure of the marker can be used to robustly avoid pose ambiguities even at large distanc…
Real-Time Monocular Segmentation and Pose Tracking of Multiple Objects
2016
We present a real-time system capable of segmenting multiple 3D objects and tracking their pose using a single RGB camera, based on prior shape knowledge. The proposed method uses twist-coordinates for pose parametrization and a pixel-wise second-order optimization approach which lead to major improvements in terms of tracking robustness, especially in cases of fast motion and scale changes, compared to previous region-based approaches. Our implementation runs at about 50–100 Hz on a commodity laptop when tracking a single object without relying on GPGPU computations. We compare our method to the current state of the art in various experiments involving challenging motion sequences and diff…
Motion analysis using the novelty filter
1991
Abstract An original approach to the motion analysis, based on the novelty filter, is proposed. The novelty filter stresses the novelties occurring in a pattern representing an image of the scene under consideration with respect to patterns representing previous images of the same scene, so that visual information about the motion of the objects is obtained. The novelty filter may be implemented by a neural network architecture, taking advantage of the capabilities of massive parallelism, adaptive learning and noise robustness. The novelty filter may learn the entire trajectory of an object, through an incremental learning of a sequence of images capturing the scene, thus emphasizing if the…