Search results for "computer vision"
showing 10 items of 2353 documents
Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination
2015
This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive…
3D inter-subject medical image registration by scatter search
2005
Image registration is a very active research area in computer vision, namely it is used to find a transformation between two images taken under different conditions. Point matching is an image registration approach based on searching for the right pairing of points between the two images. From this matching, the registration transformation we are searching, can be inferred by means of numerical methods. In this paper, we propose a scatter search (SS) algorithm to solve the matching problem. SS is a hybrid metaheuristic with a good trade-off between search space diversification and intensification. On the one hand, diversity is basically introduced from a population-based approach where syst…
Digital Device Usage Time and Ocular Symptoms during COVID-19 Pandemic
2021
Bakalaura darbs ir uzrakstīts angļu valodā uz 25 lappusēm. Tas satur 10 attēlus, 6 tabulas un 22 atsauces uz literatūras avotiem. Šī pētījuma galvenais mērķis bija novērtēt kā digitālo ierīču lietošanas izmaiņas ikdienā pirms Covid-19 pandēmijas un pandēmijas laikā Itālijā ietekmē acu nogurumu. Digitālo ierīču ietekme uz acīm tika novērtēta, apkopojot pētījuma dalībnieku atbildes uz anketas jautājumiem. Digitālo ierīču izmantošana mūsdienās ir plaši izplatīta, tās ir jāizmanto, bet ievērojot nepieciešamos piesardzības pasākumus, lai neradītu traucējumus redzes un psihiskajā sistēmās.
Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment
2013
This paper presents a novel stereo SLAM framework, where a robust loop chain matching scheme for tracking keypoints is combined with an effective frame selection strategy. The proposed approach, referred to as selective SLAM (SSLAM), relies on the observation that the error in the pose estimation propagates from the uncertainty of the three-dimensional points. This is higher for distant points, corresponding to matches with low temporal flow disparity in the images. Comparative results based on the reference KITTI evaluation framework show that SSLAM is effective and can be implemented efficiently, as it does not require any loop closure or bundle adjustment.
The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams
2023
1. Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labour-intensive sampling methods that result in data of low temporal and spatial resolution. 2. To address this need, we developed a new technology, the Riverine Organism Drift Imager (RODI), which combines in situ imaging with machine-learning classification. This technique expands on the traditional methodology by replacing the collection cup of a drift net with a camera system that continuously images r…
Two-step cross correlation-based algorithm for motion estimation applied to fertilizer granules' motion during centrifugal spreading
2011
Imaging systems are progressing in both accuracy and ro- bustness, and their use in precision agriculture is increasing accordingly. One application of imaging systems is to understand and control the cen- trifugal fertilizing spreading process. Predicting the spreading pattern on the ground relies on an estimation of the trajectories and velocities of ejected granules. The algorithms proposed to date have shown low ac- curacy, with an error rate of a few pixels. But a more accurate estimation of the motion of the granules can be achieved. Our new two-step cross- correlation-based algorithm is based on the technique used in particle image velocimetry (PIV), which has yielded highly accurate…
A Performance Evaluation of Fusion Techniques for Spatio-Temporal Saliency Detection in Dynamic Scenes
2013
International audience; Visual saliency is an important research topic in computer vision applications, which helps to focus on regions of interest instead of processing the whole image. Detecting visual saliency in still images has been widely addressed in literature. However, visual saliency detection in videos is more complicated due to additional temporal information. A spatio-temporal saliency map is usually obtained by the fusion of a static saliency map and a dynamic saliency map. The way both maps are fused plays a critical role in the accuracy of the spatio-temporal saliency map. In this paper, we evaluate the performances of different fusion techniques on a large and diverse datas…
Modified total variation regularization using fuzzy complement for image denoising
2015
In this paper, we propose a denoising algorithm based on the Total Variation (TV) model. Specifically, we associate to the regularization term of the Rodin-Osher-Fatimi (ROF) functional a small weight whenever denoising is performed in edge and texture regions, which means less regularization and more details preservation. On the other hand, a large weight is associated if the region being filtered is smooth which means noise will be well suppressed. The weight computation is inspired from the fuzzy edge complement. Experiments on well-known images and comparison with state of the art denoising algorithms demonstrate that the proposed method not only presents good denoising performance but …
A fully automatic method for biological target volume segmentation of brain metastases
2016
Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of…
AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET
2011
International audience; Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, two new methods for the detection of exudates are presented. The methods do not require a lesion training set so the need to ground-truth data is avoided with significant time savings and independence from human error. We evaluate our algorithm with a new publicly available dataset from various ethnic groups and levels of DME. Also, we compare our results with two recent exudate segmentation algorithms on the same dataset. In all of …