Search results for "image processing"
showing 10 items of 3285 documents
A GPU-accelerated augmented Lagrangian based L1-mean curvature Image denoising algorithm implementation
2015
This paper presents a graphics processing unit (GPU) implementation of a recently published augmented Lagrangian based L1-mean curvature image denoising algorithm. The algorithm uses a particular alternating direction method of multipliers to reduce the related saddle-point problem to an iterative sequence of four simpler minimization problems. Two of these subproblems do not contain the derivatives of the unknown variables and can therefore be solved point-wise without inter-process communication. Inparticular, this facilitates the efficient solution of the subproblem that deals with the non-convex term in the original objective function by modern GPUs. The two remaining subproblems are so…
High-speed motion estimation of fertilizer granules with Gabor filters
2008
In the context of fertilizer supply reduction, the understanding of the whole centrifugal spreading process became essential. Since few years we focused our research on the determination by image processing of the ejection conditions of flight of the granules, that is the trajectories and ejection angles, used as input data for ballistic flight to predict the fertilizer repartition on the ground. Due to relative high speed of the fertilizer granules (around 40 m.s -1 ), the previous parameters were evaluated using a specific high speed imaging system and image processing based on motion estimation method using Markov Random Fields method (MRFs). Even if the results were good (90% of correct…
Novel threat-based AI strategies that incorporate adaptive data structures for multi-player board games
2016
This paper considers the problem of designing novel techniques for multi-player game playing, in a range of board games and configurations. Compared to the well-known case of two-player game playing, multi-player game playing is a more complex problem with unique requirements. To address the unique challenges of this domain, we examine the potential of employing techniques inspired by Adaptive Data Structures (ADSs) to rank opponents based on their relative threats, and using this information to achieve gains in move ordering and tree pruning. We name our new technique the Threat-ADS heuristic. We examine the Threat-ADS’ performance within a range of game models, employing a number of diffe…
On Addressing the Challenges of Complex Stochastic Games Using “Representative” Moves
2018
The problem of achieving competitive game play in a board game, against an intelligent opponent, is a well-known and studied field of Artificial Intelligence (AI). This area of research has seen major breakthroughs in recent years, particularly in the game of Go. However, popular hobby board games, and particularly Trading Card Games, have unique qualities that make them very challenging to existing game playing techniques, partly due to enormous branching factors. This remains a largely unexamined domain and is the arena we operate in. To attempt to tackle some of these daunting requirements, we introduce the novel concept of “Representative” Moves (RMs). Rather than examine the complete l…
Reduced reference 3D mesh quality assessment based on statistical models
2015
International audience; During their geometry processing and transmission 3D meshes are subject to various visual processing operations like compression, watermarking, remeshing, noise addition and so forth. In this context it is indispensable to evaluate the quality of the distorted mesh, we talk here about the mesh visual quality (MVQ) assessment. Several works have tried to evaluate the MVQ using simple geometric measures, However this metrics do not correlate well with the subjective score since they fail to reflect the perceived quality. In this paper we propose a new objective metric to evaluate the visual quality between a mesh with a perfect quality called reference mesh and its dis…
No-Reference 3D Mesh Quality Assessment Based on Dihedral Angles Model and Support Vector Regression
2016
International audience; 3D meshes are subject to various visual distortions during their transmission and geometrical processing. Several works have tried to evaluate the visual quality using either full reference or reduced reference approaches. However, these approaches require the presence of the reference mesh which is not available in such practical situations. In this paper, the main contribution lies in the design of a computational method to automatically predict the perceived mesh quality without reference and without knowing beforehand the distortion type. Following the no-reference (NR) quality assessment principle, the proposed method focuses only on the distorted mesh. Specific…
Les squelettes : structures d'interaction directe et intuitive avec des formes 3D
2014
The interactions in shape creation graphic applications are far from natural. The user tends to avoid as much as possible such applications and prefer to sketch or model his/her shape.To bridge this widening gap between computer and the general public, we focus on skeletons. They are intuitive shape representation models that we propose to use as direct and intuitive interaction structures.All skeletons suffer from very low quality as shape representation models, concerning the geometry of the shape they capture, the quantity of skeletal noise they contain or the lack of useful organization of their elements. Moreover, some functionalities that must be granted to skeletons are only partiall…
Tissue- and cell-specific expression of metallothionein genes in cadmium- and copper-exposed mussels analyzed by in situ hybridization and RT–PCR
2007
Abstract Metallothioneins (MTs) are metal-inducible proteins that can be used as biomarkers of metal exposure. In mussels two families of MT isoforms (MT10 and MT20) have been characterized. In this study, mussels (Mytilus galloprovincialis) were exposed to 200 ppb Cd and 40 ppb Cu for 2 and 9 days to characterize the tissue and isoform specificity of metal-induced MT expression. Non-radioactive in situ hybridization demonstrated that both MT isoforms were mainly transcribed in digestive tubule epithelial cells, especially in basophilic cells. Weaker MT expression was detected in non-ciliated duct cells, stomach and gill epithelial cells, haemocytes, adipogranular cells, spermatic follicles…
Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks
2015
The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set i…
Hardware implementation of real-time Extreme Learning Machine in FPGA: Analysis of precision, resource occupation and performance
2016
Extreme Learning Machine (ELM) on-chip learning is implemented on FPGA.Three hardware architectures are evaluated.Parametrical analysis of accuracy, resource occupation and performance is carried out. Display Omitted Extreme Learning Machine (ELM) proposes a non-iterative training method for Single Layer Feedforward Neural Networks that provides an effective solution for classification and prediction problems. Its hardware implementation is an important step towards fast, accurate and reconfigurable embedded systems based on neural networks, allowing to extend the range of applications where neural networks can be used, especially where frequent and fast training, or even real-time training…