Search results for " Vision"

Complexity reduction in efficient prototype-based classification

2006

Corrigendum to three papers that deal with “Anti”-Bayesian Pattern Recognition [Pattern Recognition]

2014

In the papers 1 (Thomas and Oommen, 2013), 2 (Oommen and Thomas, 2014) and 3 (Thomas and Oommen, 2013), and their associated conference versions cited in those papers, we had introduced a new method of so-called "Anti"-Bayesian Pattern Recognition (PR) which achieved the classification using only a few (sometimes as few as two) points distant from the mean. While the PR strategy, in and of itself, is accurate, the claim that it was based on the Order Statistics (OS) of the distributions of the features is not. The PR and classification results are rather founded on the symmetric quantiles and not on the symmetric OSs. This brief paper corrects the flawed claim presented in those papers. Hig…

An associative link from geometric to symbolic representations in artificial vision

1991

Recent approaches to modelling the reference of internal symbolic representations of intelligent systems suggest to consider a computational level of a subsymbolic kind. In this paper the integration between symbolic and subsymbolic processing is approached in the framework of the research work currently carried on by the authors in the field of artificial vision. An associative mapping mechanism is defined in order to relate the constructs of the symbolic representation to a geometric model of the observed scene.

Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors

2021

AbstractThis study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the whole tumor volume. Additionally, a marginal erosion was applied…

Real-time micro-expression analysis by artificial vision

2022

Human-computer interaction technologies focus more and more on the human being, whether it is on his identity, or on his physical and mental state. Significant progress has been made in the last few decades. However, the study of thoughts and emotions is still an underdeveloped field, but one that has begun to gain considerable interest. In this field, the analysis of facial expressions is the preferred treatment.Unlike a macro-expression, which is visible to the eye, a micro-expression is a type of involuntary facial expression that is extremely rapid and of very low intensity. The computer vision scientific community has been studying ways to automatically recognize micro-expressions usin…

A Segmentation System for Soccer Robot Based on Neural Networks

2000

An innovative technique for segmentation of color images is proposed. The technique implements an approach based on thresholding of the hue histogram and a feed-forward neural network that learns to recognize the hue ranges of meaningful objects. A new function for detecting valleys of the histogram has been devised and tested. A novel blurring algorithm for noise reduction that works effectively when used over hue image has been employed. The reported experimental results show that the technique is reliable and robust even in presence of changing environmental conditions. Extended experimentation has been carried on the framework of the Robot Soccer World Cup Initiative (RoboCup).

<title>Real-time face tracking and recognition for video conferencing</title>

2001

This paper describes a system of vision in real time, allowing to detect automatically the faces presence, to localize and to follow them in video sequences. We verify also the faces identities. These processes are based by combining technique of image processing and methods of neural networks. The tracking is realized with a strategy of prediction-verification using the dynamic information of the detection. The system has been evaluated quantitatively on 8 video sequences. The robustness of the method has been tested on various lightings images. We present also the analysis of complexity of this algorithm in order to realize an implementation in real time on a FPGA based architecture.

A vision system for symbolic interpretation of dynamic scenes using arsom

2001

We describe an artificial high-level vision system for the symbolic interpretation of data coming from a video camera that acquires the image sequences of moving scenes. The system is based on ARSOM neural networks that learn to generate the perception-grounded predicates obtained by image sequences. The ARSOM neural networks also provide a three-dimensional estimation of the movements of the relevant objects in the scene. The vision system has been employed in two scenarios: the monitoring of a robotic arm suitable for space operations, and the surveillance of an electronic data processing (EDP) center.

OmniFlowNet: a Perspective Neural Network Adaptation for Optical Flow Estimation in Omnidirectional Images

2021

International audience; Spherical cameras and the latest image processing techniques open up new horizons. In particular, methods based on Convolutional Neural Networks (CNNs) now give excellent results for optical flow estimation on perspective images. However, these approaches are highly dependent on their architectures and training datasets. This paper proposes to benefit from years of improvement in perspective images optical flow estimation and to apply it to omnidirectional ones without training on new datasets. Our network, OmniFlowNet, is built on a CNN specialized in perspective images. Its convolution operation is adapted to be consistent with the equirectangular projection. Teste…

A 4K-Input High-Speed Winner-Take-All (WTA) Circuit with Single-Winner Selection for Change-Driven Vision Sensors

2019

Winner-Take-All (WTA) circuits play an important role in applications where a single element must be selected according to its relevance. They have been successfully applied in neural networks and vision sensors. These applications usually require a large number of inputs for the WTA circuit, especially for vision applications where thousands to millions of pixels may compete to be selected. WTA circuits usually exhibit poor response-time scaling with the number of competitors, and most of the current WTA implementations are designed to work with less than 100 inputs. Another problem related to the large number of inputs is the difficulty to select just one winner, since many competitors ma…