This paper describes a vision-based system that is able to automatically recognize deformable objects, to estimate their pose, and to select suitable picking points. A hierarchical self-organized neural network is used to segment color images based on texture information. A morphological analysis allows the recognition of the objects and the picking points extraction. The proposed approach is useful in all of the situations where texture properties are significant for detecting regions of interest on deformable objects. Several tests on a large number of images, acquired in real operative working conditions, demonstrate the effectiveness of the system
The realistic representation of deformations is still an active area of research, especially for def...
A method for deformable shape detection and recognition is described. Deformable shape templates are...
Deformable models have been studied in image analysis over the last decade and used for recognition ...
The human\u27s innate ability to process information garnered from a visual scene has no parallel in...
Abstract. The paper discusses an innovative approach to acquire and learn de-formable objects ’ prop...
The focus of this thesis is on the emerging technology known as Neural Networks which has recently b...
A robust and accurate object recognition tool is presented in this paper. The paper introduced the u...
Performing tasks with a robot hand often requires a complete knowledge of the manipulated object, in...
The paper discusses a novel unsupervised learning approach for tracking deformable objects manipulat...
This article presents a methodology for the haptic perception of contour shapes of almost planar obj...
A new and systematic approach to machine vision-based robot manipulation of deformable (non-rigid) l...
International audienceThis paper proposes a unified vision-based manipulation framework using image ...
Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solv...
A new and systematic approach to machine vision-based robot manipulation of deformable (non-rigid) l...
Abstract — We present a novel method for classifying and estimating the categories and poses of defo...
The realistic representation of deformations is still an active area of research, especially for def...
A method for deformable shape detection and recognition is described. Deformable shape templates are...
Deformable models have been studied in image analysis over the last decade and used for recognition ...
The human\u27s innate ability to process information garnered from a visual scene has no parallel in...
Abstract. The paper discusses an innovative approach to acquire and learn de-formable objects ’ prop...
The focus of this thesis is on the emerging technology known as Neural Networks which has recently b...
A robust and accurate object recognition tool is presented in this paper. The paper introduced the u...
Performing tasks with a robot hand often requires a complete knowledge of the manipulated object, in...
The paper discusses a novel unsupervised learning approach for tracking deformable objects manipulat...
This article presents a methodology for the haptic perception of contour shapes of almost planar obj...
A new and systematic approach to machine vision-based robot manipulation of deformable (non-rigid) l...
International audienceThis paper proposes a unified vision-based manipulation framework using image ...
Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solv...
A new and systematic approach to machine vision-based robot manipulation of deformable (non-rigid) l...
Abstract — We present a novel method for classifying and estimating the categories and poses of defo...
The realistic representation of deformations is still an active area of research, especially for def...
A method for deformable shape detection and recognition is described. Deformable shape templates are...
Deformable models have been studied in image analysis over the last decade and used for recognition ...