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We present a fragmented piece reconstruction method that enables all non-overlapping and randomly placed fragmented pieces to be identified and gathered piece-by-piece to be placed in the corresponding position. The proposed method can be applied in many other fields such as industrial automation, robot vision, archeology, and art restoration.
It is possible to associate a highly constrained subset of relative 6 DoF poses between two 3D shapes, as long as the local surface orientation, the normal vector, is available at every surface point. Local shape features can be used to find putative point correspondences between the models due to their ability to handle noisy and incomplete data. However, this correspondence set is usually contaminated...
In many countries, robots and automation techniques are being introduced in agriculture farms to reduce the human labour and to improve the yield. However, such technological initiatives are still lacking in India, although it is the leading producer of many vegetables and fruits, for example, coconuts. Some of the activities carried out in a coconut farm that requires human labor are coconut dehusking,...
The paper presents a model and an algorithm for recognizing and handling articulated objects using industrial robots. The model is based on the skeleton of the object and it is used for recognition and to associate multiple grasping positions for robot handling. The object skeleton is a shape descriptor which preserves the topology of the object, even if the shape is changing. The model can associate...
A method for classifying objects into categories and indexing is proposed to implement object recognition. The relational measurements such as the distance between two points, color comparison is encoded by the attributed relational graph (ARG) representation to provide one-to-one correspondence between models and object features. If the contour is traversed counterclockwise, a sequence can be formed...
Robotic graspable object recognition is a crucial ingredient in many exciting autonomous manipulation applications. However, identifying complex image features from limited data remains largely unsolved. In this paper, we leverage the advantages of two kinds of feature representation approaches, kernel descriptors and deep neural networks, to present a novel hierarchical feature learning framework...
This paper describes a system that can find and lift a specific object in a bin containing piled objects. Such a task is ubiquitous in our daily life, for example, in finding a small toy in a toy box or in finding a stationary in a drawer. To efficiently achieve this task, it is necessary to recognize the object placements with consideration of occlusions and to plan a proper hand motions for lifting...
Humans can recognise objects under partial occlusion. Machine-based approaches cannot reliably recognise objects and scenes in the presence of occlusion. This paper investigates the use of the elastic net hierarchical MAX (En-HMAX) model to handle occlusions. Our experiments show that the En-HMAX model achieves an accuracy of ∼70%, when ∼50% artificial occlusions are applied to the centre of the visual...
This paper designs an algorithm for the moving object recognition based on support vector machine (SVM) in order to identify and classify the moving objects accurately. In view of the advantages of support vector machine in small sample, nonlinear, and high dimensional pattern recognition, a classifier is constructed based on support vector machine (SVM) is constructed. A feature vector is presented...
Object recognition or object's category recognition under varying conditions is one of the most astonishing capabilities of human visual system. The scientists in computer vision have been trying for decades to reproduce this ability by implementing algorithms and providing computers with appropriate tools. Hence, several intelligent systems have been proposed. To act in this field, numerous approaches...
The objective of this paper is to present an algorithm for processing infrared images and accomplishing automatic detection and path tracking of moving subjects with fever. The detection is based on two main features: the distinction between the geometry of a human face and other objects in the field of view of the camera and the temperature of the radiating object. These features are used for tracking...
In the last few years, Convolutional Neural Networks (CNNs) had become the default paradigm to address classification problems, specially, but not only, in image recognition. This is mainly due to the high success rate that they provide. Despite there currently exist approaches that apply deep learning to the 3D recognition problem, they are either too slow for online uses or too error prone. To fill...
The proposed work focused on detection of shapes of toy and its count in the particular area to segregate the items manufactured in the toy industry for packing. The identification of shapes using Ramer-Douglas-Peucker algorithm in Phython language is a technique implemented with the help of open CV image processing tool. The prototype of the model is developed by giving a sample input image with...
A novel approach to extract and retrieve furniture items from an image database and online websites which includes multiple furniture items, and then find the similar items from the database. The image could be taken using phone cameras or downloaded from online websites or from shopping malls. A real time application is developed in android phone to find the similar images of furniture taken using...
Feature descriptors have been playing an important role in many computer vision problems, such as image matching and object recognition. While classic descriptors using texture or shape as a single cue of descriptive information have been proved to be successful, recently, several approaches have been proposed introducing the combination of multiple cues to increase descriptive power and robustness...
This study aims to digitalize the Poppelreuter's overlapping figures test. The Poppelreuter's test used in psychology and neurology to assess visual perceptual function. Its recent modification performed with pencil and paper. Replacing the pencil and paper by the tablet computer equipped with the stylus, allows recording and analyzing fine motor motions observed during the test. On the one hand,...
The ubiquity increase of production volumes leads to the introduction of automatic conveyors and assembly lines. An integral part of it is the robotic units, equipped with technical vision. System of automatic recognition is used therein as a machine vision. The main part of any SAR is a vector of image features. During the past five years, various of researchers have published many algorithms for...
This paper presents a new large scale dataset targeting evaluation of local shape descriptors and 3d object recognition algorithms. The dataset consists of point clouds and triangulated meshes from 292 physical scenes taken from 11 different views, a total of approximately 3204 views. Each of the physical scenes contain 10 occluded objects resulting in a dataset with 32040 unique object poses and...
Tactile data and kinesthetic cues are two important sensing sources in robot object recognition and are complementary to each other. In this paper, we propose a novel algorithm named Iterative Closest Labeled Point (iCLAP) to recognize objects using both tactile and kinesthetic information. The iCLAP first assigns different local tactile features with distinct label numbers. The label numbers of the...
Most modern approaches for tactile object recognition with robotic hands do not use proprioceptive data. In those that do, a limited number of objects with similar shapes is recognized. Furthermore, Self-Organizing Maps (SOM) based on raw values of joint angles/torques are frequently implemented which requires large sets of training data. In this paper, we present an approach based only on joint angles...
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