scholarly journals Object Tracking based on Fuzzy Color Blobs

2012 ◽  
Vol 22 ◽  
pp. 27-34
Author(s):  
Francisco-Javier Montecillo-Puente ◽  
Victor Ayala-Ramirez
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Object Representation ◽  
Spatial Relationships ◽  
Interest Points ◽  
Articulated Objects ◽  
Specific Object

One of the mayor goals in computer vision is object representation. Object representation aims to determine a set of features that best represents a specific object in an image, for example interest points, edges, color and texture. However, objects are generally composed of several regions containing different information which is more or less convenient to be represented by one of these features. Furthermore, each of these regions could be static or moving with respect to each other. In this sense, this paper presents an object representation based on fuzzy color blobs and spatial relationships among them. This approach of object representation is used to track rigid and articulated objects.

Automated Design of Image Operators that Detect Interest Points

2008 ◽  
Vol 16 (4) ◽  
pp. 483-507 ◽  
Author(s):  
Leonardo Trujillo ◽  
Gustavo Olague
Keyword(s):  
Computer Vision ◽  
Search Space ◽  
Search Problem ◽  
Interest Points ◽  
Fitness Evaluation ◽  
Modern Computer ◽  
New Perspective ◽  
Processing Techniques ◽  
Point Detection ◽  
Optimization Search

This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

A Model of Shape Recognition and Categorisation

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 132-132
Author(s):  
S Edelman ◽  
S Duvdevani-Bar
Keyword(s):  
Computer Vision ◽  
Visual Perception ◽  
Visual System ◽  
Shape Recognition ◽  
Target Object ◽  
Spatial Relationships ◽  
Biological Vision ◽  
Making Sense ◽  
Brain Sciences ◽  
Visual Tasks

To recognise a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. It is possible to counter the influence of these factors, by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Routine visual tasks, however, typically require not so much recognition as categorisation, that is making sense of objects not seen before. Despite persistent practical difficulties, theorists in computer vision and visual perception traditionally favour the structural route to categorisation, according to which forming a description of a novel shape in terms of its parts and their spatial relationships is a prerequisite to the ability to categorise it. In comparison, we demonstrate that knowledge of instances of each of several representative categories can provide the necessary computational substrate for the categorisation of their new instances, as well as for representation and processing of radically novel shapes, not belonging to any of the familiar categories. The representational scheme underlying this approach, according to which objects are encoded by their similarities to entire reference shapes (S Edelman, 1997 Behavioral and Brain Sciences in press), is computationally viable, and is readily mapped onto the mechanisms of biological vision revealed by recent psychophysical and physiological studies.

Hybrid region and interest points-based active contour for object tracking

2013 ◽  
Vol 7 ◽  
pp. 5879-5899 ◽  
Author(s):  
W. Aitfares ◽  
E.H. Bouyakhf ◽  
A. Herbulot ◽  
F. Regragui ◽  
M. Devy
Keyword(s):  
Object Tracking ◽  
Active Contour ◽  
Interest Points ◽  
Hybrid Region

Comparison of Natural Feature Descriptors for Rigid-Object Tracking for Real-Time Augmented Reality

2014 ◽  
Author(s):  
Francely Franco Bermudez ◽  
Christian Santana Diaz ◽  
Sheneeka Ward ◽  
Rafael Radkowski ◽  
Timothy Garrett ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Object Tracking ◽  
Real Time ◽  
Virtual Object ◽  
Interest Points ◽  
Rigid Object ◽  
Binary Descriptors ◽  
Rigid Objects ◽  
Natural Feature ◽  
Feature Descriptors

This paper presents a comparison of natural feature descriptors for rigid object tracking for augmented reality (AR) applications. AR relies on object tracking in order to identify a physical object and to superimpose virtual object on an object. Natural feature tracking (NFT) is one approach for computer vision-based object tracking. NFT utilizes interest points of a physcial object, represents them as descriptors, and matches the descriptors against reference descriptors in order to identify a phsical object to track. In this research, we investigate four different natural feature descriptors (SIFT, SURF, FREAK, ORB) and their capability to track rigid objects. Rigid objects need robust descriptors since they need to describe the objects in a 3D space. AR applications are also real-time application, thus, fast feature matching is mandatory. FREAK and ORB are binary descriptors, which promise a higher performance in comparison to SIFT and SURF. We deployed a test in which we match feature descriptors to artificial rigid objects. The results indicate that the SIFT descriptor is the most promising solution in our addressed domain, AR-based assembly training.

scholarly journals An Efficient and Robust Multi-Object Recognition and Tracking Algorithm using Mask Region based Convolution Neural Network (R-CNN)

2019 ◽  
Vol 8 (9) ◽  
pp. 607-613
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Object Recognition ◽  
Object Tracking ◽  
Ground Truth ◽  
Benchmark Dataset ◽  
Tracking Algorithm ◽  
Multiple Objects ◽  
High Quality ◽  
Extensive Simulation

Presently, Multi-Object tracking (MOT) is mainly applied for predicting the positions of many predefined objects across many successive frames with the provided ground truth position of the target in the first frame. The area of MOT gains more interest in the area of computer vision because of its applicability in various fields. Many works have been presented in recent years that intended to design a MOT algorithm with maximum accuracy and robustness. In this paper, we introduce an efficient as well as robust MOT algorithm using Mask R-CNN. The usage of Mask R-CNN effectively identifies the objects present in the image while concurrently creating a high-quality segmentation mask for every instance. The presented MOT algorithm is validated using three benchmark dataset and the results are extensive simulation. The presented tracking algorithm shows its efficiency to track multiple objects precisely

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