Template Matching in Digital Images with Swarm Intelligence

2015 ◽  
pp. 6041-6049
Author(s):  
Hugo Alberto Perlin ◽  
Chidambaram Chidambaram ◽  
Heitor Silvério Lopes
Keyword(s):  
Swarm Intelligence ◽  
Template Matching ◽  
Digital Images

An Improved Artificial Bee Colony Algorithm for the Object Recognition Problem in Complex Digital Images Using Template Matching

2010 ◽  
Vol 1 (2) ◽  
pp. 54-70 ◽  
Author(s):  
Chidambaram Chidambaram ◽  
Heitor Silvério Lopes
Keyword(s):  
Object Recognition ◽  
Template Matching ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Digital Images ◽  
Color Images ◽  
Recognition Problem ◽  
Reference Image ◽  
Bee Colony ◽  
Collective Foraging

In this paper, the authors present an improved Artificial Bee Colony Algorithm (ABC) for the object recognition problem in complex digital images. The ABC is a new metaheuristics approach inspired by the collective foraging behavior of honey bee swarms. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene that may contain noise and changes in brightness and contrast. First, several search strategies were tested to find the most appropriate. Next, many experiments were done using complex digital grayscale and color images. Results are analyzed and compared with other algorithms through Pareto plots and graphs that show that the improved ABC was more efficient than the original ABC.

scholarly journals A NOVEL FACE DETECTION AND TRACKING ALGORITHM IN REAL- TIME VIDEO SEQUENCES

2013 ◽  
pp. 25-29
Author(s):  
NAGAPRIYA KAMATH K ◽  
ASHWINI HOLLA ◽  
SUBRAMANYA BHAT
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Template Matching ◽  
Digital Images ◽  
Color Space ◽  
Color Model ◽  
Hsv Color Space ◽  
Detection And Tracking ◽  
Skin Color Model ◽  
Human Faces

Face detection is a image processing technology that determines the location and size of human faces in digital images or video. This module precedes face recognition systems that plays an important role in applications such as video surveillance, human computer interaction and so on. This proposed work focuses mainly on multiple face detection technique, taking into account the variations in digital images or video such as face pose, appearances and illumination. The work is based on skin color model in YCbCr and HSV color space. First stage of this proposed method is to develop a skin color model and then applying the skin color segmentation in order to specify all skin regions in an image. Secondly, a template matching is done to assure that the segmented image does not contain any non-facial part. This algorithm works to be robust and efficient.

An Improved Artificial Bee Colony Algorithm for the Object Recognition Problem in Complex Digital Images Using Template Matching

2012 ◽  
pp. 141-157 ◽  
Author(s):  
Chidambaram Chidambaram ◽  
Heitor Silvério Lopes
Keyword(s):  
Object Recognition ◽  
Template Matching ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Digital Images ◽  
Color Images ◽  
Recognition Problem ◽  
Reference Image ◽  
Bee Colony ◽  
Collective Foraging

In this paper, the authors present an improved Artificial Bee Colony Algorithm (ABC) for the object recognition problem in complex digital images. The ABC is a new metaheuristics approach inspired by the collective foraging behavior of honey bee swarms. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene that may contain noise and changes in brightness and contrast. First, several search strategies were tested to find the most appropriate. Next, many experiments were done using complex digital grayscale and color images. Results are analyzed and compared with other algorithms through Pareto plots and graphs that show that the improved ABC was more efficient than the original ABC.

TEMPLATE MATCHING IN DIGITAL IMAGES USING A COMPACT GENETIC ALGORITHM WITH ELITISM AND MUTATION

2010 ◽  
Vol 19 (01) ◽  
pp. 91-106 ◽  
Author(s):  
RAFAEL R. DA SILVA ◽  
CARLOS R. ERIG LIMA ◽  
HEITOR S. LOPES
Keyword(s):  
Genetic Algorithm ◽  
Template Matching ◽  
Digital Images ◽  
Search Algorithm ◽  
Computational Cost ◽  
The Other ◽  
Reference Image ◽  
Memory Consumption ◽  
Compact Genetic Algorithm ◽  
Overall Performance

The emCGA is a new extension of the compact genetic algorithm (CGA) that includes elitism and a mutation operator. These improvements do not increase significantly the computational cost or the memory consumption and, on the other hand, increase the overall performance in comparison with other similar works. The emCGA is applied to the problem of object recognition in digital images. The objective is to find a reference image (template) in a landscape image, subject to distortions and degradation in quality. Two models for dealing with the images are proposed, both based on the intensity of light. Several experiments were done with reference and landscape images, under different situations. The emCGA was compared with an exhaustive search algorithm and another CGA proposed in the literature. The emCGA was found to be more efficient for this problem, when compared with the other algorithms. We also compared the two proposed models for the object. One of them is more suitable for images with rich details, and the other for images with low illumination level. Both models seem to perform equally in the presence of distortions. Overall, results suggested the efficiency of emCGA for template matching in images and encourages future developments.

Sign in / Sign up

Export Citation Format

Share Document