scholarly journals Online learning of color transformation for interactive object recognition under various lighting conditions

2004 ◽  
Author(s):  
Y. Makihara ◽  
Y. Shirai ◽  
N. Shimada
Keyword(s):  
Online Learning ◽  
Object Recognition ◽  
Lighting Conditions ◽  
Color Transformation

Adaptation to change of lighting conditions for interactive object recognition

2007 ◽  
Vol 38 (4) ◽  
pp. 52-62
Author(s):  
Yasushi Makihara ◽  
Masao Takizawa ◽  
Yoshiaki Shirai ◽  
Nobutaka Shimada
Keyword(s):  
Object Recognition ◽  
Lighting Conditions

scholarly journals Adaptive Computer Vision: Online Learning for Object Recognition

2004 ◽  
pp. 447-454 ◽  
Author(s):  
Holger Bekel ◽  
Ingo Bax ◽  
Gunther Heidemann ◽  
Helge Ritter
Keyword(s):  
Computer Vision ◽  
Online Learning ◽  
Object Recognition

Bayesian Online Learning for MEC Object Recognition Systems

2020 ◽  
Author(s):  
Apostolos Galanopoulos ◽  
Jose A. Ayala-Romero ◽  
George Iosifidis ◽  
Douglas J. Leith
Keyword(s):  
Online Learning ◽  
Object Recognition ◽  
Recognition Systems

scholarly journals Identify Occurrence of Substance Object of a Certain Classification in Fractional Videos and Pictures

2020 ◽  
Vol 9 (3) ◽  
pp. 3728-3731
Keyword(s):  
Computer Vision ◽  
Online Learning ◽  
Object Recognition ◽  
Object Detection ◽  
New Method ◽  
Learning Method ◽  
Dynamic Object ◽  
Geometric Shapes ◽  
New Learning ◽  
Detection And Recognition

Object detection and recognition are the meta-heuristic problems in computer vision. Practically usable dynamic object recognition methods are still unavailable. A new method was proposed which improves over existing methods in every stage. In that addition features like geometric shapes, ellipsis are added. An heuristic codebook was proposed of good generalization and discriminative properties, enabling multipath interferences mechanisms on propagation of1 conditional livelihood. A new learning method also proposed which is capable of online learning

FINE-GRAINED AND LAYERED OBJECT RECOGNITION

2012 ◽  
Vol 26 (02) ◽  
pp. 1255006 ◽  
Author(s):  
YANG WU ◽  
NANNING ZHENG ◽  
YUANLIU LIU ◽  
ZEJIAN YUAN
Keyword(s):  
Online Learning ◽  
Object Recognition ◽  
Learning Algorithm ◽  
Structured Prediction ◽  
Generic Model ◽  
Fine Grained ◽  
Relative Localization ◽  
Object Categories ◽  
Online Learning Algorithm ◽  
Semantic Object

This paper presents a novel research on promoting the performance and enriching the functionalities of object recognition. Instead of simply fitting various data to a few predefined semantic object categories, we propose to generate proper results for different object instances based on their actual visual appearances. The results can be fine-grained and layered categorization along with absolute or relative localization. We present a generic model based on structured prediction and an efficient online learning algorithm to solve it. Experiments on a new benchmark dataset demonstrate the effectiveness of our model and its superiority against traditional recognition methods.

scholarly journals Object Recognition under Various Lighting Conditions

2003 ◽  
pp. 899-906 ◽  
Author(s):  
Yasushi Makihara ◽  
Masao Takizawa ◽  
Yoshiaki Shirai ◽  
Nobutaka Shimada
Keyword(s):  
Object Recognition ◽  
Lighting Conditions

scholarly journals COMPARISON OF BACKGROUND SUBTRACTION, SOBEL, ADAPTIVE MOTION DETECTION, FRAME DIFFERENCES, AND ACCUMULATIVE DIFFERENCES IMAGES ON MOTION DETECTION

SINERGI ◽  
2018 ◽  
Vol 22 (1) ◽  
pp. 51
Author(s):  
Dara Incam Ramadhan ◽  
Indah Permata Sari ◽  
Linna Oktaviana Sari
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Motion Detection ◽  
Background Subtraction ◽  
Detection Method ◽  
Detection Methods ◽  
Lighting Conditions ◽  
Frame Difference ◽  
Best Value ◽  
Adaptive Motion

Nowadays, digital image processing is not only used to recognize motionless objects, but also used to recognize motions objects on video. One use of moving object recognition on video is to detect motion, which implementation can be used on security cameras. Various methods used to detect motion have been developed so that in this research compared some motion detection methods, namely Background Substraction, Adaptive Motion Detection, Sobel, Frame Differences and Accumulative Differences Images (ADI). Each method has a different level of accuracy. In the background substraction method, the result obtained 86.1% accuracy in the room and 88.3% outdoors. In the sobel method the result of motion detection depends on the lighting conditions of the room being supervised. When the room is in bright condition, the accuracy of the system decreases and when the room is dark, the accuracy of the system increases with an accuracy of 80%. In the adaptive motion detection method, motion can be detected with a condition in camera visibility there is no object that is easy to move. In the frame difference method, testing on RBG image using average computation with threshold of 35 gives the best value. In the ADI method, the result of accuracy in motion detection reached 95.12%.

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