نتایج جستجو برای: maximally stable extremal regions

تعداد نتایج: 621457  

2005
Sandra Ober Helmut Neuschmied Horst Bischof Christian Wessely

This contribution presents a video analysis system which provides automatic content analysis and enables manual and semi-automatic annotation. In particular, we focus on object recognition and visual video indexing. We briefly review the current state of the art based on interest point extraction (e.g. Harris points, Maximally Stable Extremal Regions) and calculation of invariant descriptors ar...

2016
Linfeng Yang Xinyu Shen

Here we developed a MATLAB based Graphical User Interface for people to check the information of desired books in real-time. The GUI allows user to take photos of the book cover. Then it will automatically detect features of the input image based on MSER algorithm, then it will filter out non-text features based on morphological difference between text and non-text regions. In order to further ...

2016
Andrzej Sluzek

Visual identification of complex images (e.g. images of food) remains a challenging problem. In particular, contentbased visual information retrieval (CBVIR) methods, which seem a natural choice for such tasks, are often constrained by specific characteristics of the images of interest and (possibly) other practical requirements. In this paper, a novel CBVIR approach to automatic food identific...

2007
Sheng Chen

Adaptive beamforming is capable of separating user signals transmitted on the same carrier frequency, and thus provides a practical means of supporting multiusers in a space-division multipleaccess scenario. Moreover, for the sake of further improving the achievable bandwidth efficiency, highthroughput quadrature amplitude modulation (QAM) schemes have becomes popular in numerous wireless netwo...

2013
Zijiang Song Reinhard Klette

This paper evaluates 2D feature detection methods with respect to invariance and efficiency properties. The studied feature detection methods are as follows: Speeded Up Robust Features, Scale Invariant Feature Transform, Binary Robust Invariant Scalable Keypoints, Oriented Binary Robust Independent Elementary Features, Features from Accelerated Segment Test, Maximally Stable Extremal Regions, B...

2015
N. F. Attia

Trajectory tracking is used to keep tracks objects. Trajectory tracking control is used to affect desired trajectories of a device, human and anything can move. In order to precisely track specified trajectories, or be able to follow more general trajectories, many tracking control algorithms have been proposed, but still there is some problems of tracking trajectory in its application. This pa...

2014
Michael P. Kim

Chav́ın de Huántar is an archaeological site of critical interest for intepreting the Andean archaeological record. In this paper, we present our work on developing an object recognition system to classify Chav́ın pot sherds by decorative impressions. We trained and evaluated MSER and SURF based classification systems on a small labeled set of replica sherds and achieved classification accuracy o...

2014
Man Hee Lee In Kyu Park

Local feature descriptors are widely used in many computer vision applications. Over the past couple of decades, several local feature descriptors have been proposed which are robust to challenging conditions. Since they show different characteristics in different environment, it is necessary to evaluate their performance in an intensive and consistent manner. However, there has been no relevan...

Journal: :SIAM J. Imaging Sciences 2009
Jean-Michel Morel Guoshen Yu

If a physical object has a smooth or piecewise smooth boundary, its images obtained by cameras in varying positions undergo smooth apparent deformations. These deformations are locally well approximated by affine transforms of the image plane. In consequence the solid object recognition problem has often been led back to the computation of affine invariant image local features. Such invariant f...

2008
Robert T. Collins Weina Ge

Motivation A new interest region operator and feature descriptor called Center-Surround Distribution Distance (CSDD) is based on comparing feature distributions between a central foreground region and a surrounding ring of background pixels. In addition to finding light(dark) blobs surrounded by a dark(light) background, CSDD also detects blobs with arbitrary color distribution that “stand out”...

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