Object recognition is an important task in image processing and computer vision. This paper presents a perfect method for object recognition with full boundary detection by combining affine scale invariant feature transform (ASIFT) and a region merging algorithm. ASIFT is a fully affine invariant algorithm that means features are invariant to six affine parameters namely translation (2 paramete...

1474-0346/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.aei.2012.11.001 ⇑ Corresponding author. Tel.: +386 2 229 43 18. E-mail address: [email protected] (P. Podbr When monitoring events on a building site using a system of multiple cameras, it is necessary to establish correspondence between the acquired imaging material. The basic problem when attempting this task i...

We present a mobile image matching application for Android mobile phone called Virtual Telescope to assist users in viewing distant buildings. The application automatically augments photo's field of view by retrieving replacement images and satellite images from the Internet. The application adopts content based image retrieval method and allow user two choices of feature descriptor, namely Sca...

SIFT is an image local feature description algorithm based on scale-space. Due to its strong matching ability, SIFT has many applications in different fields, such as image retrieval, image stitching, and machine vision. After SIFT was proposed, researchers have never stopped tuning it. The improved algorithms that have drawn a lot of attention are PCA-SIFT, GSIFT, CSIFT, SURF and ASIFT. In thi...

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...

SIFT is one of the most popular algorithms to extract points of interest from images. It is a scale+rotation invariant method. As a consequence, if one compares points of interest between two images subject to a large viewpoint change, then only a few, if any, common points will be retrieved. This may lead subsequent algorithms to failure, especially when considering structure and motion or obj...

Matching results depend on a chosen threshold for the Lowe criterion [3], i.e., a maximum value for the ratio of the descriptor distance of the best match to the descriptor distance of the second best match. In our test, we use for ASIFT a maximum Lowe score of 0.73, which is the default value of the provided implementation. For SIFT [3], to put us in the worst situation (many ambiguities, henc...

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. The similarity i...