The use of low-level photogrammetry is very broad, and studies in this field are conducted in
many aspects. Most research and applications are based on image data acquired during the …

Feature transformation and key-point identification is the solution to many local feature
descriptors. One among such descriptor is the Scale Invariant Feature Transform (SIFT). A …

The active appearance model (AAM), one of the most effective facial feature localization
methods, is widely used in frontal facial expression recognition. However, non‐frontal facial …

Phase correlation (PC) is a well-established image matching algorithm. It is robust to the
variation of image contrast and brightness, but whether it is invariant to local illumination …

Feature refers to some relevant information which is present on images or faces. Feature
extraction used to extract those features from the face. Among that bulk of keypoints, only …

In this paper, we propose a new image descriptor, that is, a rotation invariant histogram of
oriented gradients (RIHOG). RIHOG overcomes a disadvantage of the histogram of oriented …

Illumination variation is one of the major problems in multitemporal earth observation (EO)
image matching. Despite much research has been focused on illumination invariant image …

The scale invariant feature transform (SIFT), which was proposed by David Lowe, is a
powerful method that extracts and describes local features called keypoints from images …

Most commercially successful face recognition systems combine information from multiple
sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various …

Embarking on the journey of rephotography, capturing a contemporary image from the
vantage point of a historical counterpart and registering them, is a formidable challenge …