KEYWORDS: Camera shutters, Digital cameras, Cameras, Digital imaging, Light sources and illumination, Digital image processing, Image processing, 3D image processing, Image resolution, Electrical engineering
To achieve auto exposure in digital cameras, image brightness is widely used because of its direct relationship with exposure value. To use image entropy as an alternative statistic to image brightness, it is required to establish how image entropy changes as exposure value is varied. This paper presents a mathematical proof along with experimental verification results to show that image entropy reaches a maximum value as exposure value is varied by changing shutter speed or aperture size.
This paper presents a real-time implementation of a logo detection and tracking algorithm in video. The motivation of
this work stems from applications on smart phones that require the detection of logos in real-time. For example, one
application involves detecting company logos so that customers can easily get special offers in real-time. This algorithm
uses a hybrid approach by initially running the Scale Invariant Feature Transform (SIFT) algorithm on the first frame in
order to obtain the logo location and then by using an online calibration of color within the SIFT detected area in order
to detect and track the logo in subsequent frames in a time efficient manner. The results obtained indicate that this hybrid
approach allows robust logo detection and tracking to be achieved in real-time.
In this paper, we proposed a nonlinear unmixing matching algorithm using bidirectional reflectance function (BDRF)
and maximum liklihood estimation (MLE). Spectral unmixing algorithms are used to determine the contribution of
multiple substances in a single pixel of a hyperspectral image. For any kind of unmixing model basic approach is to
describe how different substances are combined in a composite spectrum. When a linear reationship exists between the
fractional abundance of the substances, linear unmixing algorithms can determine the endmembers present in that
particular pixel. When the relationship is not linear rather each substance is randomly distributed in a homogeneous way
the mixing is called nonlinear. Though there are plenty of unmixing algorithms based on linear mixing models (LMM)
but very few algorithms have developed to to unmix nonlinear data. We proposed a nonlinear unmixing technique
using BDRF and MLE and tested our algorithm using both synthetic and real hyperspectral data.
In this paper, we proposed a three dimensional matching algorithm using geometrical invariants. Invariant relations
between 3D objects and 2D images for object recognition has been already developed in Ref. . We proposed a
geometrical invariant approach for finding relation between 3D model and stereo image pair. Since the depth
information is lost in a single 2D image, we cannot recognize an object perfectly. By constructing a 3D invariant space
we can represent a 3D model as a set of points in the invariant space. While matching with the 2D image we can draw a
set of invariant light rays in 3D, each ray passing through a 3D invariant model point. If enough rays intersect the model
in 3D invariant space we can assume that the model is present in the image. But for a single image the method is not
that much reliable as the depth information is never considered. In the proposed method, as the matching is performed
using stereo image pair, it is more reliable and accurate.
In this paper, we proposed a three dimensional (3D) line based matching algorithm using multi-dimensional Hausdorff distance. Classical line based recognition techniques using Hausdorff distance deals with two dimensional (2D) models and 2D images. In our proposed 3D line based matching technique, two sets of lines are extracted from a 3D model and 3D image (constructed by stereo imaging). For matching these line sets, we used multidimensional Hausdorff distance minimization technique which requires only to find the translation between the image and the model, whereas most of the model based recognition techniques require to find the rotation, scale and translation variations between the image and the models. A line based approach for model based recognition using four dimensional (4D) Hausdorff distance has been already proposed in Ref. . However, our method requires a 4D Hausdorff distance calculation followed by a 3D Hausdorff distance calculation. In the proposed method, as the matching is performed using 3D line sets, it is more reliable and accurate.