IJIVPTA (International Journal of Image and Video Processing: Theory and Application) Journal is intended for Scientists, Researchers, Academicians and Engineers working on multidisciplinary field of image and video processing. The scope of the journal covers a broad spectrum of image and video processing.
Imagers based on focal plane arrays (FPA) risk introducing in-band and out-of-band spurious response, or aliasing, due to undersampling. This can make high-level discrimination tasks such as recognition and identification much more difficult. To overcome this problem, three-chip color charge coupled device (CCD) cameras typically offset one CCD by 1/2 pixel with respect to the other two. Analogously, monochrome imagers including infrared can use microscan (or dither) to reduce aliasing. This...
Synopsis: Homography transform in Fourier spectrum with application to object recognition. Ideally, recognition of objects should be projection, scale, translation and rotation invariant, just as they are in human vision. This, however, is a very complex problem, since numerous times an object is occluded and many objects rarely appear the same twice, due to different camera/observer positions, variable lighting or object motion. Our goal in this regard is to investigate autonomous object recognition in unconstrained environments by means of outlines of the objects, which we will refer to as the contours. One of the reasons for the popularity of contour-based analysis techniques is that edge detection constitutes an important aspect of shape recognition by the human visual system. The main motivation behind this work is that 2-D homography may overcome the problem of noise sensitivity and boundary variations.
Methods for super-resolution (SR) can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database). In this paper we propose a unified framework for combining these two families of methods. We further show how this combined approach can be applied to obtain super resolution from as little as a single image (with no database or prior examples). Our approach is based on the observation that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Recurrence of patches within the same image scale (at subpixel misalignments) gives rise to the classical super-resolution, whereas recurrence of patches across different scales of the same image gives rise to example-based super-resolution.
Image alignment is the process of matching one image called template (let's denote it as T) with another image, I (see the above figure). There are many applications for image alignment, such as tracking objects on video, motion analysis, and many other tasks of computer vision. In 1981, Bruse D. Lucas and Takeo Kanade proposed a new technique that used image intensity gradient information to search for the best match between a template T and another image I. The proposed algorithm has been widely used in the field of computer vision for the last 20 years, and has had many modifications and extensions. One of such modifications is an algorithm proposed by Simon Baker, Frank Dellaert, and Iain Matthews. Their algorithm is much more computationally effective than the original Lucas-Kanade algorithm.
ITK is a powerful open-source toolkit implementing state-of-the-art algorithms in medical image processing and analysis. MATLAB, on the other hand, is well-known for its easy-to-use, powerful prototyping capabilities that significantly improve productivity. With the help of MATITK, biomedical image computing researchers familiar with MATLAB can harness the power of ITK algorithms while avoiding learning C++ and dealing with low-level programming issues.
Utkarsh, Sudhanshu, Devansh, Parth, и Ajay. International Journal of Innovative Research in Information Security, Volume VII (Issue IV):
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