In this paper we prove that the image of multilinear polynomials evaluated on algebra UTn(K) n × upper triangular matrices over an infinite field K equals Jr, a power its Jacobson ideal J = J(UTn(K)). particular, shows analogue Lvov—Kaplansky conjecture for is true, solving Fagundes and de Mello. To fact, introduce notion commutator-degree polynomial characterize r in terms coefficients. It tur...

The problem of determining nice (regular, simple, minimum crossing, and monotonic) and non-degenerate (with distinct x-coordinate, non-collinear, non-cocircular, and non-parallel) orthogonal and perspective images of a set of points or a set of disjoint line segments has been studied extensively in the literature for the theoretical case of infinite resolution images [J. Vis. Commun. Image Repr...

Finite automata are being used to encode images. Applications of this technique include image compression, and extraction of self similarity information and Hausdorr dimension of the encoded image. J urgensen and Staiger 6] proposed a method by which the local Haus-dorr dimension of the encoded image could be eeectively computed. This paper describes the rst implementation of this procedure and...

Adaptive optics (AO) has greatly improved retinal image resolution. However, even with AO, temporal and spatial variations in image quality still occur due to wavefront fluctuations, intraframe focus shifts, and other factors. As a result, aligning and averaging images can produce a mean image that has lower resolution or contrast than the best images within a sequence. To address this, we prop...

We study linear filter kernel estimation from processed digital images under the assumption that the image’s source camera is known. By leveraging easy-to-obtain camera-specific sensor noise fingerprints as a proxy, we have identified the linear crosscorrelation between a pre-computed camera fingerprint estimate and a noise residual extracted from the filtered query image as a viable domain to ...

We study image compression by a separable wavelet basis { ψ(2k1x − i)ψ(2k2y − j), φ(x − i)ψ(2k2y − j), ψ(2k1(x − i)φ(y − j), φ(x − i)φ(y − i) } , where k1, k2 ∈ Z+; i, j ∈ Z; and φ, ψ are elements of a standard biorthogonal wavelet basis in L2(R). Because k1 6= k2, the supports of the basis elements are rectangles, and the corresponding transform is known as the rectangular wavelet transform. W...