Azriel Rosenfeld

Bio: Azriel Rosenfeld is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Image processing & Feature detection (computer vision). The author has an hindex of 94, co-authored 595 publications receiving 49426 citations. Previous affiliations of Azriel Rosenfeld include Meiji University.

A study of new image processing techniques

Abstract: : The report consists of two independent parts. The first part discusses the idea of iterating the conversion of a continuous-tone picture from binary to gray code representation. A simple recursive function is defined by means of which the value of the ith bit representing a picture point's grey level after the mth iteration is determined. The resulting rearrangement of grey levels as well as that of the bits throughout the planes is discussed. The second part of the report contains information regarding the high-resolution digital picture output system developed at the University of Maryland. (Author)

Is DBLP a Good Computer Science Journals Database?

Abstract: DBLP provides comprehensive and free computer science (CS) bibliographic data to all. But how well does it really cover CS journals? In this article, we compare DBLP’s indexed journals with those classified as CS by the leading indexing services today—Web of Science and Scopus.

Scheduling BCG and IL-2 Injections for Bladder Cancer Immunotherapy Treatment

Abstract: Cancer is one of the most common families of diseases today with millions of new patients every year around the world. Bladder cancer (BC) is one of the most prevalent types of cancer affecting both genders, and it is not known to be associated with a specific group in the population. The current treatment standard for BC follows a standard weekly Bacillus Calmette–Guérin (BCG) immunotherapy-based therapy protocol which includes BCG and IL-2 injections. Unfortunately, due to the biological and clinical complexity of the interactions between the immune system, treatment, and cancer cells, clinical outcomes vary significantly among patients. Unfortunately, existing models are commonly developed for a non-existing average patient or pose strict, unrealistic, expectations on the treatment process. In this work, we propose the most extensive ordinary differential equation-based biological model of BCG treatment to date and a deep learning-based scheduling approach to obtain a personalized treatment schedule. Our results show that resulting treatment schedules favorably compare with the current standard practices and the current state-of-the-art scheduling approach.

Computer Vision through Learning

Abstract: : The underlying motivation for this research is that vision systems need learning capabilities for handling problems for which algorithmic solutions are unknown or difficult to obtain. In this context, we have conducted research on a wide range of vision problems that can benefit from machine learning. We have developed a general methodology for this purpose, called MIST, that supports multilevel image sampling, transformation, learning and interpretation. MIST is based on the application of symbolic or multistrategy learning methods (the latter one integrates symbolic and neural net learning) for creating visual concept descriptions. MIST was applied and demonstrated to be useful for such problems as conceptual interpretation of natural scences, non-structural and structural texture description and identification, detection of blasting caps in X-ray images of airport luggage, and target detection in SAR images. We have also obtained important results in the areas of action recognition in video image sequences, visual memories, estimation of environment properties from sampling, and bisight head control. The obtained results have demonstrated a significant promise and usefulness of the efforts to apply modern machine learning methods to problems of computer vision.

A Computational Approach to Edge Detection

Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.

Textural Features for Image Classification

Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

A theory for multiresolution signal decomposition: the wavelet representation

Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >

Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

Abstract: We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs distribution, Markov random field (MRF) equivalence, this assignment also determines an MRF image model. The energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the MRF. For a range of degradation mechanisms, including blurring, nonlinear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical system isolates low energy states (``annealing''), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result is a highly parallel ``relaxation'' algorithm for MAP estimation. We establish convergence properties of the algorithm and we experiment with some simple pictures, for which good restorations are obtained at low signal-to-noise ratios.