A threshold selection method from gray level histograms

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.

A Computational Approach to Edge Detection

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.

Textural Features for Image Classification

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. >

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A theory for multiresolution signal decomposition: the wavelet representation

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.

/pdf/stochastic-relaxation-gibbs-distributions-and-the-bayesian-4c0mbkv82j.pdf

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

This paper reviews some of the techniques which can be used to automatically classify and describe pictorial patterns It discusses representative “preprocessing” and “property measurement” methods, with emphasis on methods which may be useful in processing biomedical pictures (The decision-making aspect of pattern recognition is not discussed) Techniques for subdividing pictures into regions, and measuring geometrical properties of such regions, are also considered The paper is intended to serve as an introduction to the extensive literature in this field; it includes numerous references to this literature

Pictorial pattern recognition.

Propagation algorithms for framing rectangle construction

We examine the volume and characteristics of psychiatric ED visitations through a perspective of four COVID-19 lockdowns. All adult visitations to the ED of Shalvata Mental Healthcare center (Israel) during 2020-2021 were retrieved and statistically analysed and data from 2017 to 2019 was considered as control. Voluntary and involuntary ED visitations were considered, separately and combined. We find that the significant decrease in the volume of voluntary ED visitations during the 1st lockdown was quickly overturned, roughly returning to the pre-pandemic state following its conclusion. In parallel, the volume of involuntary ED visitations has dramatically increased, with the most striking levels observed during the second and third lockdowns. Elapsed time since the first occurrence of COVID-19 in Israel and the level of governmental restrictions is significantly associated with the increase in the volume of ED visits and admissions, the admission rate and the rate of involuntary visits. The prolonged consequences associated with the pandemic and the measures taken to control it suggest that it is unreasonable to expect a return to normal ED utilization in the near future. As such, alternatives to strict lockdowns should be favored when possible and urgent strengthening of psychiatric care is warranted. 

https://doi.org/10.1016/j.psychres.2022.115004

COVID-19 restrictions and visitations to an Israeli psychiatric emergency department: A four-lockdowns retrospective study

A method of converting a picture into a "cartoon" or "map" whose regions correspond to differently textured regions is described. Texture edges in the picture are detected, and solid regions surrounded by these (usually broken) edges are "colored in" using a propagation process. The resulting map is cleaned by comparing the region colors with the textures of the corresponding regions in the picture, and also by merging some regions with others according to criteria based on topology and size. The method has been applied to the construction of cloud cover maps from cloud cover pictures obtained by satellites.

Automatic cloud cover mapping

: An iterative reinforcement scheme is used to enhance straight portions of edges in a image and to obtain refined estimates of their slopes. This makes it much easier to detect significant straight edge segments in the image and thus map the image's straight edge content.

Azriel Rosenfeld

Papers

Pictorial pattern recognition.

Propagation algorithms for framing rectangle construction

COVID-19 restrictions and visitations to an Israeli psychiatric emergency department: A four-lockdowns retrospective study

Automatic cloud cover mapping

Straight Edge Enhancement and Mapping