Results 1  10
of
28
Determining Optical Flow
 ARTIFICIAL INTELLIGENCE
, 1981
"... Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent veloc ..."
Abstract

Cited by 1727 (7 self)
 Add to MetaCart
Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantized rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.
Reflectance Based Object Recognition
, 1996
"... Neighboring points on a smoothly curved surface have similar surface orientations and illumination conditions. Therefore, their brightness values can be used to compute the ratio of their reflectance coefficients. Based on this observation, we develop an algorithm that estimates a reflectance ratio ..."
Abstract

Cited by 64 (1 self)
 Add to MetaCart
Neighboring points on a smoothly curved surface have similar surface orientations and illumination conditions. Therefore, their brightness values can be used to compute the ratio of their reflectance coefficients. Based on this observation, we develop an algorithm that estimates a reflectance ratio for each region in an image with respect to its background. The algorithm is efficient as it computes ratios for all image regions in just two raster scans. The region reflectance ratio represents a physical property that is invariant to illumination and imaging parameters. Several experiments are conducted to demonstrate the accuracy and robustness of ratio invariant.
Thresholding for Change Detection
, 1998
"... Image differencing is used for many applications involving change detection. Although it is usually followed by a thresholding operation to isolate regions of change there are few methods available in the literature specific to (and appropriate for) change detection. We describe four different metho ..."
Abstract

Cited by 63 (2 self)
 Add to MetaCart
Image differencing is used for many applications involving change detection. Although it is usually followed by a thresholding operation to isolate regions of change there are few methods available in the literature specific to (and appropriate for) change detection. We describe four different methods for selecting thresholds that work on very different principles. Either the noise or the signal is modelled, and the model covers either the spatial or intensity distribution characteristics. The methods are: 1/ a Normal model is used for the noise intensity distribution, 2/ signal intensities are tested by making local intensity distribution comparisons in the two image frames (i.e. the difference map is not used), 3/ the spatial properties of the noise are modelled by a Poisson distribution, and 4/ the spatial properties of the signal are modelled as a stable number of regions (or stable Euler number).
Image Difference Threshold Strategies and Shadow Detection
 in Proc. British Machine Vision Conf
, 1995
"... The paper considers two problems associated with the detection and classification of motion in image sequences obtained from a static camera. Motion is detected by differencing a reference and the "current" image frame, and therefore requires a suitable reference image and the selection of an approp ..."
Abstract

Cited by 55 (2 self)
 Add to MetaCart
The paper considers two problems associated with the detection and classification of motion in image sequences obtained from a static camera. Motion is detected by differencing a reference and the "current" image frame, and therefore requires a suitable reference image and the selection of an appropriate detection threshold. Several threshold selection methods are investigated, and an algorithm based on hysteresis thresholding is shown to give acceptably good results over a number of test image sets. The second part of the paper examines the problem of detecting shadow regions within the image which are associated with the object motion. This is based on the notion of a shadow as a semitransparent region in the image which retains a (reduced contrast) representation of the underlying surface pattern, texture or grey value. The method uses a region growing algorithm which uses a growing criterion based on a fixed attenuation of the photometric gain over the shadow region, in comparison...
Geometric Algorithms for Digitized Pictures on a MeshConnected Computer
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1985
"... AbstractAlthough meshconnected computers are used almost exclusively for lowlevel local image processing, they are also suitable for higher level image processing tasks. We illustrate this by presenting new optimal (in the 0notational sense) algorithms for computing several geometric properties ..."
Abstract

Cited by 13 (3 self)
 Add to MetaCart
AbstractAlthough meshconnected computers are used almost exclusively for lowlevel local image processing, they are also suitable for higher level image processing tasks. We illustrate this by presenting new optimal (in the 0notational sense) algorithms for computing several geometric properties of figures. For example, given a black/white picture stored one pixel per processing element in an n X n meshconnected computer, we give 0(n) time algorithms for determining the extreme points of the convex hull of each component, for deciding if the convex hull of each component contains pixels that are not members of the component, for deciding if two sets of processors are linearly separable, for deciding if each component is convex, for determining the distance to the nearest neighboring component of each component, for determining internal distances in each component, for counting and marking minimal internal paths in each component, for computing the external diameter of each component, for solving the largest empty circle problem, for determining internal diameters of components without holes, and for solving the allpoints farthest point problem. Previous meshconnected computer algorithms for these problems were either nonexistent or had worst case times of 0 (n 2). Since any serial computer has a best case time of 0(n 2) when processing an n X n image, our algorithms show that the meshconnected computer provides significantly better solutions to these problems. Index Terms Computational geometry, convexity, image processing, meshconnected computer.
Computing Reflectance Ratios from an Image
 Pattern Recognition
, 1993
"... This paper presents a photometric invariant called reflectance ratio that can be computed from a single brightness image of a scene. The brightness variation in an image of a surface depends on several factors; the threedimensional shape of the surface, its reflectance properties, and the illuminat ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
This paper presents a photometric invariant called reflectance ratio that can be computed from a single brightness image of a scene. The brightness variation in an image of a surface depends on several factors; the threedimensional shape of the surface, its reflectance properties, and the illumination conditions. Since neighboring points on a smoothly curved surface have similar surface orientations, their brightness values can be used to compute the ratio of their reflectance coefficients. Based on this observation, we develop an algorithm that estimates a reflectance ratio for each region in the image with respect to its background. The algorithm is computationally efficient as it computes ratios for all image regions in just two raster scans. In the first scan, the image is segmented into regions using a sequential labeling algorithm. During labeling, the reflectance ratio between adjacent pixels is used as a measure of connectivity. In the second scan, a reflectance ratio is compu...
Cellular Automata Methods in Mathematical Physics
, 1994
"... Cellular automata (CA) are fully discrete, spatiallydistributed dynamical systems which can serve as an alternative framework for mathematical descriptions of physical systems. Furthermore, they constitute intrinsically parallel models of computation which can be efficiently realized with specialp ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
Cellular automata (CA) are fully discrete, spatiallydistributed dynamical systems which can serve as an alternative framework for mathematical descriptions of physical systems. Furthermore, they constitute intrinsically parallel models of computation which can be efficiently realized with specialpurpose cellular automata machines. The basic objective of this thesis is to determine techniques for using CA to model physical phenomena and to develop the associated mathematics. Results may take the form of simulations and calculations as well as proofs, and applications are suggested throughout. We begin
Wndchrm an open source utility for biological image analysis
"... Background: Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottlen ..."
Abstract

Cited by 7 (7 self)
 Add to MetaCart
Background: Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottleneck in high content screening. Methods: Wndchrm is an open source utility for biological image analysis. The software works by first extracting image content descriptors from the raw image, image transforms, and compound image transforms. Then, the most informative features are selected, and the feature vector of each image is used for classification and similarity measurement. Results: Wndchrm has been tested using several publicly available biological datasets, and provided results which are favorably comparable to the performance of taskspecific algorithms developed for these datasets. The simple user interface allows researchers who are not knowledgeable in computer vision methods and have no background in computer programming to apply image analysis to their data. Conclusions: We suggest that wndchrm can be effectively used for a wide range of biological image analysis tasks. Using wndchrm can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms.
Negative Shape Features For Image Databases Consisting Of Geographic Symbols
, 1997
"... A method for representing geographic symbols for storage and retrieval in an image database is presented. Symbols are characterized by a collection of features that describe their shape. Many of these geographic symbols are composed of a circle (or rectangle) enclosing one or more small shapes. A ne ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
A method for representing geographic symbols for storage and retrieval in an image database is presented. Symbols are characterized by a collection of features that describe their shape. Many of these geographic symbols are composed of a circle (or rectangle) enclosing one or more small shapes. A new representation of such symbols based on their interior with the shapes considered as holes, termed a negative symbol, is described. A set of shape features used to characterize geographic symbols is presented. These features are appropriate for symbols that are represented by their negation as well as for symbols that are composed of only one part and thus are not represented by their negation. Negative symbols along with these features have been used successfully to index maps in a map image database system. Results of experiments testing the accuracy of the database using these features are given. 1 Introduction Consider a database containing a large number of images each composed of se...
Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods
, 2008
"... Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods. Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods. Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images. Then, an image classification method was used in order to check the classification accuracy. Experimental results show that the classification accuracy ranged between 13.5 % (color FERET) to 99% (YaleB). These results indicate that the performance of face recognition methods measured using face image datasets may be biased. Compilable source code used for this experiment is freely available for download via the internet.