This article focuses on a collection of early 20th century postcards. These postcards are rich in information and include diverse textual elements-printed, handwritten, or embedded within the natural scene-that give insights into places, monuments, and events. Many postcards also include postage stamp which are of particular interest, as their associated date stamps provide additional information about the place and date they were sent. The aim of this article is to understand the textual content of these postcards, especially their date stamps. All this extracted information are then gathered into an interactive map visualization. Promising experimental results are obtained on a database of 4,300 historical postcards.

The main topic of this work is on the detection of counterfeit documents and especially banknotes. We propose an end-to-end learning model using a deep learning approach based on Adapnet++ which manages feature extraction at multiple scale levels using several residual units. Unlike previous models based on regions of interest (ROI) and high-resolution documents, our network is feed with simple input images (i.e., a single patch) and we do not need high resolution images. Besides, discriminative regions can be visualized at different scales. Our network learns by itself which regions of interest predict the better results. Experimental results show that we are competitive compared with the state-of-the-art and our deep neural network has good ability to generalize and can be applied to other kind of documents like identity or administrative one.

In this paper, we propose a deep learning model based on deep multi-view representation learning, to address the video object segmentation task. The proposed model emphasizes the importance of the inherent correlation between video frames and incorporates a multi-view representation learning based on deep canonically correlated autoencoders. The multi-view representation learning in our model provides an efficient mechanism for capturing inherent correlations by jointly extracting useful features and learning better representation into a joint feature space, i.e., shared representation. To increase the training data and the learning capacity, we train the proposed model with pairs of video frames, i.e., Fa and Fb. During the segmentation phase, the deep canonically correlated auto encoders model encodes useful features by processing multiple reference frames together, which is used to detect the frequently reappearing. Our model enhances the state-of-the-art deep learning-based methods that mainly focus on learning discriminative foreground representations over appearance and motion. Experimental results over two large benchmarks demonstrate the ability of the proposed method to outperform competitive approaches and to reach good performances, in terms of semantic segmentation.

Document images dewarping is still a challenge especially when documents are captured with one camera in an uncontrolled environment. In this paper we propose a generic approach based on vanishing points (VP) to reconstruct the 3D shape of document pages. Unlike previous methods we do not need to segment the text included in the documents. Therefore, our approach is less sensitive to pre-processing and segmentation errors. The computation of the VPs is robust and relies on the a-contrario framework, which has only one parameter whose setting is based on probabilistic reasoning instead of experimental tuning. Thus, our method can be applied to any kind of document including text and non-text blocks and extended to other kind of images. Experimental results show that the proposed method is robust to a variety of distortions.

In this paper, we propose a method using Generative Adversarial Networks for automatically synthesizing document images that are similar to real printed documents captured by mobile phone's camera in unconstrained environment. We focus on the simulation of image defects for unconstrained mobile image acquisition procedure (non-uniform illumination, defocusing, optical and mechanical deformations, vibrations, noise in electronic components,...). Our approach is proven to be low-cost as it only requires a collection of real document images without any annotation. Experimental results show the effectiveness of our approach to improve OCR (Optical Character Recognition) recognition rate in a mobile-captured document images framework. Although in this paper, we focus on modern printed document images, our proposed approach could be extended to another type of documents, including historical one.

Nowadays the number of mobile applications is fast growing. Among them, mobile applications based on Optical Character Recognition (OCR) play an important role. One of the main challenge of such applications to overcome is that the image acquisition procedure is in a manner unreliable and may contain many distortions. As a consequence, a suitable OCR output requires efforts to enhance the quality of the captured image. This leads to an increase of computation time and cost. In this perspective, we focus on the prediction of image's sharpness quality. We choose to concentrate on image's sharpness quality because blur distortions seriously alter readability for both human and computer. Our contribution consists of a method combining focus and sharpness measures with a Support Vector Machine to classify image's sharpness quality. This approach is fast, reliable, and can be easily implemented on mobile devices. Experimental results show that our method is efficient for OCR based mobile-captured document images.

Flooding is one of the most harmful natural disasters, as it poses danger to both buildings and human lives. Therefore, it is fundamental to monitor these disasters to define prevention strategies and help authorities in damage control. With the wide use of portable devices (e.g., smartphones), there is an increase of the documentation and communication of flood events in social media. However, the use of these data in monitoring systems is not straightforward and depends on the creation of effective recognition strategies. In this paper, we propose a fusion-based recognition system for detecting flooding events in images extracted from social media. We propose two new graph-based early-fusion methods, which consider multiple descriptions and modalities to generate an effective image representation. Our results demonstrate that the proposed methods yield better results than a traditional early-fusion method and a specialized deep neural network fusion solution.

In this paper, we propose an approach that automatically selects suitable document pre-processing algorithms to increase OCR performances. We first provide an experimental evaluations protocol to study effects of document pre-processing methods on different OCR engines for document images that have different type of distorsions. We remark that, when distortions on the document image is unknown, a pre-processing methods does not always improve but sometimes decreases the OCR performance. We conclude that the effectiveness of a pre-processing algorithm depends on the nature of the OCR and type of distorsions. In the context that distortions on the document and information about OCR system's mechanism are unknown, we propose an automatic pre-processing selection method based on a convolutional neural network with 15 layers and where the last layer contains neurons representing our different pre-processing algorithms. Experimental results show the effectiveness of our approach to improve OCR performances in a mobile-captured document images framework.

A correction method for perspective distortions on document images is discussed. In documents, lines and line feeds give rise to many horizontal and vertical lines, then two vanishing points generated by these lines can be computed. High-energy regions are identified in the Radon transform thanks to a binarization step. Then, the image is zero-padded and the inverse Radon transform is applied to underline the main lines direction in the original image. The distortion is corrected by the perspective mapping determined with the two vanishing points and we propose to compute the homography matrix for the perspective mapping. Experimental results show that our method can correct the perspective distortions effectively and outperforms the state-of-the-art for vanishing points detection accuracy.

With the fast growth of document images, document annotation has become a research area of great interest. Annotation allows to describe the semantic content of documents and facilitates their use and research. However, for a huge number of documents, the manual annotation of each document becomes a tedious task. A solution is to annotate a small part of the documents and to extend it automatically to the whole dataset. In this paper, we propose a model for annotation extension and document classification using a probabilistic graphical model. In this latter, we combine visual and textual characteristics and we show that the integration of the user feedback improves the annotation step.

This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising.

In this paper we present a new hybrid page segmentation approach based on connected component and region analysis. We first describe our stroke descriptor that detects text and line component candidates using the skeleton of the binarized document image. Then, an active contour model is applied to segment the rest of the image into photo and background regions. This classification is verified by studying the variation of each detected region. Finally, we cluster the text candidates using mean-shift analysis technique according to their corresponding sizes and we present our adaptive projection profile approach to gather separately horizontal and vertical text regions. The method is applied for segmenting realistic scanned document images (newspapers and magazines) that contain text, lines and photo regions. We evaluate the performances of our approach by comparing it to the existing methods that participated in ICDAR page segmentation competition.

Huge volume of graph data are becoming available. This scenario demands the development of effective and efficient methods to perform graph matching. In this paper, we propose to adapt the Bag-of-Words model into the context of graphs. Using a vocabulary based on graph local structures, we represent graphs as histograms. Experiments show that our approach achieves good accuracy rates. Moreover, the advantage of this representation is that the computation of graph matching has a very low complexity, which allows to efficiently perform graph classification and retrieval on large datasets.

An affine invariant shape matching method using the histogram of Radon transform (HRT) and the dynamic time warping (DTW) distance is proposed. Our descriptor based on the Radon transform is robust to shape rotation, uniform scaling, and translation. For non-uniform scaling and shearing, our descriptor has a non-linear sparse and dense distortion relative to the angle coordinates. Therefore, we apply DTW on a cost matrix to be robust to these transformations. This cost matrix is defined as an angle correlation matrix based on the product of two matrices only. Moreover, based on the beam search algorithm, we speed-up the time complexity of our method. Experimental results show that our approach is fast to compute and competitive compared to well-known descriptors.

Generic polar harmonic transforms have recently been proposed to extract rotation-invariant features from images and their usefulness has been demonstrated in a number of pattern recognition problems. However, direct computation of these transforms from their definition is inefficient and is usually slower than some efficient computation strategies that have been proposed for other methods. This paper presents a number of novel computation strategies to compute these transforms rapidly. The proposed methods are based on the inherent recurrence relations among complex exponential and trigonometric functions used in the definition of the radial and angular kernels of these transforms. The employment of these relations leads to recursive and addition chain-based strategies for fast computation of harmonic function-based kernels. Experimental results show that the proposed method is about 10× faster than direct computation and 5× faster than fast computation of Zernike moments using the q-recursive strategy. Thus, among all existing rotation-invariant feature extraction methods, polar harmonic transforms are the fastest.

In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental results on several datasets demonstrate the robustness of our method compared with the state-of-the-art.

In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learned dictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. The evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-the-art methods.

This paper proposes the Bag of Visual Graphs (BoVG), a new approach to encode the spatial relationships of visual words through a codebook of visual-word arrangements, represented by graphs. This graph-based codebook defines a descriptor for image representations that not only considers the frequency of occurrence of visual words, but also their spatial relationships. Experiments demonstrate that BoVG yields high-accuracy scores in classification tasks on the traditional Caltech-101 and Caltech-256 datasets.

Remote phenology has motivated the development of new technologies for pattern observation. In this scenario, digital cameras have been used as data source for studies that estimate changes on phenological events. In this paper, we investigate the use of shape descriptors in the task of characterizing time series associated with phenological changes. The main objectives are: i) to determine which color channel is better for extracting shape descriptors and ii) to analyze the impact of the sunshine on the performance of shape descriptors.

Graphics recognition is an important task in many real-life applications. In this article, we propose a new approach to recognize graphical symbols by the use of a frequent Galois lattice. We propose to build a concept lattice not in terms of graphical patterns but in terms of frequent graphical patterns. The purpose of this paper is twofold : first, we try to identify the best primitives from a given graphical symbol based on a descriptor invariant to rotation, translation and scaling. Each symbol is decribed using a feature vector computed on stable neighborhood for a set of points chosen randomly from the symbol. Secondly, we propose a new recognition approach based on a frequent Galois lattice. The obtained concept lattice based on frequent patterns is used as a classifier. The retrieval performance and behavior of the method have been tested for graphics recognition. We have compared our method with others based on different descriptors and classifiers. Our approach proves that the symbol description method and the algorithm used to extract frequent attributes to build the frequent Galois lattice are suitable to the recognition process.

This paper presents a new approach for graphical symbols recognition by combining a concept lattice with a bag of words representation. Visual words define the properties of a graphical symbol that will be modeled in the Galois Lattice. The algorithm of classification is based on the Galois lattice where intentions of its concepts are visual words. The words as visual primitives allow to evaluate the classifier with a symbolic approach that no longer need a signature discretization step to build the Galois Lattice. Our approach is compared to classical approaches on different graphical symbols and we show the relevance and the robustness of our proposal for the classification task.

A shape descriptor combining the histogram of the Radon transform, the logarithmic-scale histogram, and the phase-only correlation function is proposed. Applying a logarithmic-scale to the Radon transform, the shape scaling and rotation become two-dimensional translation in our descriptor without any normalization. The geometric invariance to translation, when we match two shapes, are kept using the phase-only correlation function. In addition, we can determine with this function the rotation angle and the scale parameter between two shapes. Our descriptor is robust to shape occlusion and noise also.

In this paper we study the detection of skewed text lines in scanned document images. The aim of our work is to develop a new automatic approach able to estimate precisely the skew angle of text in document images. Our new method is based on Maximum Gradient Difference (MGD) and R-signature. It detects zones that have high variations of gray values in different directions using the MGD transform. We consider these zones as being text regions. R-signature which is a shape descriptor based on Radon transform is then applied in order to approximate the skew angle. The accuracy of the proposed algorithm is evaluated on an open dataset by comparing error rates.

A new parametric method for edge noise removal in graphical document images is presented using geometrical regularities of the graphics contours that exists in the images. Denoising is understood as a recovery problem and is done by employing a sparse representation framework with a basis pursuit denoising algorithm for denoising and curvelet frames for encoding directional information of the graphics contours. The optimal precision parameter used in this framework is shown to have linear relationship with the level of the noise. Experimental results show the superiority of the proposed method over existing ones in terms of image recovery and contour raggedness.

A class of rotation-invariant orthogonal moments is proposed using a complex exponential in the radial direction. Each member of this class, while sharing beneficial properties to image representation and recognition like orthogonality and rotation-invariance, has distinctive properties depending on the value of a parameter, making it more suitable for some particular applications. The computation of moments is simpler and more stable than existing methods. Experimental results show the effectiveness of this class of moments in term of description performance and pattern recognition ability.

A novel text extraction method from graphical document images is presented in this paper. Graphical document images containing text and graphics components are considered as two-dimensional signals by which text and graphics have different morphological characteristics. The proposed algorithm relies upon a sparse representation framework with two appropriately chosen discriminative overcomplete dictionaries, each one gives sparse representation over one type of signal and non-sparse representation over the other. Separation of text and graphics components is obtained by promoting sparse representation of input images in these two dictionaries. Some heuristic rules are used for grouping text components into text strings in post-processing steps. The proposed method overcomes the problem of touching between text and graphics. Preliminary experiments show some promising results on different types of document.

In this article, a system to detect rooms in architectural floor plan images is described. We first present a primitive extraction algorithm for line detection. It is based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection. We show how the lines that satisfy some graphical arrangements are combined into walls. We also present the way we detect some door hypothesis thanks to the extraction of arcs. Walls and door hypothesis are then used by our room segmentation strategy; it consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines between regions can also be rough. We take advantage of knowledge associated to architectural floor plans in order to obtain mostly rectangular rooms. Qualitative and quantitative evaluations performed on a corpus of real documents show promising results.

A new shape descriptor invariant to geometric transformation based on the Radon, Fourier, and Mellin transforms is proposed. The Radon transform converts the geometric transformation applied on a shape image into transformation in the columns and rows of the Radon image. Invariances to translation, rotation, and scaling are obtained by applying 1D Fourier-Mellin and Fourier transforms on the columns and rows of the shape's Radon image respectively. Experimental results on different datasets show the usefulness of the proposed shape descriptor.

In the context of the NAVIDOMASS project, the problematic of this paper concerns the clustering of historical document images. We propose a structural-based framework to handle the ancient ornamental letters data-sets. The contribution, firstly, consists of examining the structural (i.e. graph) representation of the ornamental letters, secondly, the graph matching problem is applied to the resulted graph-based representations. In addition, a comparison between the structural (graphs) and statistical (generic Fourier descriptor) techniques is drawn.

In the context of unsupervised clustering, a new algorithm for the domain of graphs is introduced. In this paper, the key idea is to adapt the mean-shift clustering and its variants proposed for the domain of feature vectors to graph clustering. These algorithms have been applied successfully in image analysis and computer vision domains. The proposed algorithm works in an iterative manner by shifting each graph towards the median graph in a neighborhood. Both the set median graph and the generalized median graph are tested for the shifting procedure. In the experiment part, a set of cluster validation indices are used to evaluate our clustering algorithm and a comparison with the well-known Kmeans algorithm is provided.

In this paper, the Radon transform is used to design a new descriptor called Phi-signature invariant to usual geometric transformations. Experiments show the effectiveness of the multilevel representation of the descriptor built from Phi-signature and R-signature, compared to the powerful generic Fourier descriptor.

In this paper, a parametric and unsupervised histogram-based image segmentation method is presented. The histogram is assumed to be a mixture of asymmetric generalized Gaussian distributions. The mixture parameters are estimated by using the Expectation Maximization algorithm. Histogram fitting and region uniformity measures on synthetic and real images reveal the effectiveness of the proposed model compared to the generalized Gaussian mixture model.

We propose a probabilistic graphical model to represent weakly annotated images. This model is used to classify images and automatically extend existing annotations to new images by taking into account semantic relations between keywords. The proposed method has been evaluated in classification and automatic annotation of images. The experimental results, obtained from a database of more than 30000 images, by combining visual and textual information, show an improvement by 50.5% in terms of recognition rate against only visual information classification. Taking into account semantic relations between keywords improves the recognition rate by 10.5% and the mean rate of good annotations by 6.9%. The proposed method is experimentally competitive with the state-of-art classifiers.

This paper presents a generic features selection method and its applications on some document analysis problems.The method is based on a genetic algorithm (GA), whose fitness function is defined by combining Adaboot classifiers associated with each feature. Our method is not linked to a classifier achieving the final recognition task; we have used a combination of weak classifiers to evaluate a subset of features. So we select features that can further be used in the most appropriate classifiers.This method has been tested on three applications: dropcaps classification, handwritten digits recognition and text detection. The results show the efficiency and robustness of the proposed approach.

Automatic processing of images of steles is a challenging problem due to the variation in their structures and body text characteristics. In this paper, area Voronoi diagram is used to represent the neighborhood of connected components in stele images containing Nom characters. Body text region is then extracted from stele images by the selection of appropriate adjacent Voronoi regions based on the information about the thickness of neighboring connected components. Experimental results show that the proposed method is highly accurate and robust to various types of stele.

This paper addresses the difficult problem of symbol spotting for graphic documents. We propose an approach where each graphic document is indexed as a text document by using the vector model and an inverted file structure. The method relies on a visual vocabulary built from a shape descriptor adapted to the document level and invariant under classical geometric transforms (rotation, scaling and translation). Regions of interest selected with high degree of confidence using a voting strategy are considered as occurrences of a query symbol. Experimental results are promising and show the feasibility of our approach.

Automatic Image Annotation (AIA) tries to minimize the manual effort for image annotation. However, the performance of the AIA approaches is not satisfactory. The interaction of user is needed to solve this problem. The annotation is refined during the interaction by using semantic-based relevance feedback. This approach has a limit as only the annotations of found images during the interaction are updated. In this paper we introduce a novel method of semi-automatic annotation. The method is using visual feature representations of keywords which are improved during the region-based relevance feedback. The experiments show that this method gives good results and can be used to update the annotations for all images.

The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes.

In this paper we present an adaptive method for graphic symbol representation based on shape contexts. The proposed descriptor is invariant under classical geometric transforms (rotation, scale) and based on interest points. To reduce the complexity of matching a symbol to a largeset of candidates we use the popular vector model for information retrieval. In this way, on the set of shape descriptors we build a visual vocabulary where each symbol is retrieved on visual words. Experimental results on complex and occluded symbols show that the approach is very promising.

In many vision problems, instead of having fully annotated training data, it is easier to obtain just a subset of data with annotations, because it is less restrictive for the user. For this reason, in this paper, we consider especially the problem of weakly-annotated image retrieval, where just a small subset of the database is annotated with keywords. We present and evaluate a new method which improves the effectiveness of content-based image retrieval, by integrating semantic concepts extracted from text. Our model is inspired from the probabilistic graphical model theory: we propose a hierarchical mixture model which enables to handle missing values and to capture the userpsilas preference by also considering a relevance feedback process. Results of visual-textual retrieval associated to a relevance feedback process, reported on a database of images collected from the Web, partially and manually annotated, show an improvement of about 44.5%in terms of recognition rate against content-based retrieval.

This paper presents a fast method using simple genetic algorithms (GAs) for features selection. Unlike traditional approaches using GAs, we have used the combination of Adaboost classifiers to evaluate an individual of the population. So, the fitness function we have used is defined by the error rate of this combination. This approach has been implemented and tested on the MNIST database and the results confirm the effectiveness and the robustness of the proposed approach.

In this paper we present a new descriptor based on the Radon transform. We propose a histogram of the Radon transform, called HRT, which is invariant to common geometrical transformations. For black and white shapes, the HRT descriptor is a histogram of shape lengths at each orientation. The experimental results, defined on different databases and compared with several well-known descriptors, show the robustness of our method.

In this paper, a method to browse symbols into graphical documents is presented. More precisely, we propose a combined filtering and indexing mechanism that retrieves in an efficient way the most similar symbols to a given input query. For a database of 200000 symbols the retrieval time has been divided by a factor of 4, 5 compared to a linear search.

Shape descriptors play an important role in many document analysis application. In this paper we review some of the shape descriptors proposed in the last years from a new point of view. We propose the definitions of descriptor and primitive and introduce the notion of feature extraction method. With these definitions, we propose a new classification of shape descriptors that permits to classify according to their properties pointing out their strengths and weaknesses.

This paper presents a novel approach to combine shape descriptors. Each approach is applied on several clusters of objects. For each cluster and for any descriptor a map is associated directly from the confusion matrix. Such a method allows to determine automatically the better weight associated to the descriptor for the object under consideration. At last, we show that the additive combination of such measures allows to improve the classification.

A new way of detecting arrows in line drawings is proposed in this paper. We provide a set of criteria which are aggregated using the Choquet integral. These criteria are defined from the geometric properties of an arrow. Experimental results on two kinds of line-drawing documents show the interest of our approach.

A new way to detect arrows in line drawings is proposed in this paper. Our approach is based on the definition of the structure of such a symbol. Signatures of angular areas are computed and axiomatic properties and geometric characteristics are checked using the Choquet integral. Finally an experimental application on line-drawing documents shows the interest of our approach.

We propose a method for indexing technical drawings. Our features are based on the notion of F-signature, which is a particular histogram of forces. The force histogram has low time complexity and describes a signature which is invariant to scaling, translation, symmetry and rotation. This article presents a new application of such signatures in the field of document analysis, and tests different characteristics of the F-signatures, like their sensitivity to the shape of the objects and to noise. Finally, experimental results show the effectiveness of such an approach. A brief overview of pattern recognition approaches dedicated to technical document indexing is given. The notions of histogram of forces and of its properties are presented.

Vectorization, i.e. raster-to-vector conversion, is a central part of graphics recognition problems. We discuss the pros and the cons of basing one's vectorization process on skeletonization. While distance skeletons have proven to be robust and precise, they tend to distort the results at line extremities and junctions. In these cases, contour-matching approaches yield better results, but they have their own specific problems. A perspective is probably to combine the best of both methods.

Edge detection is a fundamental stage in order to facilitate the analysis or the interpretation of an image. However classical edge detectors usually yield gaps in the contour image. To restore incomplete contours, criteria based on perceptual grouping have been proposed. Almost all existing closing algorithms based only on these criteria fail at discontinuity points junction. In this perspective we propose an original algorithm which combines perceptual grouping criteria and junction points. Furthermore, grouping is done by preprocessing iteratively in a bottom-up, local-to-global fashion.>

This paper describes a fast junction detection algorithm using the Laplacian of Guassian detector. We propose a general junction model and analyze its behavior in scale space. This study shows two properties: the Laplacian of Gaussian is zero at any junction and it has one or several elliptic extrema that always lie inside the junction sectors. These extrema move in scale space on lines passing by the junction point. Using these properties, we build an accurate and efficient junction detector. Experimental results confirm, in practice, the efficiency and the reliability of this detector.

An edge combination algorithm is described. The authors' approach is based on the study of four step edge models (ideal, blurred, pulse and staircase) in scale space. Under these conditions, it is shown that the use of two scales (high and low) is sufficient for good edge detection. A set of rules is derived to combine edge information, and an appropriate algorithm is given, taking into account the origin of false edges and their behavior in scale space.>

Describes a new approach for positioning boundaries in a discrete image to subpixel values. To correct the position of step edges, the authors combine the operator output and the properties of both operator and edge. This method is simpler to use than existing ones and requires a relatively small amount of computer power. Both availability and reliability are discussed for first and second order operators.>