Optic flow estimation by a Hopfield neural network using geometrical constraints

Sparse optic flow maps are general enough to obtain useful information about camera motion. Usually, correspondences among features over an image sequence are estimated by radiometric similarity. When the camera moves under known conditions, global geometrical constraints can be introduced in order to obtain a more robust estimation of the optic flow. In this paper, a method is proposed for the computation of a robust sparse optic flow (OF) which integrates the geometrical constraints induced by camera motion to verify the correspondences obtained by radiometric-similarity-based techniques. A raw OF map is estimated by matching features by correlation. The verification of the resulting correspondences is formulated as an optimization problem that is implemented on a Hopfield neural network (HNN). Additional constraints imposed in the energy function permit us to achieve a subpixel accuracy in the image locations of matched features. Convergence of the HNN is reached in a small enough number of iterations to make the proposed method suitable for real-time processing. It is shown that the proposed method is also suitable for identifying independently moving objects in front of a moving vehicle. Received: 26 December 1995 / Accepted: 20 February 1997     

A new and fast approach for DPIV using an incompressible affine flow model

Traditional digital particle image velocimetry (DPIV) methods are previously based on area-correlation. Though proven to be very time-consuming and error prone, it has been widely adopted because it is conceptually simple, and easy to implement, and also because there are few alternatives. This paper provides a non-correlative, conceptually new, fast and efficient approach for DPIV which takes the nature of flow into consideration. An incompressible affine flow model (IAFM) is introduced to describe a flow that incorporates rational constraint directly into the computation. This IAFM, combining with a modified optical flow method – named total optical flow computation, provides a linear system solution to DPIV. Experimental results on real images demonstrate our method to be a very promising approach for DPIV. Received: 23 March 1998 / Accepted: 1 September 1999     

The optimal location of replicas in a network using a READ-ONE-WRITE-ALL policy

We consider the problem of locating replicas in a network to minimize communications costs. Under the assumption that the read-one-write-all policy is used to ensure data consistency, an optimization problem is formulated in which the cost function estimates the total communications costs. The paper concentrates on the study of the optimal communications cost as a function of the ratio between the frequency of the read and write operations. The problem is reformulated as a zero-one linear programming problem, and its connection to the p-median problem is explained. The general problem is proved to be NP-complete. For path graphs a dynamic programming algorithm for the problem is presented. Received: May 1993 / Accepted: June 2001     

Model-based detection,segmentation, and classification for image analysis using on-line shape learning

Detection, segmentation, and classification of specific objects are the key building blocks of a computer vision system for image analysis. This paper presents a unified model-based approach to these three tasks. It is based on using unsupervised learning to find a set of templates specific to the objects being outlined by the user. The templates are formed by averaging the shapes that belong to a particular cluster, and are used to guide a probabilistic search through the space of possible objects. The main difference from previously reported methods is the use of on-line learning, ideal for highly repetitive tasks. This results in faster and more accurate object detection, as system performance improves with continued use. Further, the information gained through clustering and user feedback is used to classify the objects for problems in which shape is relevant to the classification. The effectiveness of the resulting system is demonstrated in two applications: a medical diagnosis task using cytological images, and a vehicle recognition task. Received: 5 November 2000 / Accepted: 29 June 2001 Correspondence to: K.-M. Lee     

Classification of document pages using structure-based features

Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify it by type in the absence of domain-specific models. Our approach to classification is based on “visual similarity” of layout structure and is implemented by building a supervised classifier, given examples of each class. We use image features such as percentages of text and non-text (graphics, images, tables, and rulings) content regions, column structures, relative point sizes of fonts, density of content area, and statistics of features of connected components which can be derived without class knowledge. In order to obtain class labels for training samples, we conducted a study where subjects ranked document pages with respect to their resemblance to representative page images. Class labels can also be assigned based on known document types, or can be defined by the user. We implemented our classification scheme using decision tree classifiers and self-organizing maps. Received June 15, 2000 / Revised November 15, 2000     

Regularized CDWT optical flow applied to moving-target detection in IR imagery

A modified version of the CDWT optical flow algorithm developed by Magarey and Kingsbury is applied to the problem of moving-target detection in noisy infrared image sequences, in the case where the sensor is also moving. Frame differencing is used to detect pixel-size targets moving in strongly cluttered backgrounds. To compensate for sensor motion, prior to differencing, the background is registered spatially using the estimated motion field between the frames. Results of applying the method to three image sequences show that the target SNR is higher when the estimated motion field for the whole scene is explicitly regularized. A comparison with another optical flow algorithm is also presented.     

Pitch-based segmentation and recognition of dot-matrix text

Dot-matrix text recognition is a difficult problem, especially when characters are broken into several disconnected components. We present a dot-matrix text recognition system which uses the fact that dot-matrix fonts are fixed-pitch, in order to overcome the difficulty of the segmentation process. After finding the most likely pitch of the text, a decision is made as to whether the text is written in a fixed-pitch or proportional font. Fixed-pitch text is segmented using a pitch-based segmentation process that can successfully segment both touching and broken characters. We report performance results for the pitch estimation, fixed-pitch decision and segmentation, and recognition processes. Received October 18, 1999 / Revised April 21, 2000     

Assigning labels in an unknown anonymous network with a leader

We consider the task of assigning distinct labels to nodes of an unknown anonymous network in a distributed manner. A priori, nodes do not have any identities, except for one distinguished node, called the source, and do not know the topology or the size of the network. They execute identical algorithms, apart from the source which plays the role of a leader and starts the labeling process. Our goal is to assign short labels, as fast as possible. The quality of a labeling algorithm is measured by the range from which the algorithm picks the labels, or alternatively, the length of the assigned labels. Natural efficiency measures are the time, i.e., the number of rounds required for the label assignment, and the message and bit complexities of the label assignment protocol, i.e., the total number of messages (resp., bits) circulating in the network. We present label assignment algorithms whose time and message complexity are asymptotically optimal and which assign short labels. On the other hand, we establish inherent trade-offs between quality and efficiency for labeling algorithms. Received: July 2000 / Accepted: February 2001     

Motion segmentation and pose recognition with motion history gradients

This paper presents a fast and simple method using a timed motion history image (tMHI) for representing motion from the gradients in successively layered silhouettes. This representation can be used to (a) determine the current pose of the object and (b) segment and measure the motions induced by the object in a video scene. These segmented regions are not “motion blobs”, but instead are motion regions that are naturally connected to parts of the moving object. This method may be used as a very general gesture recognition “toolbox”. We demonstrate the approach with recognition of waving and overhead clapping motions to control a music synthesis program. Accepted: 13 August 2001     

Range image segmentation using local approximation of scan lines with application to CAD model acquisition

Abstract. Automatic acquisition of CAD models from existing objects requires accurate extraction of geometric and topological information from the input data. This paper presents a range image segmentation method based on local approximation of scan lines. The method employs edge models that are capable of detecting noise pixels as well as position and orientation discontinuities of varying strengths. Region-based techniques are then used to achieve a complete segmentation. Finally, a geometric representation of the scene, in the form of a surface CAD model, is produced. Experimental results on a large number of real range images acquired by different range sensors demonstrate the efficiency and robustness of the method. Received: 1 August 2000 / Accepted: 23 January 2002 Correspondence to: I. Khalifa     

Hand image segmentation using color and RCE neural network

This paper presents a color segmentation method based on RCE neural network for hand image segmentation in the gesture-based human–service robot interaction system. The study on skin color distributions in different color spaces indicates that skin colors cluster in a small region in a color space. The RCE neural network characterizes the skin color distribution region using skin color prototypes together with their spherical influence fields during training stage, and identifies the skin regions in the color image during running stage. Experimental results have demonstrated the effectiveness of this method for the segmentation of various hand images as well as general color images with complex backgrounds.     

Agriculture satellite image segmentation using a modified artificial Hopfield neural network

Beekeeping plays an important role in increasing and diversifying the incomes of many rural communities in Kingdom of Saudi Arabia. However, despite the region’s relatively good rainfall, which results in better forage conditions, bees and beekeepers are greatly affected by seasonal shortages of bee forage. Because of these shortages, beekeepers must continually move their colonies in search of better forage. The aim of this paper is to determine the actual bee forage areas with specific characteristics like population density, ecological distribution, flowering phenology based on color satellite image segmentation. Satellite images are currently used as an efficient tool for agricultural management and monitoring. It is also one of the most difficult image segmentation problems due to factors like environmental conditions, poor resolution and poor illumination. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper Hopfield neural network (HNN) is introduced as Pixel clustering based segmentation method for agriculture satellite images.     

Filtering and segmentation of digitized land use map images

One important step in the analysis of digitized land use map images is the separation of the information in layers. In this paper we present a technique called Selective Attention Filter which is able to extract or enhance some features of the image that correspond to conceptual layers in the map by extracting information from results of clustering of local regions on the map. Different parameters can be used to extract or enhance different information on the image. Details on the algorithm, examples of application of the filter and results are also presented. Received: October 1, 1997 / Revised June 16, 1998     

Maximum mutual information training for an online neural predictive handwritten word recognition system

In this paper, we present a hybrid online handwriting recognition system based on hidden Markov models (HMMs). It is devoted to word recognition using large vocabularies. An adaptive segmentation of words into letters is integrated with recognition, and is at the heart of the training phase. A word-model is a left-right HMM in which each state is a predictive multilayer perceptron that performs local regression on the drawing (i.e., the written word) relying on a context of observations. A discriminative training paradigm related to maximum mutual information is used, and its potential is shown on a database of 9,781 words. Received June 19, 2000 / Revised October 16, 2000     

Traffic and video quality with adaptive neural compression

Video sequences are major sources of traffic for broadband ISDN networks, and video compression is fundamental to the efficient use of such networks. We present a novel neural method to achieve real-time adaptive compression of video. This tends to maintain a target quality of the decompressed image specified by the user. The method uses a set of compression/decompression neural networks of different levels of compression, as well as a simple motion-detection procedure. We describe the method and present experimental data concerning its performance and traffic characteristics with real video sequences. The impact of this compression method on ATM-cell traffic is also investigated and measurement data are provided.     

A practical use of cellular neural networks: the stereo-vision problem as an optimisation

A variational way of deriving the relevant parameters of a cellular neural network (CNN) is introduced. The approach exploits the CNN spontaneous internal-energy decrease and is applicable when a given problem can be expressed in terms of an optimisation task. The presented approach is fully mathematical as compared with the typical heuristic search for the correct parameters in the literature on CNNs. This method is practically employed in recovering information on the three-dimensional structure of the environment, through the stereo vision problem. A CNN able to find the conjugate points in a stereogram is fully derived in the proposed framework. Results of computer simulations on several test cases are provided. Received: 1 August 1997 / Accepted: 29 September 1999     

An empirical measure of the performance of a document image segmentation algorithm

Document image segmentation is the first step in document image analysis and understanding. One major problem centres on the performance analysis of the evolving segmentation algorithms. The use of a standard document database maintained at the Universities/Research Laboratories helps to solve the problem of getting authentic data sources and other information, but some methodologies have to be used for performance analysis of the segmentation. We describe a new document model in terms of a bounding box representation of its constituent parts and suggest an empirical measure of performance of a segmentation algorithm based on this new graph-like model of the document. Besides the global error measures, the proposed method also produces segment-wise details of common segmentation problems such as horizontal and vertical split and merge as well as invalid and mismatched regions. Received July 14, 2000 / Revised June 12, 2001[-1mm]     

小波消噪和神经网络的网络流量混沌预测

网络流量数据中含有大量噪声,对网络流量预测精度产生不利影响,为此,提出一种小波消噪和神经网络相融合的网络流量混沌预测模型。采用小波技术对网络流量数据进行消噪处理,采用关联维数确定BP神经网络输入变量个数,采用BP神经网络建立网络流量预测模型。结果表明,与消噪前比,小波消噪和神经网络模型更能准确刻画网络流量的变化趋势,有效提高了网络流量的预测精度,为非线性预测问题提供了一种新的研究思路。