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An image quality index plays an increasingly vital role in image processing applications for dynamic monitoring and quality adjustment, optimization and parameter setting of the imaging systems, and finally benchmarking the image processing techniques. All the above goals highly require a sustainable quantitative measure of image quality. This manuscript analytically reviews the popular reference-based metrics of image quality which have been employed for the evaluation of image enhancement techniques. The efficiency and sustainability of eleven indexes are evaluated and compared in the assessment of image enhancement after the cancellation of speckle, salt and pepper, and Gaussian noises from MRI images separately by a linear filter and three varieties of morphological filters. The results indicate more clarity and sustainability of similarity-based indexes. The direction of designing a universal similarity-based index based on information content of the image is suggested as a future research direction.

Image representation through local descriptors is a research hotspot in computer vision. In this letter, we propose a novel local image descriptor based on intensity permutation and zone division. The oFAST detector is first employed to detect keypoints with orientations, and then steered patterns are applied to sample rotation-invariant points within the local keypoint patch. In the step of local patch description, intensity permutation and zone division are implemented to construct our descriptor, with the advantages of inherent robustness and invariance to monotonic brightness changes. Our proposed algorithm performed well in the experiments on benchmark dataset for descriptor evaluation.

The goal of this paper is to describe an exploration system for large image databases in order to help the user understand the database as a whole, discover hidden relationships, and formulate insights with minimum effort. While the proposed system works with any type of low-level feature representation of images, we describe our system using color information. The system is built in three stages: the feature extraction stage in which images are represented in a way that allows efficient storage and retrieval results closer to the human perception; the second stage consists of building a hierarchy of clusters in which the cluster prototype, as the electronic identification ( e ID) of that cluster, is designed to summarize the cluster in a manner that is suited for quick human comprehension of its components. In a formal definition, an electronic IDentification ( e ID) is the most similar image to the other images from a corresponding cluster; that is, the image in the cluster that maximizes the sum of the squares of the similarity values to the other images of that cluster. Besides summarizing the image database to a certain level of detail, an e ID image will be a way to provide access either to another set of e ID images on a lower level of the hierarchy or to a group of perceptually similar images with itself. As a third stage, the multi-dimensional scaling technique is used to provide us with a tool for the visualization of the database at different levels of details. Moreover, it gives the capability for semi-automatic annotation in the sense that the image database is organized in such a way that perceptual similar images are grouped together to form perceptual contexts. As a result, instead of trying to give all possible meanings to an image, the user will interpret and annotate an image in the context in which that image appears, thus dramatically reducing the time taken to annotate large collection of images.

Traffic sign recognition (TSR) is an essential research issue in the design of driving support system and smart vehicles. In this paper, we propose a permutation-based image feature to describe traffic signs, which has an inherent advantage of illumination invariance and fast implementation. Our proposed feature LIPID (local image permutation interval descriptor) employs interval division and zone number assignment on order permutation of pixel intensities, and takes the zone numbers as the descriptor. A comprehensive performance evaluation on German Traffic Sign Recognition Benchmark (GTSRB) dataset is carried out, which reveals the great performance of our proposed method. Experiment results exhibit that our feature outperforms some state-of-the-art descriptors, showing a potential prospect in TSR applications.

The problem of face detection in images – locating image areas corresponding to human faces – has received a considerable amount of attention in recent years due to numerous possible applications. In this paper, we describe a face detection system for an image annotation task that requires detection of faces at multiple scales against a complex background. The proposed system is a top-down system; it uses the presence of skin tone pixels coupled with shape and face-specific features to locate faces in images. The main distinguishing feature of the proposed system is its use of an iterative region partitioning procedure to generate candidate face regions. To date, the performance of the system has been tested with over 200 test images of varying complexity with promising results.

This paper presents a scheme for image retrieval that lets a user retrieve images either by exploring summary views of the image collection at diAerent levels or by similarity retrieval using query images. The proposed scheme is based on image clustering through a hierarchy of self-organizing feature maps. While the suggested scheme can work with any kind of low-level feature representation of images, our implementation and description of the system is centered on the use of image color information. Experimental results using a database of 2100 images are presented to show the eAcacy of the suggested scheme. ” 1999 Published by Elsevier Science B.V. All rights reserved.

Digital video is becoming widely available in compressed form, such as a motion JPEG or MPEG coded bitstream. In applications such as video browsing or picture-in-picture, or in transcoding for a lower bit rate, there is a need to downscale the video prior to its transmission. In such instances, the conventional approach to generating a downscaled video bitstream at the video server would be to first decompress the video, perform the downscaling operation in the pixel domain, and then recompress it as, say, an MPEG, bitstream for efficient delivery. This process is computationally expensive due to the motion-estimation process needed during the recompression phase. We propose an alternative compressed domain-based approach that computes motion vectors for the downscaled (N/2xN/2) video sequence directly from the original motion vectors for the N/spl times/N video sequence. We further discover that the scheme produces better results by weighting the original motion vectors adaptively. The proposed approach can lead to significant computational savings compared to the conventional spatial (pixel) domain approach. The proposed approach is useful for video severs that provide quality of service in real time for heterogeneous clients.

Conventional processing of JPEG or MPEG compressed image or video data involves first decompressing the data and applying the desired processing function, and then the processed data are recompressed for the purposes of transmission or storage. We propose an alternate processing pipeline which involves direct manipulation of the JPEG or MPEG compressed domain representation to achieve the desired spatial domain processing. For direct manipulation in the compressed domain, we develop a discrete cosine transform (DCT)-domain convolution theorem which besides exploiting the sparseness of the DCT-domain representation also exploits the orthogonality and symmetry in the DCT-domain representation. These properties lead to efficient compressed domain-based processing methods unlike their spatial domain counterparts, where such properties are not available. This theorem can be used in a variety of image and video editing functions when the image and video data are available only as a JPEG or MPEG bitstream. We illustrate the use of the DCT-domain convolution theorem in a typical video editing application such as video bluescreen editing.

The organization of video information for video databases requires segmentation of a video into its constituent shots and their subsequent characterization in terms of content and camera work. In this paper, we look at these two steps using compressed video data directly. For shot detection, we suggest a scheme consisting of comparing intensity, row, and column histograms of successive I frames of MPEG video using the chi-square test. For characterization of segmented shots, we address the problem of classifying shot motion into different categories using a set of features derived from motion vectors of P and B frames of MPEG video. The central component of the proposed shot motion characterization scheme is a decision tree classifier built through a process of supervised learning. Experimental results using a variety of videos are presented to demonstrate the effectiveness of performing shot detection and characterization directly on compressed video.

Image representation through local descriptors is the basis of numerous computer vision applications. In the past decade, many local image descriptors such as SIFT and SURF have been proposed, yet algorithms requiring low memory and computation complexity are still preferred. Binary descriptors such as BRIEF have been suggested to satisfy this demand, showing a comparable performance but much faster computation speed. In this paper, we propose a novel local image descriptor, LIPID, which employs intensity permutation and interval division to yield an effective performance in terms of speed and recognition. Our method is inspired by LUCID, proposed by Ziegler and Christiansen [8]. An extensive evaluation on the well-known benchmark datasets reveals the robustness and effectiveness of LIPID as well as its capability to handle illumination changes and texture images.

Many applications in image science require similarity retrieval of an image from a large collection of images. In such cases, image indexing becomes important for efficient organization and retrieval of images. This paper addresses this issue in the context of a database of handwritten signature images and describes a system for similarity retrieval and identification of handwritten signature images. The proposed system uses a set of geometric and topologic features to map a signature image into two strings of finite symbols. A local associative indexing scheme is then used on the strings to organize and search the signature image database. The advantage of the local associative indexing is that it is tolerant of missing features and allows queries even with partial signatures. The performance of the system has been tested with an image database of 120 signatures. The results obtained indicate that the proposed system is able to identify signatures with great accuracy even when a part of a signature is missing.

A new multistage Hough transform approach to the problem of ellipse detection in images is presented. The proposed approach is based on the polar and pole definition of conics and its advantage lies in avoiding the propagation of parameter estimation error over the successive stages of the Hough transform. The proposed method is capable of detecting partially visible ellipses, overlapping ellipses, and groups of concentric ellipses. A set of experimental results is presented to demonstrate these capabilities of the suggested approach.

CASMIL aims to develop a cost-effective and efficient approach to monitor and predict deformation during surgery, allowing accurate, and real-time intra-operative information to be provided reliably to the surgeon.CASMIL is a comprehensive Image-guided Neurosurgery System with extensive novel features. It is an integration of various modules including rigid and non-rigid body co-registration (image-image, image-atlas, and image-patient), automated 3D segmentation, brain shift predictor, knowledge based query tools, intelligent planning, and augmented reality. One of the vital and unique modules is the Intelligent Planning module, which displays the best surgical corridor on the computer screen based on tumor location, captured surgeon knowledge, and predicted brain shift using patient specific Finite Element Model. Also, it has multi-level parallel computing to provide near real-time interaction with iMRI (Intra-operative MRI). In addition, it has been securely web-enabled and optimized for remote web and PDA access.A version of this system is being used and tested using real patient data and is expected to be in use in the operating room at the Detroit Medical Center in the first half of 2006.CASMIL is currently under development and is targeted for minimally invasive surgeries. With minimal changes to the design, it can be easily extended and made available for other surgical procedures.

To reason about the meaning of an image, useful information should be provided with that image; however, images often contain little to no textual information about the objects they are depicting, which is the precise reason why there is a need for CBIR systems that exploit only the correlations present in the raw pixel data. In this paper, we proposed a new type of image feature, which consists of patterns of colors and intensities that capture the latent associations among images and primitive features in such a way that the noise and redundancy are eliminated. We introduced the synobin, a new term for content-based image retrieval literature, which is the equivalent of a synonym word from text retrieval, to name the bin that is synonymous with other bins of a color feature, in the sense that they are similarly used across the image database. In a formal definition, a group of synobins is given by the most important bins participating in forming of a useful pattern, that is, the bins having the highest coefficients in the linear combination defining that pattern. Incorporating our feature model into a CBIR system moves the research in image retrieval beyond simple matching of images based on their primitive features and creates a ground for learning image semantics from visual content. A system developed using our proposed feature model will have the capability of learning associations not only between semantic concepts and images, but also between semantic concepts and patterns. We evaluated the performance of our system based on the retrieval accuracy and on the perceptual similarity order among retrieved images. When compared to standard image retrieval methods, our preliminary results show that even if the feature space was reduced to only 3%–5% of the initial space, the accuracy and perceptual similarity for our system remain the same or better depending on the category of images.

In selective object segmentation, the goal is to extract the entire object of interest without regards to homogeneous regions or object shape. In this paper we present the selective image segmentation problem as a classification problem, and use active learning to train an image feature classifier to identify the object of interest. Since our formulation of this segmentation problem uses human interaction, active learning is used for training to minimize the training effort needed to segment the object. Results using several images with known ground truth are presented to show the efficacy of our approach for segmenting the object of interest in still images. The approach has potential applications in medical image segmentation and content-based image retrieval among others.

In this paper, we present a computer-assisted image browsing system based on pathfinder networks. Similarity of images to one another is determined through a proposed method of automatic shape feature discovery. Local features are generated by clustering small (on the order of 10 by 10 pixels) binary image blocks culled from the edge analysis of images in the database and using the cluster means as the local feature detectors. The clustering method for the binary image blocks is based on the Hausdorff metric of distance between sets of points. Relationships between local features then determine the similarity between images. Pathfinder networks are then used to visually represent similarity between images. The results are presented on a database containing three categories of images.

This paper describes a method for creating agents for locating images of specific categories such as sky, vegetation, fire, and smoke. The method uses only color information, and is based on vector quantization to build a category specific codebook from a set of training images. The method is shown to yield categorization of images collected from several web sites on Internet with a high success rate. The method can be used as an aid to image annotation or as a way to filter images in a content-based image retrieval system.

The automatic recognition of video clips is an important capability with applications in broadcast monitoring for content theft and adherence to advertisement campaign. We present an approach for video clip recognition based on HMM and Gaussian mixture modeling for modeling video and audio streams respectively. The approach is used to model TV commercials. The recognition results of using single stream and joint models are compared The error rate of 0.01% is achieved when the joint audiovisual processing model is employed.

Three dimensional computed tomography is a computationally intensive procedure, requiring large amounts of RAM and processing power. Parallel methods for two dimensional computed tomography have been studied, but not extended or applied to 3D cone beam tomography. A discussion on how the 3D cone beam tomography problem can be implemented in parallel using MPI is given. We show an improvement in performance from 58.2% to 81.8% processor utilization in a heterogeneous cluster of workstations by load balancing. A 96.8% efficiency was seen using a 2 processor SMP.

Textural features are often consider as one of the most powerful features in describing the intrinsic physical properties of object surfaces in a scene. In this paper, we propose characterizing image textures by a least-squares hyperplane fitting of their image multidimensional primitives. The adaptive implementation of the hyperplane fitting process is carried out by a newly proposed nonlinear supervised neural unit trained by a constrained form of anti-Hebbian learning. Experimental results are presented to demonstrate the performance of the proposed texture characterization model in image classification and segmentation applications. >

Closed-loop video rendering implies generating video streams in compressed form given as input video streams that are in compressed form such as JPEG or MPEG bit streams. Closed-loop video rendering has significant advantages over open-loop schemes including reduced storage requirements which in turn reduces memory disk swapping time that constitutes a large portion of the time spent in the desktop editing of massive video material. Processing in the compressed domain also offers faster processing speeds due to the smaller data size present in the compressed domain. This approach also improves image quality since multiple decompression and compression processes are avoided during the editing process. Based on MPEG, a standard video compression scheme, the authors discuss fundamental techniques, analyze pros and cons, implications and preferences for a compressed domain video rendering system. A software-based system for real-time video rendering is proposed with preliminary results demonstrating the feasibility of their closed-loop MPEG video rendering approach.

A radial basis function (RBF) neural network is considered as one of the universal input-output functional mapping learning systems. Important issues in designing an efficient RBF neural network are the number of neurons and the shape and location of neurons to define local receptive fields in feature space. This paper presents a solution to these problems using decision tree partitioning. A conversion algorithm from decision tree to RBF neural network is described. Two examples are presented to illustrate the proposed approach.

In this paper, we propose a new type of image feature, which consists of patterns of colors and intensities that capture the latent associations among images and primitive features in such a way that the noise and redundancy are minimized. Incorporating our feature model into a Content-based Image Retrieval (CBIR) system moves the research in image retrieval beyond simple matching of images based on their primitive features and creates a ground for learning image semantics from visual content. A system developed using our proposed feature model, will have the capability of learning associations between not only semantic concepts and images, but also between semantic concepts and patterns. We evaluated the performance of our system based on the retrieval accuracy and on the perceptual similarity order among retrieved images. When compared to standard image retrieval methods, our preliminary results show that, even if the feature space was reduced to a significantly lower dimensional space, the accuracy and perceptual similarity for our system remain the same or better depending on the category of images.

In image similarity retrieval systems, color is one of the most widely used features. Users who are not well versed with the image domain characteristics might be more comfortable in working with an Image Retrieval System that allows specification of a query in terms of keywords, thus eliminating the usual intimidation in dealing with very primitive features. In this paper we present two approaches to automatic image annotation, by finding those rules underlying the links between the low-level features and the high-level concepts associated with images. One scheme uses global color image information and classification tree based techniques. Through this supervised learning approach we are able to identify relationships between global color-based image features and some textual decriptors. In the second approach, using low-level image features that capture local color information and through a k-means based clustering mechanism, images are organized in clusters such that images that are similar are located in the same cluster. For each cluster, a set of rules is derived to capture the association between the localized color-based image features and the textual descriptors relevant to the cluster.

We proposed an omni-face tracking system for video annotation in this paper, which is designed to find faces from arbitrary views in complex scenes. The face detector first locates potential faces in the input by performing skin-tone detection. The subsequent processing consists of two largely independent components, the frontal face module and the side- view face module, responsible for finding frontal-view and side-view faces, respectively. The frontal face module uses a region-based approach wherein regions of skin-tone pixels are analyzed for gross oval shape and the presence of facial features. In contrast, the side-view face module follows an edge-based approach to look for curves similar to a side-view profile. To extract the trajectories of faces, the temporal continuity between consecutive frames within the video shots is considered to speed up the tracking process. The main contribution of this work is being able to find faces irrespective of their poses, whereas contemporary systems deal with frontal-view faces only. Information regarding to human faces is encoded in XML format for semantic video content representation. The effectiveness of human face for video annotation is demonstrated in a TV program classification system that categories the input video clip into predefined types. It is shown that the classification accuracy is improved saliently by the employment of face information.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An omni-face detection scheme for image/video content description is proposed in this paper. It provides the ability to extract high-level features in terms of human activities rather than low-level features like color, texture and shape. The system relies on an omni-face detection system capable of locating human faces over a broad range of views in color images or videos with complex scenes. It uses the presence of skin-tone pixels coupled with shape, edge pattern and face-specific features to locate faces. The main distinguishing contribution of this work is being able to detect faces irrespective of their poses, including frontal-view and side-view, whereas contemporary systems deal with frontal-view faces only. The other novel aspects of the work lie in its iterative candidate filtering to segment objects from extraneous region, the use of Hausdorff distance-based normalized similarity measure to identify side-view facial profiles, and the exploration of hidden Markov model (HMM) to verify the presence of a side-view face. Image and video can be assigned with semantic descriptors based on human face information for later indexing and retrieval.

Currently, most approaches for object tracking are under spatial domain using optical flow and depth, or some model- based methods which need to uncompress those video sequences then do further disposal. The computation for uncompressing is expensive and not good for real time control. Although there are also some researchers doing object tracking under compressed domain, they only use part of DCT values of I frames in a video sequence, which doesn't take advantage of the information under compressed domain. In this paper we consider a new method for object tracking which uses the information only supplied in compressed domain through the MPEG encoder. The main scheme we have performed is to get the motion vectors of P and B frames directly from the MPEG video without decompressing it then cluster objects based on the motion vectors. In particular, camera motion is also taken into account since camera's motion can influence the objects' motion and segmentation results dramatically. Experiments based on the method mentioned above have been carried out in several videos. The results obtained indicate that tracking object in compressed domain is very promising.

Web-WISE is a system designed to address the need for efficient content-based seeking and retrieval of images on the web. It supports searching by multi-features, including color and texture. Web-WISE contains three automatic components, which are 1): Internet Agent responsible for searching the Web to fetch images; 2): Analysis Agent, which extracts the color and texture features of color JPEG images directly from compressed domain and 3): Query Agent, which explores the image database using certain metrics and returns a set of images (thumbnails) visually similar to the query image. Due to its generic design, adding other features to the system will be very natural, including the ability to index and retrieve other types of visual information and retrieval based on edge and shape. This paper presents the theoretic and practical background of the system, the schemes of these three components and the performance of the system.

Color is one of the most widely used features for image similarity retrieval. Most of the existing image similarity retrieval schemes employ either global or local color histogramming. In this paper, we explore the use of localized dominant hue and saturation values for color-based image similarity retrieval. This scheme results in a relatively compact representation of color images for similarity retrieval. Experimental results comparing the proposed representation with global and local color histogramming are presented to show the efficacy of the suggested scheme.

Video content characterization is a challenging problem in video databases. The aim of such characterization is to generate indices that can describe a video clip in terms of objects and their actions in the clip. Generally, such indices are extracted by performing image analysis on the video clips. Many such indices can also be generated by analyzing the embedded audio information of video clips. Indices pertaining to context, scene emotion, and actors or characters present in a video clip appear especially suitable for generation via audio analysis techniques of keyword spotting, and speech and speaker recognition. In this paper, we examine the potential of speaker identification techniques for characterizing video clips in terms of actors present in them. We describe a three-stage processing system consisting of a shot boundary detection stage, an audio classification stage, and a speaker identification stage to determine the presence of different actors in isolated shots. Experimental results using the movie A Few Good Men are presented to show the efficacy of speaker identification for labeling video clips in terms of persons present in them.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Chroma-keying or blue screen matting is an important video editing operational. In blue screen matting, everything in the image that has the 'user specified level for the blue channel' is 'keyed' out and replaced by either another image or a color from a color generator. We develop a technique for blue screen matting that manipulates the image when the information for the image is available only in compressed form such as a JPEG or MPEG bitstream. Specifically, for the compressed domain approach, we show that the matting process is a convolution operation; hence, we develop a DCT convolution theorem. The DCT convolution theorem can be used to show that the compressed domain approach proposed in this paper provides a significant reduction in computation complexity compared to previously developed approaches which were mostly adhoc techniques. The convolution theorem exploits the sparseness as well as the orthogonality of the data available in the DCT domain and thus yields an efficient algorithm for he chroma-keying or blue screen matting. The algorithm extends to the DCT domain concepts such as alpha channel and premultiplied alpha. The method is also extended to MPEG video domain wherein we explore the efficiency of the matting process when interframe coding is used.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The scanline algorithms are popular in a computer graphics for complex geometric manipulations. The main characteristic of the scanline algorithms is that a geometric transformation is decomposed into multiples transforms with each pass operating only along row or column scanlines. This leads to conversion of 2D image manipulation problems to straightforward 1D problems resulting in simple and systematic methods. The goal of this work is to examine the scanline approach for manipulation of transform-compressed images without decompressing them. We show how the scanline algorithms for rotation and projective mapping can be developed for JPEG/DCT images. The performance of the proposed scanline algorithms is evaluated with respect to quality, speed, and control and memory overhead.

The major problem facing video databases is that of content characterization of video clips once the cut boundaries have been determined. The current efforts in this direction are focussed exclusively on the use of pictorial information, thereby neglecting an important supplementary source of content information, i.e. the embedded audio or sound track. The current research in audio processing can be readily applied to create many different video indices for use in Video On Demand (VOD), educational video indexing, sports video characterization, etc. MPEG is an emerging video and audio compression standard with rapidly increasing popularity in multimedia industry. Compressed bit stream processing has gained good recognition among the researchers. We have also demonstrated feature extraction in MPEG compressed video which implements a majority of scene change detection schemes on compressed video. In this paper, we examine the potential of audio information for content characterization by demonstrating the extraction of widely used features in audio processing directly from compressed data stream and their application to video clip classification.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper examines the issue of direct extraction of low level features from compressed images. Specifically, we consider the detection of areas of interest and edges in images compressed using the discrete cosine transform (DCT). For interest areas, we show how a measure based on certain DCT coefficients of a block can provide an indication of underlying activity. For edges, we show using an ideal edge model how the relative values of different DCT coefficients of a block can be used to estimate the strength and orientation of an edge. Our experimental results indicate that coarse edge information from compressed images can be extracted up to 20 times faster than conventional edge detectors.

In recent years, databases have evolved from storing pure textual information to storing multimedia information -- text, audio, video, and images. With such databases comes the need for a richer set of search keys that include keywords, shapes, sounds, examples, sketches, color, texture and motion. In this paper we address the problem of image retrieval where keys are object shapes or user sketches. In our scheme, shape features are extracted from each image as it is stored. The image is first segmented and points of high curvature are extracted. Regions surrounding the points of high curvature are used to compute feature values by comparing the regions with a number of references. The references themselves are picked out from the set of orthonormal wavelet basis vectors. An ordered set of distance measures between each local region and the wavelet references form a feature vector. When a user queries the database through a sketch, the feature vectors for high curvature points on the sketch are determined. An efficient nearest neighbor search then yields a set of images which contain objects that match the user's sketch closely. The process is completely automated. Initial experimental results are presented.

One of the challenging problems in video databases is the organization of video information. Segmenting a video into a number of clips and characterizing each clip has been suggested as one mechanism for organizing video information. This approach requires a suitable method to automatically locate cut points in a video. One way of finding such cut points is to determine the boundaries between consecutive camera shots. In this paper, we address this as a statistical hypothesis testing problem and present three tests to determine cut locations. All the three tests are such that they can be applied directly to the compressed video. This avoids an unnecessary decompression-compression cycle, since it is common to store and transmit digital video in compressed form. As our experimental results indicate, the statistical approach permits accurate detection of scene changes induced through straight as well as optical cuts.

Many applications require similarity retrieval of an image from a large collection of images. In such cases, image indexing becomes important for efficient organization and retrieval of images. This paper addresses this issue in the context of a database of signature images and describes a system for similarity retrieval and recognition of signature images. The proposed system uses a set of geometric and topological features to map a signature image into two strings of finite symbols. A local associative indexing scheme is then used on the strings to organize and search the signature database. The advantage of the local associative indexing is that it is tolerant of missing features and allows queries even with partial signatures. The performance of the system has been tested with promising results with a signature database of 120 signatures.

Spatial image filtering is a method by which an image can be enhanced. The method involves computing correlation of a mask and input image to produce desired image. This requires a number of computations consisting of addition and division operations. Such operations are repeated multiple times for common areas of overlapping masks as we shift the mask over consecutive pixels. As a result, there is a significant number of redundant operations that slows down the spatial filtering process. In this paper, we propose a new method to eliminate redundant operations and we apply it to volumetric MRI images using mean filter. We analyse the complexity of the proposed method and compare its processing speed to that of the conventional implementation of the mean filter. Our method shows up to one order of magnitude improvement in processing speed over the conventional method especially on 3D images with large filter masks.

The problem of determining the position and orientation of a mobile robot has been addressed by several researchers using sensors of different modalities, including video cameras. Invariably, all the vision-based approaches for robot localization consider that the camera is mounted on the robot and that the robot working environment is assumed to contain prominent landmarks at known locations. In this paper we propose a robot localization scheme where the robot itself serves as the landmark for cameras that are positioned in the environment to cover the entire work area of the robot. Although the proposed approach is applicable for the robots of any regular shape, we develop the solution to the localization problem by assuming a cylindrical shape for the robot. A compete mathematical analysis of the localization problem is given by extending the three-dimensional structure-from-rotational motion approach to the present task. We also examine the implementation issue of the proposed approach and present experimental results to show its effectiveness.

In addition to invariance with respect to certain geometric transformations, there are two other key requirements for any shape recognition system. It should be flexible enough to adopt to a variety of sets of shapes with minimal training; and it should be capable of performing even in presence of occlusion. This paper describes one such shape recognition system that is currently under development. The system is based on the redundant hashing scheme of Kohonen for recognizing and correcting misspelt words. The current version of the system is meant for 2-D shapes; however, the same approach is applicable to 3-D shape recognition. In the present implementation for 2-D shapes, a polygonal approximation of the given shape is encoded in the form of a string. The encoded string is then used to generate a very small set of shape hypotheses through the use of redundant hashing. The best hypothesis from the set of competing hypotheses is selected by a very simple matching scheme followed by a verification phase based on rotation transformation. The experiments thus far indicate that the system is capable of recognizing shapes in presence of occlusion with about 5% error. The system has significant ability to adopt to new sets of shapes; it does not require any training except the building of hash index table and the corresponding shape dictionary.

Echocardiographic images are generally of poor quality. From a strict gray scale perspective, there are few edges which correspond to the underlying anatomy. Trained ultrasonographers must use considerable a priori information regarding normal human anatomy and use this information extensively to 'fill in' the missing and scanty details of a typical echocardiogram. Due to these constraints, the conventional image processing approaches often fail miserably for echocardiographic images. Knowledge-based approaches for segmenting echo images have been proposed in the recent past; these appear to be much more successful. One of the limitations of knowledge-based approaches is that the acquisition of knowledge and the formation of rules is a relatively difficult task. Moreover, there are instances where experts may have different opinions; this is specially true in the context of echocardiographic images. Additionally, it is much more difficult to codify the expert's knowledge at the image level. Considering these factors plus the typical medical research scenario, where a series of images are acquired at one sitting for a patient, we present in this paper an approach for segmenting echocardiographic image sequences by directly acquiring the expert's knowledge at the image level. The present implementation yields a look up table capturing the expert's knowledge leading to near real time segmentation of echocardiographic images. The results obtained are quite in agreement with the segmentations obtained through trained ultrasonographers.

This PDF file contains the editorial “Guest Editorial: Special Section on Image and Video Databases” for JEI Vol. 7 Issue 02

Scanline algorithms are popular in computer graphics for complex geometric manipulations. The main characteristic of scanline algorithms is that a geometric transformation is decomposed into multipass transforms with each pass operating only along row or column scanlines. This leads to conversion of 2-D image manipulation problems to straightforward 1-D problems resulting in simple and systematic methods. The goal of this work is to examine the scanline approach for manipulation of transform-compressed images without decompressing them. We show how the scanline algorithms for rotation and projective mapping can be developed for JPEG/DCT images. The performance of the proposed scanline algorithms is evaluated with respect to quality, speed, and control and memory overhead.

One of the challenging problems in video databases is the organisation of video information. Segmenting a video into a number of clips and characterising each clip has been suggested as one mechanism for organising video information. This approach requires a suitable method to automatically locate cut points (boundaries between consecutive camera shots in a video). Several existing techniques solve this problem using uncompressed video. Since video is increasingly being captured, moved, and stored in compressed form, there is a need for detecting shot boundaries directly in compressed video. The authors address this issue and show certain feature extraction steps in MPEG compressed video that allow the implementation of most of the existing cut detection methods developed for uncompressed video for MPEG video stream. They also examine the performance of three tests for cut detection by viewing the problem of cut detection as a statistical hypothesis testing problem. As the experimental results indicate, the statistical hypothesis testing approach permits fast and accurate detection of video cuts.

One of the vital problems in motion analysis is to match a set of feature points over an image sequence. In this paper, we solve this problem by relying on continuity of motion which is known to play an important role in the perception of apparent motion in the human visual system. We propose a relaxation algorithm for feature point matching where the formation of smooth trajectories over space and time is favored. Experimental results on laboratory generated and real scene sequences are presented to demonstrate the merit of out approach.

Identifying the same physical point in more than one image, the correspondence problem, is vital in motion analysis. Most research for establishing correspondence uses only two frames of a sequence to solve this problem. By using a sequence of frames, it is possible to exploit the fact that due to inertia the motion of an object cannot change instantaneously. By using smoothness of motion, it is possible to solve the correspondence problem for arbitrary motion of several nonrigid objects in a scene. We formulate the correspondence problem as an optimization problem and propose an iterative algorithm to find trajectories of points in a monocular image sequence. A modified form of this algorithm is useful in case of occlusion also. We demonstrate the efficacy of this approach considering synthetic, laboratory, and real scenes.

A critical problem in automatic target recognition is the extraction and tracking of moving targets in high clutter, low resolution, low contrast thermal imagery. This problem is further exacerbated by the large distances that typically exist between the sensor and the targets. In this paper, a new paradigm called motion filtering is presented for exploiting motion to extract and track targets. The main feature of the present approach is the emphasis on the motion information. Experimental results from two sequences of tactical situations are presented to exemplify the capabilities of the proposed approach.

One of the vital problems in motion analysis is to match a set of feature points over an image sequence. In this paper, we solve this problem by relying on continuity of motion which is known to play an important role in motion perception in biological vision systems. We propose a relaxation algorithm for feature point matching where the formation of smooth trajectories over space and time is favored. Experimental results on a laboratory generated as well as a real scene sequence are presented to demonstrate the merit of our approach.

A method for extracting road boundaries using the monochrome image of a visual road scene is presented. The statistical information regarding the intensity levels present in the image along with some geometrical constraints concerning the road are the basics of this approach. Results and advantages of this technique compared to others are discussed. The major advantages of this technique, when compared to others, are its ability to process the image in only one pass, to limit the area searched in the image using only knowledge concerning the road geometry and previous boundary information, and dynamically adjust for inconsistencies in the located boundary information, all of which helps to increase the efficacy of this technique.

extraction. This is in contrast to the early approaches of twoframe analysis. The use of long image sequences permitssome additional constraints like the continuity of motion tobe exploited provided that the frames are acquired at regularshort intervals of time. Motion analysis approaches usinglong image sequences are also more robust to noise due tothe redundancy of input data. This makes these approachesmore suitable for realistic applications.For motion analysis approaches using an extended imagesequence, it is very important that any motion discontinuityacross time be identified. This is independent of the goal ofmotion analysis, as such a detection of motion discontinuitywill permit the integration of desired information only fromthe appropriate part of the extended image sequence. As an