Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Michael Caputo
michael.vislab@gmail.com

Last updated: June 15, 2016

 

 

Target/Object Recognition

Visual Learning by Evolutionary and Coevolutionary Feature Synthesis

We present a novel method for learning complex concepts/hypotheses directly from raw training data. The task addressed here concerns data-driven synthesis of recognition procedures for real-world object recognition. The method uses linear genetic programming to encode potential solutions expressed in terms of elementary operations, and handles the complexity of the learning task by applying cooperative coevolution to decompose the problem automatically at the genotype level. Extensive experimental results show that the approach attains competitive performance for three-dimensional object recognition in real synthetic aperture radar imagery.

Coevolution and Linear Genetic Programming for Visual Learning

We introduce a novel genetically-inspired visual learning method. Given the training images, this general approach induces a sophisticated feature-based recognition system, by using cooperative coevolution and linear genetic programming for the procedural representation of feature extraction agents. The paper describes the learning algorithm and provides a firm rationale for its design. An extensive experimental evaluation, on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery, shows the competitiveness of the proposed approach with human-designed recognition systems.

Visual Learning by Evolutionary Feature Synthesis

We present a novel method for learning complex concepts/hypotheses directly from raw training data. The task addressed here concerns data-driven synthesis of recognition procedures for real-world object recognition task. The method uses linear genetic programming to encode potential solutions expressed in terms of elementary operations, and handles the complexity of the learning task by applying cooperative coevolution to decompose the problem automatically. The training consists in coevolving feature extraction procedures, each being a sequence of elementary image processing and feature extraction operations. Extensive experimental results show that the approach attains competitive performance for 3-D object recognition in real synthetic aperture radar (SAR) imagery.

Evolutionary Feature Synthesis for Object Recognition

We've developed a coevolutionary genetic programming (CGP) approach to learn composite features for object recognition. The motivation for using CGP is to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. CGP, on the other hand, can try a very large number of unconventional combinations and these unconventional combinations yield exceptionally good results in some cases. The comparison with other classical classification algorithms is favourable to the CGP-based approach we've proposed.

Stochastic Models for Recognition of Occluded Targets

Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. Stochastic models provide some attractive features for pattern matching and recognition under partial occlusion and noise. We present a hidden Markov modeling based approach for recognizing objects in SAR images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. In order to improve performance we integrate these models synergistically using their probabilistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.

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Genetic Algorithm Based Feature Selection for Target Detection in SAR Images

A genetic algorithm (GA) approach is presented to select a set of features to discriminate the targets from the natural clutter false alarms in SAR images. A new fitness function based on minimum description length principle (MDLP) is proposed to drive GA and it is compared with three other fitness functions. Experimental results show that the new fitness function outperforms the other three fitness functions and the GA driven by it selected a good subset of features to discriminate the targets from clutters effectively.

Increasing the Discrimination of Synthetic Aperture Radar Recognition Models

The focus of this work is optimizing recognition models for synthetic aperture radar (SAR) signatures of vehicles to improve the performance of a recognition algorithm under the extended operating conditions of target articulation, occlusion, and configuration variants. The approach determines the similarities and differences among the various vehicle models. Methods to penalize similar features or reward dissimilar features are used to increase the distinguishability of the recognition model instances.

Predicting an Upper Bound on SAR ATR Performance

We present a method for predicting a tight upper bound on performance of a vote-based approach for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, as well as model factors such as structural similarity. The proposed method is validated using MSTAR public SAR data, which are obtained under different depression angles, configurations, and articulations.

Predicting Performance of Object Recognition

We present a method for predicting fundamental performance of object recognition. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, in addition to model similarity. This is unlike previous approaches, which consider only a subset of these factors. Performance is predicted in two stages. In the first stage, the similarity between every pair of model objects is captured. In the second stage, the similarity information is used along with statistical models of the data-distortion factors to determine an upper bound on the probability of recognition error. This bound is directly used to determine a lower bound on the probability of correct recognition.

Predicting Performance of Object Recognition

We present a method for predicting fundamental performance of object recognition. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, in addition to model similarity. This is unlike previous approaches, which consider only a subset of these factors. Performance is predicted in two stages. In the first stage, the similarity between every pair of model objects is captured. In the second stage, the similarity information is used along with statistical models of the data-distortion factors to determine an upper bound on the probability of recognition error. This bound is directly used to determine a lower bound on the probability of correct recognition.

Recognizing Occluded Objects in SAR Images

Recognition algorithms, based on local features, are presented that successfully recognize highly occluded objects in both XPATCH synthetic SAR signatures and real SAR images of actual vehicles from the MSTAR data. Extensive experimental results are presented for a basic recognition algorithm and for an improved algorithm. The results show the effect of occlusion on recognition performance in terms of probability of correct identification (PCI), receiver operating characteristic (ROC) curves, and confusion matrices.

Recognition of Articulated and Occluded Objects

A model-based automatic target recognition (ATR) system is developed to recognize articulated and occluded objects in Synthetic Aperture Radar (SAR) images, based on invariant features of the objects. The basic elements of the new recognition system are described and performance results are given for articulated, occluded and occluded articulated objects and they are related to the target articulation invariance and percent unoccluded.

Recognizing Articulated Objects in SAR Images

We introduced the first successful approach for recognizing articulated vehicles in real synthetic aperture radar (SAR) images. Although related to geometric hashing, our recognition approach is specifically designed for SAR, taking into account the great azimuthal variation and moderate articulation invariance of SAR signatures. We present a basic recognition system for the XPATCH data, and an improved recognition system that achieves excellent results with the more limited articulation invariance encountered with the real SAR targets in the MSTAR data.

Model-Based Recognition of Articulated Objects

A model-based matching technique for recognition of articulated objects (with two parts) and the poses of these parts on SAR (Synthetic Aperture Radar) images is presented. Using articulation invariants as features, the recognition system first hypothesized the pose of the larger part and then the pose of the smaller part. Geometric reasoning was carried out to correct identification errors. The thresholds for the quality of match were determined dynamically by minimizing the probability of a random match. Results are presented using SAR images of three articulated objects. The system performance was evaluated with respect to identification performance, accuracy of estimates for the poses of the objects parts, and noise.

A System for Model-Based Object Recognition in Perspective Aerial Images

Recognition of objects in complex, perspective aerial imagery was difficult because of occlusion, shadow, clutter and various forms of image degradation. A system for aircraft recognition under real-world conditions is presented. The approach was based on the use of a hierarchical database of object models and involved three key processes: the qualitative object recognition process performed heterogeneous model-based symbolic feature extraction and generic object recognition, the qualitative object recognition process performed heterogeneous model-based symbolic feature extraction and generic object recognition, and the primitive feature extraction process regulated the extracted features based on their saliency and interacted with the recognition and refining process.

A System for Model-based Object Recognition in Perspective Aerial Images

We present a system for aircraft recognition under real-world conditions. The particular approach is based on the use of a hierarchical database of object models and involves three key processes: (a) The qualitative object recognition process; (b) The refocused matching and evaluation process; and (c) The primitive feature extraction process . Experimental results showing the qualitative recognition of aircraft in perspective, aerial images are presented.

Bounding Fundamental Performance of Feature-Based Object Recognition

Performance prediction was a crucial step for transforming the field of object recognition from an art to a science so we addressed this problem in the context of a vote-based approach for object recognition using 2D point features. A method is presented for predicting tight lower and upper bounds on fundamental performance of the selected recognition approach. Performance bounds were predicted by considering data-distortion factors, in addition to model structural similarity. Given a statistical model of data uncertainty, the structural similarity between every pair of model objects was computed as a function of the relative transformation between them. Model-similarity information was then used along with statistical data-distortion models to predict bounds on the probability of correct recognition.

Geometrical and Magnitude Invariants for Recognition of Articulated and Non-Standard Objects

Using SAR scattering center locations and magnitudes as features, invariances with articulation (i.e. turret rotation for the T72 tank and ZSU 23/4 gun), with configurations variants (e.g. fuel barrels, searchlight, wire cables, etc.) and with a depression angle change was shown for real SAR images obtained from the MSTAR public data. This location and magnitude quasi-invariance was used as a basis for an innovative SAR recognition engine that successfully identified real articulated and non-standard configuration vehicles based on non-articulated, standard recognition models. Identification performance results are given as confusion matrices and ROC curves for articulated objects, for configuration variants, and for a small change in depression angle with the MSTAR data. The recognition rate is related to the percent of location and magnitude invariant scattering centers.

Interactive Target Recognition using a Database-Retrieval Oriented Approach

Recognition of Objects when the number of model objects becomes large was a challenging problem which made it increasingly difficult to view the object recognition problem as a “find the best match” problem. A database-retrieval oriented approach where the goal was to index, retrieve, rank, and output a few top-ranked models, according to their similarity with an input query object is presented. The approach consisted of three stages: feature-based representation of model objects and object-feature correspondence analysis, clustering and indexing of the model objects in the factor space, and ranking indexed models based on mutual information with query object. The approach was suitable for semi-automatic object recognition tasks which involved human interaction.

Performance Modeling of Feature-Based Classification in SAR Imagery

A method for modeling the performance of a vote-based approach for target classifications in SAR imagery is presented. In this approach, the geometric locations of the scattering centers was used to represent 2D model views of a 3D target for a specific sensor under a given viewing condition (azimuth, depression, and squint angles) and performance of such an approach was modeled in the presence of data uncertainty, occlusion, and clutter. The proposed method captured the structural similarity between model views, which played an important role in determining the classification performance and in particular, performance would improve if the model views were dissimilar and vice versa. The method consisted of the following steps: in the first step given a bound on data uncertainty, model similarity was determined by finding feature correspondence in the space of relative translations between each pair of model views, in the second step, statistical analysis was carried out in the vote , occlusion and clutter space, in order to determine the probability of misclassifying each model view, and finally in the third step, the misclassification probability was averaged for all model views to estimate the probability-of-correct-identification (PCI) plot as a function of occlusion and clutter rates. Validity of the method was demonstrated by comparing predicted PCI plots with ones that were obtained experimentally.

Predicting Object Recognition Performance under Data Uncertainty, Occlusion, and Clutter

A method for predicting the performance of an object recognition approach in the presence of data uncertainty, occlusion and clutter is presented. The recognition approach used a vote-based decision criterion, which selected the object/pose hypotheses that had the maximum number of consistent features (votes) with the scene data. The prediction method determined a fundamental, optimistic, limit on achievable performance by any vote-based recognition system. It captured the structural similarity between model objects, which was a fundamental factor in determining the recognition performance. Given a bound on data uncertainty, we determined the structural similarity between every pair of model objects. This was done by computing the number of consistent features between the two objects as a function of the relative transformation between them. Similarity information was then used, along with statistical models for data distortion, to estimate the probability of correct recognition (PCR) as a function of occlusion and clutter rates.

Target Recognition for Articulated and Occluded Objects in Synthetic Aperture Radar Imagery

Recognition of articulated occluded real-world man-made objects in Synthetic Aperture Radar (SAR) imagery had not been addressed in the field of image processing and computer vision. The traditional approach to object recognition in SAR imagery (at one foot or worse resolution) typically involved template matching methods, which were not suited for these cases because articulation or occlusion changed global features like the object outline and major axis. The performance of a model-based automatic target recognition (ATR) engine with articulated and occluded objects in SAR imagery was characterized based on invariant properties of the objects. Although the approach was related to geometric hashing, it was a novel approach for recognizing objects in SAR images. The novelty and power of the approach came from a combination of a SAR specific method for recognition, taking into account azimuthal variation, articulation invariants and sensor resolution.

Gabor Wavelet Representation for 3D Object Recognition

A model-based object recognition approach that used a Gabor wavelet representation is presented. The focus was to use magnitude, phase, and frequency measures of the Gabor wavelet representation in an innovative flexible matching approach that provided robust recognition. The Gabor grid, a topology-preserving map, efficiently encoded both signal energy and structural information of an object in a sparse multiresolution representation. The Gabor grid subsampled the Gabor wavelet decomposition of an object model and deformed to allow the indexed object model to match with similar representation obtained using image data. Flexible matching between the model and the image minimized a cost function based on local similarity and geometric distortion of the Gabor grid. Grid erosion and repairing was performed whenever a collapsed grid, due to object occlusion, was detected and the results on infrared imagery are presented, where objects underwent rotation, translation, scale, occlusion, and aspect variations under changing environmental conditions.

Stochastic Models for Recognition of Articulated Objects

A hidden Markov modeling (HMM) based approach for recognition of articulated objects in synthetic aperture radar (SAR) images is presented. We developed multiple models for a given SAR image of an object and integrated these models synergistically using their probabilistic estimates for recognition and estimates of invariance of features as a result of articulation. The models were based on sequentialization of scattering centers extracted from SAR images. Experimental results are presented using 1440 training images and 2520 testing images for 4 classes.

Target indexing in SAR images using scattering centers and the Hausdorff distance

A method is presented with a concern for efficient and accurate indexing for target recognition in SAR images. The solution was a method that efficiently retrieved correct object hypotheses using the major axis of a pattern of scattering centers in SAR images and the Hausdorff distance measure. The features that were used are the locations of scattering centers in SAR returns. Experimental results showed that indexing using major axis efficiently narrows down the number of candidate hypotheses and that the Hausdorff distance measure performed well in picking the correct hypothesis. These properties of the algorithm along with computational efficiency made the method a promising approach to target indexing in SAR images.

Generic Object Recognition using Multiple Representations

Real-world image understanding tasks often involved complex object models which were not adequately represented by a single representational scheme for the various recognition scenarios encountered in practice. Multiple representations, on the other hand, allowed different matching strategies to be applied for the same object, or even for different parts of the same object. A concern with the derivation of hierarchical CAD models having multiple representations - concave/convex edges and straight homogeneous generalized cylinder - and their use for generic object recognition in outdoor visible imagery is presented. It also presents a refocused matching algorithm that used a hierarchically structured model database to facilitate generic object recognition.

Adaptive Object Detection From Multi-Sensor Data

Two general methodologies for developing self-adapting automatic object detection systems to achieve robust performance are introduced. They were based on optimization of parameters of an algorithm and adaptation of the input to an algorithm. Different modified Hebbian learning rules were used to build adaptive feature extractors which transformed the input data into a desired form for a given object detection algorithm. To show its feasibility, input adaptors for object detection were designed and tested using multi-sensor data including SAR, FLIR, and color images.

Automatic Model Construction for Object Recognition Using ISAR Images

A learning-from-examples approach was used to construct recognition models of the objects from their ISAR data. Given a set of ISAR data of an object of interest, structural features were extracted from the images. Statistical analysis and geometrical reasoning were then used to analyze the features to find spatial and statistical invariance so that a structural model of the object suitable for object recognition could be constructed. Results of experiments using the automatically constructed models in object recognition are presented.

Modeling Clutter and Context for Target Detection in Infrared Images

In order to reduce false alarms and to improve the target detection performance of an automatic target detection and recognition system operating in a cluttered environment, it was important to develop the models not only for man-made targets but also of natural background clutters. Because of the high complexity of natural clutters, this clutter model could only be reliably built through learning from real examples. If available, contextual information that characterizes each training example could be used to further improve the learned clutter model. We present such a clutter model aided target detection system. Emphasis was placed on two topics: learning the background clutter model from sensory data through a self-organizing process and reinforcing the learned clutter model using contextual information.

Composite Phase and Phase-Based Gabor Element Aggregation

The phase, obtained by Gabor filtering an image, could be used to aggregate related Gabor elements (simple features identified by peaks in the Gabor magnitude). This phase-based feature grouping simplified the perennial problem of target/background segmentation because we only needed to determine if the aggregate feature was target or background, rather than determining the status of each feature independently. Since the phase from a single quadrature Gabor output could not tolerate large changes in orientation, a new local measure, which was referred to as the composite phase, was developed. It was a combination of the filter responses from multiple orientations which allowed the phase to follow contours with large changes in orientation. A constant composite phase contour was used to connect related Gabor elements that would otherwise appear separated within the magnitude response.

Error Bound for Multi-Stage Synthesis of Narrow Bandwidth Gabor Filters

A study that developed an error bound for narrow bandwidth Gabor filters synthesized using multiple stages is presented. It is shown that the error introduced by approximating narrow bandwidth Gabor kernels by a weighted sum of spatially offset, separable kernels was a function of the frequency offset and the reduction in bandwidth of the desired kernel compared to the basis values, as well as the spatial subsampling rate between filter stages. This error bound was expected to prove useful in the design of a general basis filter set for multi-stage filtering because the maximum frequency offset was largely determined by the spacing of the basis filters.

Gabor Wavelets for 3-D Object Recognition

A model-based object recognition approach that used a hierarchical Gabor wavelet representation is presented. The key idea was to use magnitude, phase and frequency measures of Gabor wavelet representation in an innovative flexible matching approach that was able to provide robust recognition. A Gabor grid, a topology-preserving map, efficiently encoded both signal energy and structural information of an object in a sparse multi-resolution representation and the Gabor grid subsampled the Gabor wavelet decomposition of an object model and was deformed to allow the indexed object model match with the image data. Flexible matching between the model and the image minimized a cost function based on local similarity and geometric distortion of the Gabor grid. Grid erosion and repairing was performed whenever a collapsed grid, due to object occlusion, was detected. The results on infrared imagery are presented, where objects underwent rotation, translation, scale, occlusion and aspect variations under changing environmental conditions.

A System for Aircraft Recognition in Perspective Aerial Images

Recognition of aircraft in complex, perspective aerial imagery had to be accomplished in presence of clutter, occlusion, shadow, and various forms of image degradation. A system for aircraft recognition under real-world conditions that was based on the use of a hierarchical database of object models is presented. This particular approach involved three key processes: (a) The qualitative object recognition process performed model-based symbolic feature extraction and generic object recognition; (b) The refocused matching and evaluation process refined the extracted features for more specific classification with input from (a); and (c) The primitive feature extraction process regulated the extracted features based on their saliency and interacted with (a) and (b). Experimental results showing the qualitative recognition of aircraft in perspective, aerial images are presented.

Background Modeling for Target Detection and Recognition

In order to reduce false alarms and to improve the detection and recognition performance in cluttered environments, it was important to develop not only the models for man-made targets but also the models of natural backgrounds. A learning based approach to construct and to maintain a concise and accurate background model bank by learning from positive and negative examples is presented. Features used to characterize the natural backgrounds included joint space-frequency features based on the Gabor transform, and localized statistics of geometric elements. An open-structure representation was used to manage the background modeling process so that it was easy to include new sensors, new features, and other contextual information.

Generic Object Recognition Using CAD-Based Multiple Representations

Real-world applications of computer vision usually involves a variety of object models making a single model representation somewhat inadequate for object recognition. Multiple representations, on the other hand, allow different matching strategies to be applied for the same object, or even for different parts of the same object. Our concern was the use of CAD-derived hierarchical models having multiple representations - concave/convex edges and straight homogeneous generalized cylinder - for generic object recognition in outdoor visible imagery. It also presents a refocused matching algorithm that used a hierarchically structured model database to facilitate generic object recognition.

Hierarchical Gabor Filters for Object Detection in Infrared Images

A new representation called “Hierarchical Gabor Filters” and associated local measures which were used to detect potential objects of interest in images is presented. The “first stage” of the approach used a wavelet set of wide-bandwidth separable Gabor filters to extract local measures from an image. The “second stage made certain spatial groupings explicit by creating small-bandwidth, non-separable Gabor filters that were tuned to elongated contours or periodic patterns. The non-separable filter responses were obtained from a weighted combination of the separable basis filters, which preserved the computational efficiency of separable filters while providing the distinctiveness required to discriminate objects from clutter.

Image Understanding for Automatic Target Recognition

Automatic Target Recognition (ATR) was an extremely important capability for defense applications. Many aspects of Image Understanding (IU) research were traditionally used to solve ATR problems. ATR applications and problems in developing real-world ATR systems, and the status of technology for these systems are presented. We identified several IU problems that needed to be resolved in order to enhance the effectiveness of ATR-based weapon systems. Technological gains in developing robust ATR systems were shown to lead to significant advances in many other areas of applications of image understanding.

Recognition of Occluded Objects: A Cluster-Structure Algorithm

We applied clustering methods to a new problem domain and presented a new method based on a cluster-structure approach for the recognition of 2-D partially occluded objects. Basically, the technique consisted of three steps: clustering of border segment transformations; finding continuous sequences of segments in appropriately chosen clusters; and clustering of sequence average transformation values. As compared to some of the earlier methods, which identified an object based on only one sequence of matched segments, the newer approach allowed for the identification of all parts of the model which matched in the occluded scene. We also discuss the application of the clustering techniques to 3D scene analysis. In both cases, the cluster-structure algorithm entailed the application of clustering concepts in a hierarchical manner, resulting in a decrease in the computational effort as the recognition algorithm progressed. The implementation of the techniques discussed for the 2-D case was completed and the algorithm was evaluated with respect to a large number of examples where several objects partially occluded one another. The method was able to tolerate a moderate change in scale and a significant amount of shape distortion arising as a result of segmentation and/or the polygonal approximation of the boundary of the object.

Knowledge-Based Robust Target Recognition & Tracking

In the Honeywell Strategic Computing Computer Vision Program, we worked on demonstrating knowledge-based robust target recognition and tracking technology.The focus of our work was to use artificial intelligence techniques in computer vision, spatial-reasoning, temporal reasoning, incorporation of a priori, and contextual and multisensory information for dynamic scene understanding. The topics under investigation were: landmark and target recognition using multi-source a priori information, robust target motion detection and tracking using qualitative reasoning, and interpretation of terrain using symbolic grouping. An integrated system concept for these topics is presented, along with results on real imagery. Practical applications of work involve vision controlled navigation/guidance of the autonomous land vehicle, reconnaissance, surveillance, photo-interpretation, and other military applications such as search and rescue and targeting missions.

Automatic Target Recognition: State of the Art Survey

A review of the techniques used to solve the automatic target recognition (ATR) problem is given. Emphasis is placed on algorithmic and implementation approaches. ATR algorithms such as target detection, segmentation, feature computation, classification, etc. are evaluated and several new quantitative criteria are presented. Evaluation approaches are discussed and various problems encountered in the evaluation of algorithms are addressed. Techniques such as the use of contextual cues, semantic and structural information, hierarchical reasoning in the classification and incorporation of multi-sensors in ATR systems are also presented.

Clustering Based Recognition of Occluded Objects

Clustering techniques have been used to perform image segmentation, to detect lines and curves in the images and to solve several other problems in pattern recognition and image analysis. We applied clustering methods to a problem domain and present a method based on a cluster-structure paradigm for the recognition of 2D partially occluded objects and also discuss the application of the clustering techniques to 3D object recognition. In both cases, the cluster-structure paradigm entails the application of clustering concepts in a hierarchical manner. The amount of computational effort decreased as the recognition algorithm progressed. The implementation of the technique discussed for the 2D case was completed and evaluated with respect to a large number of examples where several objects partially occluded one another. The method was able to tolerate a moderate change in scale and a significant amount of shape distortion arising as a result of segmentation and/or the polygonal approximation of the boundary of the object.

Recognition of Occluded Objects: A Cluster Structure Paradigm

Clustering techniques have been used to perform image segmentation, to detect lines and curves in the images and to solve several other problems in pattern recognition and image analysis. Here we applied clustering methods to a problem domain and present a new method based on a cluster-structure paradigm for the recognition of 2-D partially occluded objects. The cluster structure paradigm entailed the application of clustering concepts in a hierarchical manner. The amount of computational effort decreased as the recognition algorithm progresses. As compared to some of the earlier methods, which identify an object based on only one sequence of matched segments, this technique allows for the identification of all parts of the model which match with the apparent object. Also the method was able to tolerate a moderate change in scale and a significant amount of shape distortion arising as a result of segmentation and/or the polygonal approximation of the boundary of the object.

Shape Matching of Two-Dimensional Objects

Results in the areas of shape matching of non-occluded and occluded two-dimensional objects are presented. This technique was based on a stochastic labeling procedure which explicitly maximized a criterion function based on the ambiguity and inconsistency of classification. To reduce the computation time, the technique was hierarchical and used results obtained at low levels to speed up and improve the accuracy of results at higher levels. This basic technique was extended to the situation where various objects partially occluded each other to form an apparent object and our interest was to find all the objects participating in the occlusion. In such a case several hierarchical processes were executed in parallel for every object participating in the occlusion and were coordinated in such a way that the same segment of the apparent object was not matched to the segments of different actual objects. These techniques were applied to two-dimensional simple closed curves represented by polygons and the power of the techniques was demonstrated by the examples taken from synthetic, aerial, industrial, and biological images where the matching was done after using the actual segmentation methods.

Evaluation of Automatic Target Recognition Algorithms

We briefly review the techniques used to solve the automatic target recognition (ATR) problem. Emphasis is placed on the algorithmic and implementation approaches. The evaluation of ATR algorithms such as target detection, segmentation, feature evaluation, and classification are discussed in detail and several quantitative criteria are suggested. The evaluation approach is discussed and various problems encountered in the evaluation of algorithms are addressed. Strategies used in the database design are outlined. Techniques such as the use of semantic and structural information, hierarchical reasoning in the classification, and incorporation of multi-sensor in the ATR systems are also presented.

Intelligent Auto-Cueing of Tactical Targets in FLIR Images

Algorithms used to automatically detect segment and classify tactical targets in FLIR (Forward Looking InfraRed) images are presented. The results are shown on a FLIR database consisting of 480, 512x512, 8 bit air-to-ground images.

Recognition of Occluded Objects

Matching of occluded objects was one of the prime capabilities of any computer vision system. A hierarchical stochastic labeling technique that did shape matching of 2D occluded objects is presented. The technique explicitly maximized a criterion function based on the ambiguity and inconsistency of classification. The 2D shapes were represented by their polygonal approximation. For each of the objects that participated in the occlusion, there was a hierarchical process. These processes were executed in parallel and were coordinated in such a way that the same segment of the apparent object, formed as a result of occlusion of two or more actual objects, was not matched to the segments of different actual objects.

Shape Matching of 2D Objects Using a Hierarchical Stochastic Labeling Technique

A stochastic labeling technique to do shape matching of non-occluded and occluded 2D objects is presented. The technique explicitly maximized a criterion function based on the ambiguity and inconsistency of classification. The technique was hierarchical and used results obtained at low levels to speed up and improve the accuracy of results at higher levels. This basic technique had been extended to the situation when various objects partially occlude. In such a case several hierarchical processes were executed in the occlusion and were coordinated in such a way that the same segment of the apparent object was not matched to the segments of different actual objects.

Shape Matching of Two-Dimensional Occluded Objects

A hierarchical stochastic labeling technique to do shape matching of 2D occluded objects is presented. The technique explicitly maximized a criterion function based on the ambiguity and inconsistency of classification and the hierarchical nature of the algorithm reduced the computation time and used results obtained at low levels to speed up and improve accuracy of results at higher levels. The 2D shapes were represented by their polygonal approximation. For each of the objects participating in the occlusion, there was a hierarchical process. These processes were executed in parallel and were coordinated in such a way that the same segment of the apparent object, formed as a result of occlusion of two or more actual objects, was not matched to the segments of different actual objects. This problem was solved by combining the gradient projection method and penalty function approach. Objects participating in the occlusion may move, rotate, undergo significant changes in shape and their scale may also change.

Recognition of Occluded Two Dimensional Objects

The problem of recognizing occluded or partially occluded objects has become more and more important in applications such as biomedical image analysis, industrial inspection, and robotics. We proposed a hierarchical stochastic labeling technique to identify parts of two dimensional shapes represented by their polygonal approximations.

Reconnaissance de Formes Planes par une Methode Hierarchique d’Etiquetage Probabiliste (French)

Nous montrons comment le problème de la Reconnaissance de l’occurence d’une forme plane à l’interieur d’une autre forme indépendemment d’un facteur d’échelle et de rotation peut être resolu par une méthode hiérarchique d’étiquetage probabiliste. Des examples d’application à des silhouettes de pièces industrielles sont présentés.