Subhodev Das

Adaptive image segmentation using genetic and hybrid search methods

This paper describes an adaptive approach for the important image processing problem of image segmentation that relies on learning from experience to adapt and improve the segmentation performance. The adaptive image segmentation system incorporates a feedback loop consisting of a machine learning subsystem, an image segmentation algorithm, and an evaluation component which determines segmentation quality. The machine learning component is based on genetic adaptation and uses (separately) a pure genetic algorithm (GA) and a hybrid of GA and hill climbing (HC). When the learning subsystem is based on pure genetics, the corresponding evaluation component is based on a vector of evaluation criteria. For the hybrid case, the system employs a scalar evaluation measure which is a weighted combination of the different criteria. Experimental results for pure genetic and hybrid search methods are presented using a representative database of outdoor TV imagery. The multiobjective optimization demonstrates the ability of the adaptive image segmentation system to provide high quality segmentation results in a minimal number of generations.<<ETX>>

A system for model-based object recognition in perspective aerial images

Abstract Recognition of objects in complex, perspective aerial imagery is difficult because of occlusion, shadow, clutter and various forms of image degradation. This paper presents 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 performs heterogeneous model-based symbolic feature extraction and generic object recognition; (b) The refocused matching and evaluation process refines the extracted features for more specific classification with input from (a); and (c) The primitive feature extraction process regulates the extracted features based on their saliency and interacts with (a) and (b). Experimental results showing the qualitative recognition of aircraft in perspective, aerial images are presented.

A system for obstacle detection during rotorcraft low altitude flight

An airborne vehicle such as a rotorcraft must avoid obstacles like antennas, towers, poles, fences, tree branches, and wires strung across the flight path. Automatic detection of the obstacles and generation of appropriate guidance and control actions for the vehicle to avoid these obstacles would facilitate autonomous navigation. The requirements of an obstacle detection system for rotorcraft in low-altitude Nap-of-the-Earth (NOE) flight based on various rotorcraft motion constraints is analyzed here in detail. It is argued that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors to be effective. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo. The optical flow-based motion analysis that is combined with on-board inertial navigation system (INS) to compute ranges to visible scene points is described. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis.

A comparative study of stereo, vergence, and focus as depth cues for active vision

The performances of the binocular cues of stereo, vergence, and the monocular cue of focus for range estimation using an active vision system are compared. The performance of each cue is characterized by its sensitivity to errors in the imaging parameters. The effect of random quantization errors is expressed in terms of the standard derivation of the resulting depth error. The effect of systematic calibration errors on estimation using each cue is studied. Performance characterization of each cue is shown to be useful for active control of the imaging parameters to improve the accuracy of the estimated range. Methods to integrate the use of the cues in order to overcome their individual limitations are discussed.<<ETX>>

Analysis of terrain using multispectral images

Automated terrain analysis is required for many practical applications, such as outdoor navigation, image exploitation, remote sensing, reconnaissance and surveillance. In this paper, we present a hierarchical approach to analyze multispectral (MS) imagery for autonomous land vehicle navigation. The approach integrates several strategies to label various terrain classes in these images acquired using twelve spectral bands in the visible and near-infrared spectrum. At the low (pixel) level, it combines texture gradient results from specifically selected channels by varying the size of gradient operators and performing multithresholding and relaxation-based edge linking operations to obtain robust closed region boundaries. At the high (symbolic) level, it makes use of the spectral, locational, and relational constraints among regions to achieve accurate terrain image interpretation. Details of the technique with examples from real imagery collected by an autonomous land vehicle (ALV) are presented.

Generic object recognition using multiple representations

Real-world image understanding tasks often involve complex object models which are not adequately represented by a single representational scheme for the various recognition scenarios encountered in practice. 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. This paper is concerned 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. It also presents a refocused matching algorithm that uses a hierarchically structured model database to facilitate generic object recognition. Experimental results demonstrating generic recognition of objects in perspective, aerial images are presented.

Synergism of binocular and motion stereo for passive ranging

Range measurements to objects in the world relative to mobile platforms such as ground or air vehicles are critical for visually aided navigation and obstacle detection/avoidance. An approach is presented that consists of a synergistic combination of two types of passive ranging method: binocular stereo and motion stereo. We show a new way to model the errors in binocular and motion stereo in conjunction with an inertial navigation system (INS) and derive the appropriate Kalman filter to refine the estimates from these two stereo ranging techniques. We present results using laboratory images that show that refined estimates can be optimally combined to give range values which are more accurate than any one of the individual estimates from binocular and motion stereo. By incorporating a blending filter, the approach has the potential of providing accurate, dense range measurements for all the pixels in the field of view (FOV). >

Integrated binocular and motion stereo in a inertial navigation sensor-based mobile vehicle

Range measurements to real-world objects from mobile platforms such as ground or air vehicles are critical for visually aided navigation and obstacle detection/avoidance. The authors present an approach that consists of a synergistic combination of two types of passive ranging methods: binocular stereo and motion stereo. They describe a new way to model the errors in binocular and motion stereo in conjunction with an inertial navigation system and derive the appropriate Kalman filter for refining the estimates from these two stereo ranging techniques. Results for laboratory images are presented, showing that refined estimates can be optimally combined to give range values which are more accurate than any one of the individual estimates from binocular or motion stereo. By incorporating a blending filter, the approach has the potential of providing accurate, dense range measurements for all the pixels in the field of view.<<ETX>>

A system for obstacle detection during rotorcraft low-altitude flight

Airborne vehicles such as rotorcraft must avoid obstacles such as antennas, towers, poles, fences, tree branches, and wires strung across the flight path. The paper analyzes the requirements of an obstacle detection system for rotorcrafts in low-altitude Nap-of-the-Earth flight based on various rotorcraft motion constraints. It argues that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo in conjunction with inertial navigation system information. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis.<<ETX>>An airborne vehicle such as a rotorcraft must avoid obstacles like antennas, towers, poles, fences, tree branches, and wires strung across the flight path. Automatic detection of the obstacles and generation of appropriate guidance and control actions for the vehicle to avoid these obstacles would facilitate autonomous navigation. The requirements of an obstacle detection system for rotorcraft in low-altitude Nap-of-the-Earth (NOE) flight based on various rotorcraft motion constraints is analyzed here in detail. It is argued that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors to be effective. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo. The optical flow-based motion analysis that is combined with on-board inertial navigation system (INS) to compute ranges to visible scene points is described. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis.

A system for aircraft recognition in perspective aerial images

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