Tayfun Aytaç

Simultaneous extraction of geometry and surface properties of targets using simple infrared sensors

We investigate the use of low-cost infrared (IR) sensors for the simultaneous extraction of geometry and surface properties of com- monly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and sur- face properties of the reflecting target in a way that cannot be repre- sented by a simple analytical relationship, therefore complicating the lo- calization and recognition process. We propose the use of angular intensity scans and present an algorithm to process them to determine the geometry and the surface type of the target and estimate its position. The method is verified experimentally with planes, 90-deg corners, and 90-deg edges covered with aluminum, white cloth, and Styrofoam pack- aging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1 deg, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99 and 81%, respectively, which shows that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to extract sub- stantially more information than that for which such devices are com- monly employed.

Differentiation and localization of targets using infrared sensors

This study investigates the use of low-cost infrared emitters and detectors in the differentiation and localization of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings obtained with such systems are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6°, respectively. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than that which they are commonly employed for.

Position-invariant surface recognition and localization using infrared sensors

Low-cost infrared emitters and detectors are used for the recognition of surfaces with different properties in a location-invariant manner. The intensity readings obtained with such devices are highly dependent on the location and properties of the surface in a way that cannot be represented in a simple manner, complicating the recognition and localization process. We propose the use of angular intensity scans and present an algorithm to process them. This approach can distinguish different surfaces independently of their positions. Once the surface is identified, its position can also be estimated. The method is verified experimentally with the surfaces aluminum, white painted wall, brown kraft paper, and polystyrene foam packaging material. A correct differentiation rate of 87% is achieved, and the surfaces are localized within absolute range and azimuth errors of 1.2 cm and 1.0 deg, respectively. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than they are commonly employed for.

Rule-based target differentiation and position estimation based on infrared intensity measurements

This study investigates the use of low-cost infrared sensors in the differentiation and localization of target primitives commonly encoun- tered in indoor environments, such as planes, corners, edges, and cyl- inders. The intensity readings from such sensors are highly dependent on target location and properties in a way that cannot be represented in a simple manner, making the differentiation and localization difficult. We propose the use of angular intensity scans from two infrared sensors and present a rule-based algorithm to process them. The method can achieve position-invariant target differentiation without relying on the ab- solute return signal intensities of the infrared sensors. The method is verified experimentally. Planes, 90-deg corners, 90-deg edges, and cyl- inders are differentiated with correct rates of 90%, 100%, 82.5%, and 92.5%, respectively. Targets are localized with average absolute range and azimuth errors of 0.55 cm and 1.03 deg. The demonstration shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than they are commonly employed for.

Detection and tracking of sea-surface targets in infrared and visual band videos using the bag-of-features technique with scale-invariant feature transform

Sea-surface targets are automatically detected and tracked using the bag-of-features (BOF) technique with the scale-invariant feature transform (SIFT) in infrared (IR) and visual (VIS) band videos. Features corresponding to the sea-surface targets and background are first clustered using a training set offline, and these features are then used for online target detection using the BOF technique. The features corresponding to the targets are matched to those in the subsequent frame for target tracking purposes with a set of heuristic rules. Tracking performance is compared with an optical-flow-based method with respect to the ground truth target positions for different real IR and VIS band videos and synthetic IR videos. Scenarios are composed of videos recorded/generated at different times of day, containing single and multiple targets located at different ranges and orientations. The experimental results show that sea-surface targets can be detected and tracked with plausible accuracies by using the BOF technique with the SIFT in both IR and VIS band videos.

Adaptive enhancement of sea-surface targets in infrared images based on local frequency cues

Image enhancement is an important preprocessing step of infrared (IR) based target recognition and surveillance systems. For a better visualization of targets, it is vital to develop image enhancement techniques that increase the contrast between the target and background and emphasize the regions in the target while suppressing noises and background clutter. This study proposes what we believe to be a novel IR image enhancement method for sea-surface targets based on local frequency cues. The image is transformed blockwise into the Fourier domain, and clustering is done according to the number of expected regions to be enhanced in the scene. Based on the variations in the elements in any cluster and the differences between the cluster centers in the frequency domain, two gain matrices are computed for midfrequency and high frequency images by which the image is enhanced accordingly. We provide results for real data and compare the performance of the proposed algorithm through subjective and quantitative tests with four different enhancement methods. The algorithm shows a better performance in the detail visibility of the target.

Surface differentiation by parametric modeling of infrared intensity scans

We differentiate surfaces with different properties with simple low-cost IR emitters and detectors in a location-invariant manner. The intensity readings obtained with such sensors are highly dependent on the location and properties of the surface, which complicates the differentiation and localization process. Our approach, which models IR intensity scans parametrically, can distinguish different surfaces independent of their positions. Once the surface type is identified, its position (r,) can also be estimated. The method is verified experimentally with wood; Styrofoam packaging material; white painted matte wall; white and black cloth; and white, brown, and violet paper. A correct differentiation rate of 100% is achieved for six surfaces, and the surfaces are localized within absolute range and azimuth errors of 0.2 cm and 1.1 deg, respectively. The differentiation rate decreases to 86% for seven surfaces and to 73% for eight surfaces. The method demonstrated shows that simple IR sensors, when coupled with appropriate signal processing, can be used to recognize different types of surfaces in a location-invariant manner.

Differentiation and localization of target primitives using infrared sensors

This study investigates the use of low-cost infrared sensors in the differentiation and localization of commonly encountered target primitives in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings from such sensors are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6/spl deg/, respectively. The proposed method should facilitate the use of infrared sensors in mobile robot applications for differentiation and localization beyond their common usage as simple proximity sensors for object detection and collision avoidance.

Target differentiation with simple infrared sensors using statistical pattern recognition techniques

This study compares the performances of various statistical pattern recognition techniques for the differentiation of commonly encountered features in indoor environments, possibly with different surface properties, using simple infrared (IR) sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the differentiation process. We construct feature vectors based on the parameters of angular IR intensity scans from different targets to determine their geometry and/or surface type. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. Parametric differentiation correctly identifies six different surface types of the same planar geometry, resulting in the best surface differentiation rate (100%). However, this rate is not maintained with the inclusion of more surfaces. The results indicate that the geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. The results demonstrate that simple IR sensors, when coupled with appropriate processing and recognition techniques, can be used to extract substantially more information than such devices are commonly employed for.

Multiscale contrast direction adaptive image fusion technique for MWIR-LWIR image pairs and LWIR multifocus infrared images

Infrared (IR) cameras are widely used in the latest surveillance systems because spectral characteristics of objects provide valuable information for object detection and identification. To assist the surveillance system operator and automatic image processing tasks, fusing images in the IR band was performed as a solution to increase situational awareness and different fusion techniques were developed for this purpose. Proposed techniques are generally developed for specific scenarios because image content may vary dramatically depending on the spectral range, the optical properties of the cameras, the spectral characteristics of the scene, and the spatial resolution of the interested targets in the scene. In this study, a general purpose IR image fusion technique that is suitable for real-time applications is proposed. The proposed technique can support different scenarios by applying a multiscale detail detection and can be applied to images captured from different spectral regions of the spectrum by adaptively adjusting the contrast direction through cross-checking between the source images. The feasibility of the proposed algorithm is demonstrated on registered multispectral [mid-wave IR (MWIR), long-wave IR (LWIR)] and LWIR multifocus images. Fusion results are presented and the performance of the proposed technique is compared with the baseline fusion methods through objective and subjective tests. The technique outperforms baseline methods in the subjective tests and provide promising results in objective quality metrics with an acceptable computational load. In addition, the proposed technique preserves object details and prevents undesired artifacts better than the baseline techniques in the image fusion scenario that contains four source images.