Alptekin Temizel

Image Resolution Enhancement using Wavelet Domain Hidden Markov Tree and Coefficient Sign Estimation

Image resolution enhancement using wavelets is a relatively new subject and many new algorithms have been proposed recently. These algorithms assume that the low resolution image is the approximation subband of a higher resolution image and attempts to estimate the unknown detail coefficients to reconstruct a high resolution image. A subset of these recent approaches utilized probabilistic models to estimate these unknown coefficients. Particularly, hidden Markov tree (HMT) based methods using Gaussian mixture models have been shown to produce promising results. However, one drawback of these methods is that, as the Gaussian is symmetrical around zero, signs of the coefficients generated using this distribution function are inherently random, adversely affecting the resulting image quality. In this paper, we demonstrate that, sign information is an important element affecting the results and propose a method to estimate signs of these coefficients more accurately.

Image resolution upscaling in the wavelet domain using directional cycle spinning

Cycle spinning, a technique mainly used for wavelet de- noising, has also been shown to be successful toward image reso- lution upscaling in the wavelet domain. We propose a directional variant of the cycle spinning methodology. We obtain estimates of local edge orientation from a wavelet decomposition of the available low-resolution image and use this information to influence the choice of cycle spinning parameters that are employed for resolution upscaling. Our experimental results show that the proposed method outperforms competing methods for a wide range of images offering modest but consistent improvements both in objective as well as subjective terms. Lower computational complexity compared to the conventional cycle spinning is also demonstrated.

Real-Time Adaptive Camera Tamper Detection for Video Surveillance

Criminals often resort to camera tampering to prevent capture of their actions. Real-time automated detection of video camera tampering cases is important for timely warning of the operators. Tampering is generally done by obstructing the camera view by a foreign object, displacing the camera and changing the focus of the camera lens. In automated camera tamper detection systems, low false alarm rates are important as reliability of these systems is compromised by unnecessary alarms and consequently the operators start ignoring the warnings. We propose adaptive algorithms to detect and identify such cases with low false alarms rates in typical surveillance scenarios where there is significant activity in the scene.

Camera tamper detection using wavelet analysis for video surveillance

It is generally accepted that video surveillance system operators lose their concentration after a short period of time and may miss important events taking place. In addition, many surveillance systems are frequently left unattended. Because of these reasons, automated analysis of the live video feed and automatic detection of suspicious activity have recently gained importance. To prevent capture of their images, criminals resort to several techniques such as deliberately obscuring the camera view, covering the lens with a foreign object, spraying or de-focusing the camera lens. In this paper, we propose some computationally efficient wavelet domain methods for rapid camera tamper detection and identify some real-life problems and propose solutions to these.

Multi-spectral Satellite Image Registration Using Scale-Restricted SURF

Satellites generally have arrays of sensors having different resolution and wavelength parameters. For some applications, images acquired from different viewpoints and positions are required to be aligned. This alignment process could be achieved by matching the image features followed by image registration. In this paper registration of multispectral satellite images using Speeded Up Robust Features (SURF) method is examined. The performance of SURF for registration of high resolution satellite images captured at different bands is evaluated. Scale restriction (SR) method, which has recently been proposed for SIFT, is adapted to SURF to improve multispectral image registration performance. Matching performance between different bands using SURF, U-SURF, SURF with SR and U-SURF with SR is tested and robustness of these with respect to orientation and scale is evaluated.

Real-time multi-camera video analytics system on GPU

In this article, parallel implementation of a real-time intelligent video surveillance system on Graphics Processing Unit (GPU) is described. The system is based on background subtraction and composed of motion detection, camera sabotage detection (moved camera, out-of-focus camera and covered camera detection), abandoned object detection, and object-tracking algorithms. As the algorithms have different characteristics, their GPU implementations have different speed-up rates. Test results show that when all the algorithms run concurrently, parallelization in GPU makes the system up to 21.88 times faster than the central processing unit counterpart, enabling real-time analysis of higher number of cameras.

Multi-view structure-from-motion for hybrid camera scenarios

We describe a pipeline for structure-from-motion (SfM) with mixed camera types, namely omnidirectional and perspective cameras. For the steps of this pipeline, we propose new approaches or adapt the existing perspective camera methods to make the pipeline effective and automatic. We model our cameras of different types with the sphere camera model. To match feature points, we describe a preprocessing algorithm which significantly increases scale invariant feature transform (SIFT) matching performance for hybrid image pairs. With this approach, automatic point matching between omnidirectional and perspective images is achieved. We robustly estimate the hybrid fundamental matrix with the obtained point correspondences. We introduce the normalization matrices for lifted coordinates so that normalization and denormalization can be performed linearly for omnidirectional images. We evaluate the alternatives of estimating camera poses in hybrid pairs. A weighting strategy is proposed for iterative linear triangulation which improves the structure estimation accuracy. Following the addition of multiple perspective and omnidirectional images to the structure, we perform sparse bundle adjustment on the estimated structure by adapting it to use the sphere camera model. Demonstrations of the end-to-end multi-view SfM pipeline with the real images of mixed camera types are presented. Display Omitted Highlights? We describe a pipeline to perform structure-from-motion with mixed camera types. ? Sphere camera model is used throughout the pipeline for different camera types. ? Demonstrations of the proposed approach in real world scenarios are presented.

Fusion of thermal- and visible-band video for abandoned object detection

Timely detection of packages that are left unattended in public spaces is a security concern, and rapid detection is important for prevention of potential threats. Because constant surveillance of such places is challenging and labor intensive, automated abandoned-object-detection systems aiding operators have started to be widely used. In many studies, stationary objects, such as people sitting on a bench, are also detected as suspicious objects due to abandoned items being defined as items newly added to the scene and remained stationary for a predefined time. Therefore, any stationary object results in an alarm causing a high number of false alarms. These false alarms could be prevented by classifying suspicious items as living and nonliving objects. In this study, a system for abandoned object detection that aids operators surveilling indoor environments such as airports, railway or metro stations, is proposed. By analysis of information from a thermal- and visible-band camera, people and the objects left behind can be detected and discriminated as living and nonliving, reducing the false-alarm rate. Experiments demonstrate that using data obtained from a thermal camera in addition to a visible-band camera also increases the true detection rate of abandoned objects.

Effective Structure-from-Motion for Hybrid Camera Systems

We describe a pipeline for structure-from-motion with mixed camera types, namely omni directional and perspective cameras. The steps of the pipeline can be summarized as calibration, point matching, pose estimation, triangulation and bundle adjustment. For these steps, we either propose improved methods or modify existing perspective camera methods to make the pipeline more effective and automatic when employed for hybrid camera systems.

High-resolution multispectral satellite image matching using scale invariant feature transform and speeded up robust features

Satellite images captured in different spectral bands might exhibit nonlinear intensity changes at the corresponding spatial locations due to the different reflectance responses for these bands. This affects the image registration performance negatively as the corresponding features might have different properties in different bands. We propose a modification to the widely used scale invariant feature transform (SIFT) method to increase the correct feature matching ratio and to decrease the computation time of this algorithm for the multispectral satellite images. We also apply scale restriction to SIFT and speeded up robust features (SURF) algorithms to increase the correct match ratio. We present test results for variations of SIFT and SURF algorithms. The results show the effectiveness of the proposed improvements compared to the other SIFT- and SURF-based methods.