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This paper presents a novel feature grouping based framework for building facade recognition from aerial images. A combination of Maximally Stable Extremal Regions (MSERs and steered Determinant-of-Hessian (steered-DoH) are proposed to detect different shapes of blobs from images. Then we employ local parallelogram grouped by these repetitive and evenly distributed blobs to form an point-based regularity...
The growing interest for mobile biometrics stems from the increasing need to secure personal data and services, which are often stored or accessed from there. Modern user mobile devices, with acquisition and computation resources to support related operations, are nowadays widely available. This makes this research topic very attracting and promising. Iris recognition plays a major role in this scenario...
In this work, two enhancement methods are proposed to speed up junction detection performed by the JUDOCA detector. The first enhancement method minimizes the number of junction candidates on which the circular kernel is applied. This is achieved by introducing a suppression technique that takes both the thin and thick edge images into consideration. The second method works on relaxing the step of...
Understanding where people attention focuses is a challenging and extremely valuable task that can be solved using computer vision technologies. In this paper we address this problem on surveillance-like scenarios, where head and body imagery are usually low resolution. We propose a method to profile the attention of people moving in a known space. We exploit coarse gaze estimation and a novel model...
In this paper, a method for unknown object tracking in output images from 360-degree cameras called Modified Training-Learning-Detection (MTLD) is presented. The proposed method is based on the recently introduced Training-Learning-Detection (TLD) scheme in the literature. The flaws of the TLD approach have been detected and significant modifications are proposed to enhance and to elaborate the scheme...
In this paper we focus on speckle noise removal. Previously, variational models have been proposed to remove the multiplicative speckle noise. In general, the variational models require a significant amount of run time to converge, and need to set the proper tuning parameter values to achieve optimal noise reduction results. In this paper, we present a local polynomial regression model for speckle...
A+ aka Adjusted Anchored Neighborhood Regression - is a state-of-the-art method for exemplar-based single image super-resolution with low time complexity at both train and test time. By robustly training a clustered regression model over a low-resolution dictionary, its performance keeps improving with the dictionary size - even when using tens of thousands of regressors. However, this can pose a...
We present an approach for the detection of buildings in multispectral satellite images. Unlike 3-channel RGB images, satellite imagery contains additional channels corresponding to different wavelengths. Approaches that do not use all channels are unable to fully exploit these images for optimal performance. Furthermore, care must be taken due to the large bias in classes, e.g., most of the Earth...
The simple yet subtle structures of faces make it difficult to capture the fine differences between different facial regions in the depth map, especially for consumer devices like Kinect. To address this issue, we present a novel method to super-solve and recover the facial depth map nicely. The key idea of our approach is to exploit the learning-based method to obtain the reliable face priors from...
In this article a new strategy for single-image super-resolution is proposed. A selective sparse coding strategy based on patch sharpness is assumed to be invariant for patch resolution. This sharpness criterion is used at training stage to classify image patches into different clusters. It is suggested that the use of coupled dictionary learning, with a mapping function can improve the representation...
Recently, a series of advances were made for image restoration tasks such as image denoising and single image super-resolution. It is particularly remarkable that methods employing different formulations and assumptions achieve comparable top performances. Moreover, the top methods operate at their best on some particular image contents and poorer on other. No method is the best on all the image contents...
Object detection and localization in images involve a multi-scale reasoning process. First, responses of object detectors are known to vary with image scale. Second, contextual relationships on a part-level, object-level, and scene-level appear at different scales of the image. This paper studies efficient modeling of these two components by training multi-scale template models. The input to the proposed...
In this paper, we propose a simple and effective depth upsampling technique using self-guided residual interpolation. The original residual interpolation requires guidance information such as high-resolution RGB color image. However, self-guided residual interpolation requires only a single depth map. In the proposed algorithm, a tentative estimation of a high-resolution depth map is first generated...
Various soft biometric traits have been used as hints in forensic investigation. Tattoo, as one of those soft biometric traits, has been used extensively because it is easy to be remembered and described by witnesses and appears very often among criminals and victims. Most of the tattoo retrieval systems currently used in police departments are still text-based systems. They depend on labels tagged...
Most Wide Area Motion Imagery (WAMI) based trackers use motion based cueing for detecting and tracking moving objects. The results are very high false alarm rates in urban environments with tall structures due to parallax effects. This paper proposes an accurate moving object detection method using a precise orthorectification approach for ground stabilization combined with accurate multiview depth...
Recently sparse representation has gained great success in face image super-resolution. The conventional sparsity-based methods enforce sparse coding on face image patches and the representation fidelity is measured by ℓ2-norm. Such a sparse coding model regularizes all facial patches equally, which however ignores the natures of facial patches, where the facial patches in the different regions (patch...
The problem of labeling the connected components (CCL) of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists and should be mandatory provided as output, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with...
The authors have conducted studies on recognizing Arabic news captions to develop a system for video retrieval to index and edit Arabic broadcast programs daily received and stored in big database. This paper describes a dedicated OCR for recognizing low resolution news captions in video images. News caption recognition system consisting of text line extraction, word segmentation and segmentation-recognition...
Pedestrian detection from in-vehicle camera images for the purpose of advanced driver assistance systems is of particular importance in cases of low-resolution pedestrians, because it is desirable to detect the pedestrian as far from the vehicle as possible to effectively provide safe driving support for the driver. Most previous studies on pedestrian detection, however, have focused on pedestrians...
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