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Based on minimum reconstruction error criterion and the intrinsic sparse property of natural data, sparse representation (SR) has shown promising performance on various image recognition tasks. However, in the field of person re-identification (re-id), the state-of-the-art is still dominated by other methods such as metric learning or CNN. It is because samples in one view may not be representative...
Time-of-flight (TOF) positron emission tomography (PET) has the potential to yield images with improved quality by comparison to conventional PET. Numerous iterative algorithms have been investigated for image reconstruction in TOF-PET. In this work, we investigate optimization based image reconstruction from list-mode TOF-PET data collected with a digital PET system under clinical evaluation. Specifically,...
Due to the explosive growth of visual data and the raised urgent needs for more efficient nearest neighbor search methods, hashing methods have been widely studied in recent years. However, parameter optimization of the hash function in most available approaches is tightly coupled with the form of the function itself, which makes the optimization difficult and consequently affects the similarity preserving...
We investigate a new method for the acquisition of bandlimited functions, which we call mobile sampling. A field is sampled by a mobile sensor that moves along a continuous path. In this context, it is possible to increase the spatial sampling rate along the sensor's path with marginal additional cost. Hence we assume that the sensors acquire the field values on its path at an arbitrarily high resolution...
Automatic discovery of topical objects from a set of image collections provides more strong cognitive capability of robot to understand the unstructured environment. In this paper, we propose a novel framework based on dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness...
This paper presents a renewed image annotation baseline method under the nearest neighbor tag transfer framework. Two key problems are considered in this paper: (1) which images are determined as the neighbors; (2) how their keywords are transferred. Firstly, a soft neighbor selection scheme is designed by image embedding technique, with which we can provide more power to the crucial neighbors in...
The fusion of images captured from multi-modality sensors has been studied for many years. It is aiming at combining multiple sources together to maximize the meaningful information and reduce the redundancy. Meanwhile, sparse representation of images has been attracting more and more attentions. It has been effectively utilized on image reconstruction, image de-noising, super-resolution and others...
This paper presents a novel approach to simultaneously compute the motion segmentation and the 3D reconstruction of a set of 2D points extracted from an image sequence. Starting from an initial segmentation, our method proposes an iterative procedure that corrects the misclassified points while reconstructing the 3D scene, which is composed of objects that move independently. This optimization procedure...
We present an adaptive acquisition protocol design technique with the goal of improving local contrast-to-noise ratios (CNR) for flexible SPECT and PET scanners in which the data acquisition parameters at each view can be modulated. Such flexible scanners include rotating SPECT scanners, dual head coincidence imagers, SPECT scanners with variable collimators and those where collimators move in relation...
GPU hardware architectures have evolved into a suitable platform for the hardware acceleration of complex computing tasks. Stereo vision is one such task where acceleration is desirable for robotic and automotive systems. Much research was invested in developing stereo vision algorithms with increased quality, but real-time implementations are still lacking. In this work we focus on creating a real-time...
The reconstruction of complete vascular trees from medical images has many important applications. Although vessel detection has been extensively investigated, little work has been done on how connect the results to reconstruct the full trees. In this paper, we propose a novel theoretical framework for automatic vessel connection, where the automation is achieved by leveraging constraints from the...
In this work we focus on creating a real-time dense stereo reconstruction system with accurate sub-pixel estimation. We selected the Semi-Global Matching method as the basis of our system due to its high quality and possible real-time implementations. In our solution we use the Census transform as the matching metric because our results show that it can reduce the matching errors for traffic images...
In this paper, we propose a novel algorithm for computing an atlas from a collection of images. In the literature, atlases have almost always been computed as some types of means such as the straightforward Euclidean means or the more general Karcher means on Riemannian manifolds. In the context of images, the paper's main contribution is a geometric framework for computing image atlases through a...
The framework of rate-distortion optimization (RDO) has been widely adopted for video coding to achieve a good trade-off between bit-rate and distortion. However, objective distortion metrics such as mean square error traditionally used in this framework are poorly correlated with perceptual video quality. To address this issue, we incorporate the structural similarity index as a quality metric into...
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