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This paper presents a new method for the reconstruction of images from samples located at non-integer mesh positions. This is a common scenario for many image processing applications such as multi-image super-resolution, frame-rate up-conversion, or virtual view synthesis in multi-camera systems. The proposed method consists of an iterative procedure that employs adaptive denoising in order to reduce...
Pedestrian detection, as an important task in video surveillance and forensics applications, has been widely studied. However, its performance is unsatisfactory especially in the low resolution conditions. In realistic scenarios, the size of pedestrians in the images is often small, and detection can be challenging. To solve this problem, this paper proposes a novel resolution-score discriminative...
In this paper, we propose a deep CNN to tackle the image restoration problem by learning the structured residual. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering...
Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found...
Microscopic analysis of histological sections is considered the “gold standard” to verify structural parcellations in the human brain. Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks. However, such cytoarchitectonic mapping is a semiautomatic, time consuming process that does...
Face Super Resolution(FSR) is to infer High Resolution(HR) facial images from given Low Resolution(LR) ones with the assistance of LR and HR training pairs. Among existing methods, local patch based methods are superior in visual and objective quality than global based methods. These local patch based methods are based on the consistency assumption that the neighbors in HR/LR space form similar local...
In this paper, we present a novel self-learning single image super-resolution (SR) method, which restores a highresolution (HR) image from self-examples extracted from the low-resolution (LR) input image itself without relying on extra external training images. In the proposed method, we directly use sampled image patches as the anchor points, and then learn multiple linear mapping functions based...
Resolution in medical images is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. In this paper, we propose a coupled dictionary learning approach for super...
Scene classification of high resolution remote sensing images plays an important role for a wide range of applications. While significant efforts have been made in developing various methods for scene classification, most of them are based on handcrafted or shallow learning-based features. In this paper, we investigate the use of deep convolutional neural network (CNN) for scene classification. To...
Despite the great advances made in the field of image super-resolution (ISR) during the last years, the performance has merely been evaluated perceptually. Thus, it is still unclear whether ISR is helpful for other vision tasks. In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications. In particular, six ISR methods are evaluated on...
Deep learning has shown great successes in solving various problems of computer vision. To the best of our knowledge, however, little existing work applies deep learning to saliency modeling. In this paper, a new saliency model based on convolutional neural network is proposed. The proposed model is able to produce a saliency map directly from an image's pixels. In the model, multi-level output values...
In the paper, comparative studies of three projection systems was carried out, i.e., With a cylindrical screen, with rear projection on foil placed on car windows -- "on screen", and with a collimation system. The purpose of the study was to assess the performance characteristics of visualization systems and the susceptibility of trainees to symptoms of simulator sickness. The results indicated...
Face hallucination can be a useful tool for visualizing a low quality face into a visually better quality, making it an attractive technology for many applications. While faces in surveillance videos are usually at very low resolution, in this paper, we propose to use face hallucination technology to visualize faces from visual surveillance systems, and develop a weighted scheme to enhance the quality...
In the context of developmental robotics, a robot has to cope with complex sensorimotor spaces by reducing their dimensionality. In the case of sensor space reduction, classical approaches for pattern recognition use either hardcoded feature detection or supervised learning. We believe supervised learning and hard-coded feature extraction must be extended with unsupervised learning of feature representations...
This paper deals with the interactive design of generic classifiers for aerial images. In many real-life cases, object detectors that work are not available, due to a new geographical context or a need for a type of object unseen before. We propose an approach for on-line learning of such detectors using user interactions. Variants of gradient boosting and support-vector machine classification are...
Due to limitations on the image capturing devices, distance, storage capability and bandwidth for transmission, many images in multimedia applications are low-bit-rate compressed and low resolution. In this paper, we proposed a class-specified learning based super resolution for this kind of low quality images. Firstly, we proposed a class-specified filter to remove the compressed distortions. Then...
Accessing the visual information of video content is a challenging task. Automatic annotation techniques have made significant progress, however they still suffer from the lack of appropriate training data. To overcome this problem we propose the use of still images taken from a photo sharing website as an additional resource for training. However, a mere extension of the training set with still images...
This paper presents a new exemplar-based image super-resolution (SR) method in which we propose making use of scale invariant image features for high frequency (HF) approximation. We introduce the scale invariant feature transform (SIFT) descriptors in both building an exemplar dataset adaptively and producing the HF details with respect to the features of an input low resolution image. Given a large...
This work contributes to part-based object detection and recognition by introducing an enhanced method for local part detection. The method is based on complex-valued multiresolution Gabor features and their ranking using multiple hypothesis testing. In the present work, our main contribution is the introduction of a canonical object space, where objects are represented in their ``expected pose and...
With recent technological advances, the geospatial industry produces digital image data at an astonishing rate. Such large amounts of data need to be analyzed for visual content in a timely fashion. For in-depth analysis of the geospatial there is a need to find efficient methods to process the visual information into actionable knowledge. One of the most promising methods is to evaluate the relevance...
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