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Retinal implants are raising hope for blind people to restore part of their visual capabilities. Enormous advances have taken place in this field while several groups worldwide are striving to achieve this long-yearned-for accomplishment. Current studies about visual perception parameters from patients using subretinal implants showed that effects like electric field interference limit the achievable...
In this paper, we propose a fast and long-term object tracking algorithm using the ℓ2,1 minimization to obtain a better tracking quality. Our method is based on Regularized Least-Squares Classification (RLSC), in which the target model is updated using an online learning process during object tracking. We construct an appearance model using saliency map, image intensity and position of the target...
This paper develops a new algorithm based on Bag-of-Word to reflect spatial relationship of objects for visual object categorization. Beyond existing spatial pyramid for image representation, our contributions are the following: 1) we propose a combinational detector based on Maximally Stable Extremal Regions detector and Hessian-Laplacian detector to extract more discriminative features; 2) for object...
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...
Several studies on autism spectrum disorder (ASD) show that there exists significant heterogeneity in phenotype of the disorder. Additionally, many published findings also suggested that ASD is defined by atypical local/global processing. In this paper, we designed a puzzled-based intervention to examine the sensitiveness to the information of local /global processing on individuals with ASD. Additionally,...
The inspection, assessment, maintenance and safe operation of the existing civil infrastructure consists one of the major challenges facing engineers today. Such work requires either manual approaches, which are slow and yield subjective results, or automated approaches, which depend upon complex handcrafted features. Yet, for the latter case, it is rarely known in advance which features are important...
We propose an autism spectrum disorder (ASD) prediction system based on machine learning techniques. Our work features the novel development and application of machine learning methods over traditional ASD evaluation protocols. Specifically, we are interested in discovering the latent patterns that possibly indicate the symptom of ASD underneath the observations of eye movement. A group of subjects...
We present Extractor and Viewer, two tools from the Kayrebt toolset. The former is a plugin for the Gnu Compiler Collection (GCC) which builds pseudo-UML2 activity diagrams from C source code. It is specifically designed to handle the Linux kernel, a large and complex codebase. Use cases for this tool are numerous. The diagrams extracted from the C source code can be used to get a better insight of...
Recent advances in graphics processing units (GPUs) have resulted in massively parallel hardware that is widely available to achieve high performance in desktop, notebook, and even mobile computer systems. While multicore technology has become the norm of modern computers, programming such systems requires the understanding of underlying hardware architecture and hence posts a great challenge for...
The goal of video summarization is to turn large volume of video data into a compact visual summary that can be easily interpreted by users in a while. Existing summarization strategies employed the point based feature correspondence for the superframe segmentation. Unfortunately, the information carried by those sparse points is far from sufficiency and stability to describe the change of interesting...
P300 speller for Brain-Computer Interface systems aim to provide a direct communication between computer - machine and human brain, without any muscular activity. The communication is provided by detecting the presence of P300 Event Related Potentials (ERPs) in the electroencophelogram (EEG) signals, recorded from scalp. The major problem associated with P300 spellers is the stratification of EEGs...
This paper proposes a system to address the problem of visual speech recognition. The proposed system is based on visual lip movement recognition by applying video content analysis technique. Using spatiotemporal features descriptors, we extracted features from video containing visual lip information. A preprocessing step is employed by removing the noise and enhancing the contrast of images in every...
Visual search and classification applications often use local features for image representation and description. Various detectors and descriptors have been developed for extracting these features. The local descriptors can be aggregated into a global image signature for a more compact representation. The global signature can be used in mobile applications where memory and computation time is critical...
We propose the technique of the semi-automatic image creation. By this we mean an automatic completion of an image that is partially defined on the given domain. The essential feature of this technique is that the complementary area is much larger than that where the image is defined. Moreover, the proposed technique can be used in image upsampling, image inpainting, etc. In this contribution, we...
Scene classification is a key problem in the interpretation of high-resolution remote sensing imagery. The state-of-the-art methods, e.g. bag-of-visual-words model and its various extensions as well as the topic models, share similar procedures: patch sampling, feature description/learning and classification. Patch sampling is the first and the key procedure which has a great influence on the results...
Scene classification for high-resolution remotely sensed imagery have been widely investigated in recent years. However, there is few public, widely accepted and large scale dataset for benchmarking different methods. This paper presents a new and large dataset consisting of 5000 high-resolution remote sensing images which is manually labeled in 20 semantic classes for scene classification. Each class...
In this paper, we proposes a visual-based vehicle classification system, in which it involves visual feature representation and classification step. In the feature representation step, we present a center enhanced spatial pyramid matching (CE-SPM) to extract the feature from images. In this work, we defined additional region in the center of each images to calculate the histograms of visual words...
Compared to image representation based on low-level local descriptors, deep neural activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation, but poorer in geometric invariance properties. In this paper, we present a straightforward framework for better image representation by combining the two approaches. To take advantages of both representations, we extract a fair...
This paper proposes a superpixel tracking method via a graph-based hybrid discriminative-generative appearance model. By utilizing a superpixel-based graph structure as the visual representation, spatial information between superpixels is considered. For constructing the discriminative appearance model, we propose a graph-based semi-supervised support vector machine (SVM) approach by taking superpixels...
We propose a two stage visual matching pipeline including a first step using VLAD signatures for filtering results, and a second step which reranks the top results using raw matching of SIFT descriptors. This enables adjusting the tradeoff between high computational cost of matching local descriptors and the insufficient accuracy of compact signatures in many application scenarios. We describe GPU...
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