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This paper presents a new technique to solve the single image super resolution reconstruction problem based on multiple extreme learning machine regressors, called here MELM. The MELM employs a feature space of low resolution images, divided in subspaces, and one regressor is trained for each one. In the training task, we employ a color dataset containing 91 images, with approximately 5.3 million...
The frequent occurrence of road congestion and traffic accidents has affected people's travel efficiency and travel safety. Traffic sign recognition has become one of the key research objects in intelligent transportation system. This paper studies the identification of road traffic signs based on video images. First of all, collected image will be image preprocessing with image reduction, brightness...
Smart human tracking systems based on surveillance camera are very popular recently. For example, retailers and museums use head counting to analyze the consumer statistics. This paper proposed an approach to detect multiple heads, which can be applied in smart human tracking system. The computing resource of this kind of applications is so high that it is not applicable in embedded platforms. So,...
In the person re-identification across multiple camera research field, attributes of the pedestrian are important cues to differentiate the appearance of each identity. In this work, ten types of attributes are considered as defined in the DukeMTMC-attribute dataset. A custom deep network architecture is proposed to perform the identification process. Furthermore, experiments were carried out to assess...
In order to reduce the number of accidents caused by the call when the driver was driving, this paper uses the computer vision technology to dectet the behavior of the driver. Based on the constrained local models (CLM) to detect the characteristic changes of the mouth area, combine the HSV color space and the template matching to detect the hand characteristics to judge whether the driver has the...
Moving object tracking with discriminative model is very popular in recent years, which focuses on online selecting highly informative features to maximize the separability between object and background. An adapted particle filter tracker with online learning and inheriting discriminative model is proposed in this paper. Top-ranked discriminative features are selected into appearance model by Online...
The interactive image segmentation model allows users to iteratively add new inputs for refinement until a satisfactory result is finally obtained. Therefore, an ideal interactive segmentation model should learn to capture the user's intention with minimal interaction. However, existing models fail to fully utilize the valuable user input information in the segmentation refinement process and thus...
The hand segmentation is the critical pre-processing of the gesture recognition application. Nowadays, to achieve a robust hand segmentation under cluttered background is still challenging. Advanced research in model-driven approach based on the depth information has obtained impressive performance. However, it is unable to deal with the hand very close to the body part. Also, a large number of marked...
The present work proposes a neurofeedback training system for the induction of an attention state aided by audiovisual stimuli on an experimental group of nine junior high school individuals between twelve and fifteen years old. A control group of 10 individuals with the same characteristics as the experimental group is defined as well to validate the training's efficiency. The auditory stimulation...
We report an experimental study that involves understanding how display (conventional or ecological) and system mode (profiting, neutral or losing) affect financial trading performance and risk preference. Twenty-four undergraduate and graduate student participants interacted with a financial trading simulator in the playback of a real market. Each participant completed a conventional display scenario...
The high-level feature representation of deep convo-lutional neural networks (ConvNets) has proven to be superior to hand-crafted low-level features. Thus, this study investigates the effect of fusing such high-level features from multi-deep ConvNets under an application of visual object/scene categorization. In which, three pre-trained ConvNets are exploited as feature extractors, a single hidden...
This paper presents a multiple classifier system (MCS) to identify plants species based on the texture and shape features extracted from leaf images. A diverse pool of SVM and Neural Network classifiers is trained on four different feature sets, namely, Local Binary Pattern (LBP), Histogram of Gradients (HOG), Speed of Robust Features (SURF) and Zernike Moments (ZM). Then, a static classifier selection...
This paper proposes a new optical camouflage system that uses RGB-D cameras, for acquiring point cloud of background scene, and tracking observers' eyes. This system enables a user to conceal an object located behind a display that surrounded by 3D objects. If we considered here the tracked point of observer's eyes is a light source, the system will work on estimating shadow shape of the display device...
Harbor water area extraction is a key step in nearshore environment pollution surveillance using remote sensing image processing techniques. This letter proposes the definition circle (DC) model of color gradient to describe color fluctuations in harbor water surface areas based on pan-sharpened remote sensing images. The DC model includes two steps: center setting and radius tuning. In the center...
Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations – small sensor size, compact lenses and the lack of specific hardware, – impede them to achieve the quality results of DSLR cameras. In this work we present an end-to-end deep learning approach that bridges this gap by translating ordinary photos into DSLR-quality images. We propose learning the translation...
While natural beauty is often considered a subjective property of images, in this paper, we take an objective approach and provide methods for quantifying and predicting the scenicness of an image. Using a dataset containing hundreds of thousands of outdoor images captured throughout Great Britain with crowdsourced ratings of natural beauty, we propose an approach to predict scenicness which explicitly...
We investigate methods for combining multiple selfsupervised tasks—i.e., supervised tasks where data can be collected without manual labeling—in order to train a single visual representation. First, we provide an apples-toapples comparison of four different self-supervised tasks using the very deep ResNet-101 architecture. We then combine tasks to jointly train a network. We also explore lasso regularization...
Person re-identification (Re-ID) is an important problem in video surveillance, aiming to match pedestrian images across camera views. Currently, most works focus on RGB-based Re-ID. However, in some applications, RGB images are not suitable, e.g. in a dark environment or at night. Infrared (IR) imaging becomes necessary in many visual systems. To that end, matching RGB images with infrared images...
This paper presents a real-time vision based robot teleoperation system that consists of a three-dimensional (3D) vision subsystem and a slave robot which are connected by LAN. The vision subsystem utilizes an Asus Xtion Pro Live camera to get the 3D data of the operation scene. The vision system is used to determine the position and orientation of a four-ball feature frame held by the operator. Then...
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites. We first fine-tune GoogleNet by jointly modeling clothing images and their corresponding descriptions in a visual-semantic embedding space. Then, for each attribute (word), we generate its spatiallyaware representation by combining its semantic word vector representation...
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