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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...
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...
This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering. Unlike most other deep learning strategies applied in this context, our approach tackles these challenging problems by estimating edges and reconstructing images using only cascaded convolutional layers arranged such that...
Precise search of visually-similar vehicles poses a great challenge in computer vision, which needs to find exactly the same vehicle among a massive vehicles with visually similar appearances for a given query image. In this paper, we model the relationship of vehicle images as multiple grains. Following this, we propose two approaches to alleviate the precise vehicle search problem by exploiting...
We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives. This supervision signal is obtained from an equivariance relation, which does not require any manual annotation. We relate transformations of images to transformations of the representations. More specifically, we look for the representation that satisfies such relation...
Although shadows in images have a constructive role providing a natural view of features of the scene, they also have a destructive role in image processing by hiding significant information. Improving the quality of 3D textured models for serious games and augmented reality applications via shadow detection and removal remains challenging due to the complexity of an image scene. This paper proposes...
Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify similar persons. Because of challenges regarding changes in lighting between views, occlusion or even privacy...
The aim of this paper is to compare two different types of filter for the diving video processing. The two filters are a boolean filter and a fuzzy filter. These filters are applied for improving the diving video analysis aimed to introduce quantitative tools and diving performance measurement and therefore to improve training. The aim of the filter is to identify the athlete in the video to further...
Usually the static or dynamic characteristics of the flame are extracted for flame detection. But the relationship between the various features of flame could not be distinguished by the human eye. the Gradient Boost Decision Tree (GBDT) is thus proposed to combine and optimize the flame shape and texture features, so as to mine the relationship of flame features. Then the more discriminant new flame...
In this study, we propose a novel shape-based traffic sign detection method which consists of two stages. First, a rotational symmetry voting scheme is proposed to detect the centers and boundary sets of the candidate polygons in the image. Second, a Link Distribution (LD) model, which considers a polygon as the collection of links between center and boundary points, is proposed to refine the detection...
Security is obligatory for digital world. It requires robust and reliable security mechanisms which comprises irreplaceable identification of individual. Biometrics plays an important role in recognizing individual uniquely, furthermore iris based security is more impenetrable as compared to fingerprint based security. Also, human iris doesn't change with ageing and can be easily captured. Generic...
We present a conditional generative method that maps low-dimensional embeddings of image and natural language to a common latent space hence extracting semantic relationships between them. The embedding specific to a modality is first extracted and subsequently a constrained optimization procedure is performed to project the two embedding spaces to a common manifold. Based on this, we present a method...
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