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Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable...
Convolutional neural networks (CNNs) provide the current state of the art in visual object classification, but they are far less accurate when classifying partially occluded objects. A straightforward way to improve classification under occlusion conditions is to train the classifier using partially occluded object examples. However, training the network on many combinations of object instances and...
Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems. Various approaches have been proposed and shown to be visually plausible on small datasets collected by their authors. A quantitative comparison of existing approaches using the same dataset has never been conducted due to the lack of suitable benchmark...
Free/Open Source Software developers come from a myriad of different backgrounds, and are driven to contribute to projects for a variety of different reasons, including compensation from corporations or foundations. Motivation can have a dramatic impact on how and what contribution an individual makes, as well as how tenacious they are. These contributions may align with the needs of the developer,...
In this paper, we describe the application TaRDIS, a visual analytics system for spatial and temporal data designed for the needs of archaeo-related disciplines that supports domain experts in analyzing their data. The temporal data is visualized in form of an interactive Harris Matrix that illustrates the temporal position of the layers. The 2D and 3D visualization sketches the spatial position of...
We present two artistically-relevant algorithms to aid in the quantification of artistic style, the Discrete Tonal Measure (DTM) and Discrete Variational Measure (DVM). These quantitative features can provide clues to the artistic elements that enable art scholars to categorize works as belonging to different artistic styles. We also introduce two new datasets for analysis of artistic style: one based...
The success of fine-grained visual categorization (FGVC) extremely relies on the modeling of appearance and interactions of various semantic parts. This makes FGVC very challenging because: (i) part annotation and detection require expert guidance and are very expensive; (ii) parts are of different sizes; and (iii) the part interactions are complex and of higher-order. To address these issues, we...
We study large-scale multi-label classification (MLC) on two recently released datasets: Youtube-8M and Open Images that contain millions of data instances and thousands of classes. The unprecedented problem scale poses great challenges for MLC. First, finding out the correct label subset out of exponentially many choices incurs substantial ambiguity and uncertainty. Second, the large data-size and...
The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network (CNN) using a minimal model (Minimal CNN). The proposed minimal CNN is presented using a layering approach. This approach provides a concise and accessible understanding of the main mathematical operations of a CNN. Hence, it benefits...
Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN for learning spatio-temporal video representation. A few studies have shown that performing 3D convolutions is a rewarding approach to capture both spatial and temporal dimensions in videos. However, the development of a very deep...
During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend...
A major challenge in matching between vision and language is that they typically have completely different features and representations. In this work, we introduce a novel bridge between the modality-specific representations by creating a co-embedding space based on a recurrent residual fusion (RRF) block. Specifically, RRF adapts the recurrent mechanism to residual learning, so that it can recursively...
Most recent CNN architectures use average pooling as a final feature encoding step. In the field of fine-grained recognition, however, recent global representations like bilinear pooling offer improved performance. In this paper, we generalize average and bilinear pooling to “α-pooling”, allowing for learning the pooling strategy during training. In addition, we present a novel way to visualize decisions...
Visual or image-based self-localization refers to the recovery of a camera's position and orientation in the world based on the images it records. In this paper, we deal with the problem of self-localization using a sequence of images. This application is of interest in settings where GPS-based systems are unavailable or imprecise, such as indoors or in dense cities. Unlike typical approaches, we...
This paper aims to develop an effective flower classification approach using the technology of feature extraction. With this regard, a fused descriptor based on Pyramid Histogram of Visual Words (PHOW) is used to extract the color, texture and contour information of flower image. Secondly, Dictionary Learning and Locality-constrained Linear Coding (LLC) are operated on PHOW feature and then images...
Accurate and efficient assessment of barley quality is crucial for brewery industry. Currently, such the assessment is performed by a qualified expert, is expensive, time-consuming and may yield unreproducible results. We introduce a barley ontology (BO) to formalize the expert's knowledge and to involve information from industry standards. The BO specifies quality classes of malting barley, their...
Detecting potential aerial threats like drones with computer vision is at the paramount of interest for the protection of critical locations. This type of a system should prevent efficiently the false alarms caused by non-malign objects such as birds, which intrude the image plane. In this paper, we propose an improved version of a previously presented Speeded-up Robust Feature Transform (SURF) based...
In Gradient-Based Cross-Spectral Stereo Matching (GB-CSSM) output disparity maps tend to produce coarse results that are, for the most part, reliable. However, general methods of improving the performance of disparity maps generated from the Cross-Spectral comparison of visual and full infrared input images are non-existent. In particular, previous works fail to address the role and interaction of...
This work applies the Gaussian Mixture Probability Hypothesis Density (GMPHD) Filter to multi-object tracking in video data. In order to take advantage of additional visual information, Kernelized Correlation Filters (KCF) are evaluated as a possible extension of the GMPHD tracking-by-detection scheme to enhance its performance. The baseline GMPHD filter and its extension are evaluated on the UA-DETRAC...
Recently, kernelized correlation Filter-based trackers have aroused the interest of many researchers and achieved good results in the field of tracking. However, the current tracking model based on kernelized correlation filters can not deal with the changes of the target appearance and scale effectively. Therefore, in this paper, we intend to solve these two problems and improve the robustness of...
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