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The intrinsic interactions among a video's emotion tag, its content, and a user's spontaneous response while consuming the video can be leveraged to improve video emotion tagging, but this capability has not been thoroughly exploited yet. In this paper, we propose an implicit hybrid video emotion tagging approach by integrating video content and users' multiple physiological responses, which are only...
Modern young people (“digital natives”) have grown in an era dominated by new technologies where communications are pushed to quite a real-time level, and pose no limits in establishing relationships with other people or communities. However, the speed of evolution does not allow young people to split consciously acceptable behaviors from potentially harmful ones and a new phenomenon known as cyber...
We propose a machine learning based approach to real-time detection and classification assistance for images from unknown environments. While systems for detecting and classifying regular structures like faces in still images are well established, the task of e. g. detecting new morphotypes/objects in an environment is much more complex. The morphotypes/objects are not guaranteed to have apriori known...
This paper presents a method for detecting a pedestrian by leveraging multi-spectral image pairs. Our approach is based on the observation that a multi-spectral image, especially far-infrared (FIR) image, enables us to overcome inherent limitations for pedestrian detection under challenging circumstances, such as even dark environments. For that task, multi-spectral color-FIR image pairs are used...
Fast R-CNN is a well-known approach to object detection which is generally reported to be robust to scale changes. In this paper we examine the influence of scale within the detection pipeline in the case of company logo detection. We demonstrate that Fast R-CNN encounters problems when handling objects which are significantly smaller than the receptive field of the utilized network. In order to overcome...
Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based...
Automatically recognising facial emotions has drawn increasing attention in computer vision. Facial landmark based methods are one of the most widely used approaches to perform this task. However, these approaches do not provide good performance. Thus, researchers usually tend to combine more information such as textural and audio information to increase the recognition rate. In this paper we propose...
With the rapid increase of multimedia data, textual content in an image has become a very important source of information for several applications like navigation, image search and retrieval, image understanding, captioning, machine translation and several others. Scene text localization is the first step towards such applications and most current methods focus on generating a small set of high precision...
This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren & Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety...
Wireless capsule endoscopy video summarization (WCE-VS) is highly demanded for eliminating redundant frames with high similarity. Conventional WCE-VS methods extract various hand-crafted features as image representations. Researches show that such features only reflect the low-level characteristics of single frame and essentially are not effective to capture the semantic similarity between WCE frames...
In real applications of one class classification, new features may be added due to some practical or technical reason. While lacking of representative samples for the new features, multi-task learning idea could be used to bring some information from the former learning model. Based on the above assumption, a new multi-task learning approach is proposed to deal with the training of the updated system...
The color constancy problem is addressed by structured-output regression on the values of the fully-connected layers of a convolutional neural network. The AlexNet and the VGG are considered and VGG slightly outperformed AlexNet. Best results were obtained with the first fully-connected “fc6” layer and with multi-output support vector regression. Experiments on the SFU Color Checker and Indoor Dataset...
Deep learning-based models have recently been widely successful at outperforming traditional approaches in several computer vision applications such as image classification, object recognition and action recognition. However, those models are not naturally designed to learn structural information that can be important to tasks such as human pose estimation and structured semantic interpretation of...
Nonverbal cues constitute a significant part of human communication. Traditionally the object of psychology, nonverbal communication studies now permeate fields such as social signal processing and human computer interaction. The ubiquity of digital recordings of human social interactions and of free sharing platforms offers many opportunities for the automated analysis of group interaction dynamics;...
Automatic classification of Human Epithelial Type-2 (HEp-2) specimen patterns is an important yet challenging problem in medical image analysis. Most prior works have primarily focused on cells images classification problem which is one of the early essential steps in the system pipeline, while less attention has been paid to the classification of whole-specimen ones. In this work, a specimen pattern...
Background subtraction (BS) is one of the key steps for detecting moving objects in video surveillance applications. In the last few years, many BS methods have been developed to handle the different challenges met in video surveillance but the role and the relevance of the visual features used has been less investigated. In this paper, we present an Online Weighted Ensemble of One-Class SVMs (Support...
Micro-expression recognition is a challenging task in computer vision field due to the repressed facial appearance and short duration. Previous work for micro-expression recognition have used hand-crafted features like LBP-TOP, Gabor filter and optical flow. This paper is the first work to explore the possible use of deep learning for micro-expression recognition task. Due to the lack of data for...
Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points. In spite of advancements in the last decades, performance of such systems is still far from optimal when we test the systems against skilled forgeries - signature forgeries that target a particular individual. In previous...
Gender estimation has received increased attention due to its use in a number of pertinent security and commercial applications. Automated gender estimation algorithms are mainly based on extracting representative features from face images. In this work we study gender estimation based on information deduced jointly from face and body, extracted from single-shot images. The approach addresses challenging...
We propose a method to recognize pollen grains using a two-stage classifier. First, texture classification categorizes the pollen grains into sub-groups. Then, a final classification of individual pollen types is done by segmenting the image int multiple layers of regions for each pollen image. The main novelty in our method is threefold: (1) Adopting two successive classification stages. (2) Combining...
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