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In previous works of ours [1-3], we proposed a neural network-based face detection and facial expression analysis system, which was able to classify three expressions in frontal view face images. In the present work, we examine the possibility of classifying these expressions in side view face images. Specifically, we evaluate the extracted facial feature discrimination power of three image acquisition...
Abnormal activity detection plays an important role in many areas such as surveillance, military installations, and sports. Existing abnormal activity detectors mostly rely on motion data obtained over a number of frames to characterize abnormality. However, only motion may not be able to capture all forms of abnormality, in particular, poses that do not amount to motion "outliers". In this...
In recent years, a tremendous research effort has been made in the area of generic object recognition. However, the most important thing is not the names but functions for robots to comprehend objects. Object functions refer to “the purpose that something has or the job that someone or something does”. Various elements (e.g., the physical information, material, appearance and human interaction) independently...
Highly unique pattern of the vein within the finger makes it robust biométrie characteristic which is instrumental in achieving low error rates for verification. Vascular pattern is reliable in many terms as they are characterized by very low error rates, good spoofing resistance and user convenience when compared with other existing biométrie modalities. In this paper, we present an extensive study...
Recognizing human actions in video has gradually attracted much attention in computer vision community, however, it also faces many realistic challenges caused by background clutter, viewpoint changes, variation of actors appearance. These challenges reflect the difficulty of obtaining a clean and discriminative video representation for classification. Recently, VLAD (Vector of Locally Aggregated...
Word level Script and language identification is a process of separating the script and language of each word present in a printed or handwritten multi-script document. It is an essential part of a multi-lingual Optical Character Recognizer (OCR). Most of the OCRs are solely designed for a single script. So it can't convert a document which is written in more than one script. This paper explained...
Most existing methods for abnormal event detection in the literature are relied on a training phase. Different from conventional approaches for abnormal event detection, a saliency attention based abnormal event detection approach is proposed in this paper. It is inspired by the visual attention mechanism that abnormal events are those which attract attention mostly in videos. The temporal and spatial...
Activities capture vital facts for the semantic analysis of human behavior. In this paper, we propose a method for recognizing human activities based on periodic actions from a single instance using convolutional neural networks (CNN). The height of the foot above the ground is considered as features to discriminate human locomotion activities. The periodic nature of actions in these activities is...
In this paper, we propose a cascade dictionary learning algorithm for action recognition. In the first stage, a dictionary for basic sparse coding is learned based on local descriptors. And then spatial pyramid features are extracted to represent all the images in the same dimensions. Instead of performing dimension reduction, all the features are regrouped and then fed into second dictionary learning...
Pedestrian detection is an important area in computer vision with key applications in intelligent vehicle and surveillance systems. One of the main challenges in pedestrian detection is occlusion. In this paper, we propose a novel pedestrian detection approach capable of handling partial occlusion. Three stage cascaded classifier is used in the proposed approach. Global classifier based on HOG features...
We introduces a new 3-D video dataset to assess the performance of Human Activity Recognition system in indoor and outdoor environment. This dataset also help to check the performance of activity recognition algorithms against the effect of varying illumination, background and viewpoint. The available dataset for activity recognition are simple and most of them contain RGB information only as well...
In this paper we propose a new high-quality and efficient single image super-resolution model that extends exploit the self-similarity property. The similarity of frequency error compensation between the high-resolution patch and low-resolution model can modeled as a optimization problem. Based on the in-place patch similarity, the optimization model is further simplified to alleviate the computing...
The images of lace textile are particularly difficult to be analyzed in digital form using classical image processing techniques. The major reasons of this difficulty emerge from the complex nature of lace which generally has different textures in its constituents like the background and patterns. In this paper, we study the behavior of Image Histogram (HistI) and Local Binary Patterns (LBP) on image...
In this paper, we present a new variations of the popular nonnegative matrix factorization (NMF) approach to extend it to the data with negative values. When a NMF problem is formulated as μ ≈μμ, we try to develop a new method that only allows μ to contain nonnegative values, but allows both μ and μ to have both nonnegative and negative values. In this way, the original NMF is extended to be used...
With the advancement of motion capture technology, 3D skeleton data is easier to be obtained. 3D skeleton data has the advantage over traditional video data for the reason that it is less affected by illumination, complex background, self-occlusion and noise. 3D skeleton data brings new opportunities and challenges to the action recognition research. In this paper, we propose a new method for action...
In Adaptive Case Management (ACM) systems, knowledge workers have the flexibility to deal with unpredictable situations. Compared with a classical BPM approach the extensive prescriptive process analysis and definitions are replaced by context-sensitive proposals, which is more suited for knowledge-intensive work. Thus, it is vital that ACM systems support knowledge workers with knowledge captured...
This paper proposes a video retargeting method. The method makes multimedia more suitable for ubiquitous video access, including comfortable watching, interesting region detection and safety transmitting. Because of the rapid progress of electronic commercial product, video service needs to adapt different device. There are some challenges: First at all, the resolution, aspect ratio and size of display...
Traffic accidents are a fact of life. While accidents are sometimes unavoidable, studies show that the long response time required for emergency responders to arrive is a primary reason behind increased fatalities in serious accidents. One way to reduce this response time is to reduce the amount of time it takes to report an accident. Smartphones are ubiquitous and with network connectivity are perfect...
In this paper, we address the problem of recognizing group activities that include interactions between human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene so as to be robust against noisy information. Two novel features,...
In multi-label learning, each sample can be assigned to multiple class labels simultaneously. In this work, we focus on the problem of multi-label learning with missing labels (MLML), where instead of assuming a complete label assignment is provided for each sample, only partial labels are assigned with values, while the rest are missing or not provided. The positive (presence), negative (absence)...
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