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Computer-aided analyses of motion capture data require an effective and efficient concept of motion similarity. Traditional methods generally compare motion sequences by applying time-warping techniques to high-dimensional trajectories of joints. An increasing effectiveness of machine-learning techniques, such as deep convolutional neural networks, brings new possibilities for similarity comparison...
While recent advances in deep learning pushed the state-of-the-art in object detection and semantic segmentation, it often comes at the cost of a considerable annotation effort. Thus, weakly supervised learning became of increasing interest. In this paper a novel approach to the challenging task of weakly supervised segmentation and object localization will be presented. The problem is tackled from...
Object deformation and occlusion are ubiquitous problems for visual tracking. Though many efforts have been made to handle object deformation and occlusion, most existing tracking algorithms fail in case of large deformation and severe occlusion. In this paper, we propose a graph learning-based tracking framework to handle both challenges. For each consecutive frame pair, we construct a weighted graph,...
Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two...
A big challenge in the precision agriculture is the detection of fruits in coffee crops on agricultural environments. This paper presents a comparison of four features set to detect the red fruits (mature) in Coffee plants. An Unmanned Aerial Vehicle (UAV) is used to obtain high-resolution RGB images of a coffee hall. The proposed methodology enables the extraction of visual features from image regions...
Numerous style transfer methods which produce artistic styles of portraits have been proposed to date. However, the inverse problem of converting the stylized portraits back into realistic faces is yet to be investigated thoroughly. Reverting an artistic portrait to its original photo- realistic face image has potential to facilitate human perception and identity analysis. In this paper, we propose...
Stereo matching is a fundamental task in vision applications. we propose an adaptive cross-scale aggregation method for stereo matching, which is introduced by solving an optimization problem. Unlike the original approach which introduces the same regularization term based on the inter-scale regularizer parameter to control the cost consistency among the multi-scales for all regions of the input images...
We propose a simple yet effective pre-classification method for background removal and bruise separation for automatic fruit sorting and grading systems. Different fruits require different pre-classification segmentation approach driven by a unique logic in regard of specific appearance of the concerning fruit. The proposed method is experimented upon guava samples having different tones of bruises,...
Skin segmentation, which involves detecting human skin areas in an image, is an important process for skin disease analysis. The aim of this paper is to identify the skin regions in a newly collected set of psoriasis images. For this purpose, we present a committee of machine learning (ML) classifiers. A psoriasis training set is first collected by using pixel values in five different color spaces...
Identification of the correct medicinal plants that goes in to the preparation of a medicine is very important in ayurvedic medicinal industry. The main features required to identify a medicinal plant is its leaf shape, colour and texture. Colour and texture from both sides of the leaf contain deterministic parameters to identify the species. This paper explores feature vectors from both the front...
Blind image quality assessment (BIQA) methods aim to estimate the quality of a given test image without referring to the corresponding reference (original) image. Most BIQA methods use visual sensitivity models, which take into consideration intrinsic image characteristics (e.g. contrast, luminance, and texture) to identify degradations and estimate quality. For example, texture-based BIQA methods...
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...
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