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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...
Monitoring phenology of agricultural plants is a critical understanding in precision agriculture. Vital improvements can be achieved with precise detection of phenological change of plants which would henceforth improve the timing for the harvest, pest control, yield prediction, farm monitoring, disaster warning etc. Many countries across the world have been developing initiatives to build national...
Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior methods 1) only use low-level features and 2) lack high-level context. In this paper, we propose a novel deep learning based algorithm that can tackle both these problems...
Accurate cell detection is often an essential prerequisite for subsequent cellular analysis in computer aided diagnosis (CAD) for histopathlogy images. It is challenging due to high cell density, touching cells, low contrast, variant cell shapes and sizes, weak boundaries and the use of different image acquisition techniques. Existing methods are often struggling at tackling with the challenges at...
In this paper, we present a novel approach to estimate the relative depth of regions in monocular images. There are several contributions. First, the task of monocular depth estimation is considered as a learning-to-rank problem which offers several advantages compared to regression approaches. Second, monocular depth clues of human perception are modeled in a systematic manner. Third, we show that...
Tongue manifestation is one of the most significant basic criteria for the diagnosis of Traditional Chinese Medicine (TCM). And tongue color recognition with high accuracy will contribute to the efficiency of TCM diagnosis. The drawbacks of traditional tongue diagnosis methods are that the features need to be designed artificially. While the feature acquisition from the deep learning is a process...
Assessment of aging civil infrastructure should be done periodically to getting information about the structural condition. In context to it, classification, detection, and localization of cracks within these concrete structures is of paramount importance. The most commonly used procedure, i.e. visual inspection, is executed manually by human inspectors, and thus, its accuracy depends on personnel's...
This paper presents fine-tuned CNN features for person re-identification. Recently, features extracted from top layers of pre-trained Convolutional Neural Network (CNN) on a large annotated dataset, e.g., ImageNet, have been proven to be strong off-the-shelf descriptors for various recognition tasks. However, large disparity among the pre-trained task, i.e., ImageNet classification, and the target...
According to the similar nutritional properties, foods could be classified in six groups (Vegetables, Fruits, Dairy, Oils, Grains and Protein foods) and nourish human body respectively. However, people could not understand the nutrients of foods which they obtained generally. Hence, this paper proposes a system based on deep learning for training. Users take pictures on diets by their smartphones...
The insulator is an import part of transmission line, and the defects detection of insulator rely deeply on the insulators' position. Traditional methods about insulator recognition task are depend on color features and geometric features, those methods would be influenced by lots of factors, such as illumination and background in result getting poor generalization ability. In this paper, we propose...
Automatically recognizing pornographic images from the Web is a vital step to purify Internet environment. Inspired by the rapid developments of deep learning models, we present a deep architecture of convolutional neural network (CNN) for high accuracy pornographic image recognition. The proposed architecture is built upon existing CNNs which accepts input images of different sizes and incorporates...
Paper describes the developed approaches to detecting emergency situations that primed based on color segmentation, frame difference and deep convolutional networks. The main objective was to test the interaction of computer vision traditional methods, combined with modern methods of machine learning. Experimentally proved that detection quality for the combination of such methods is 96.7%. In this...
The widespread use of video recording devices to obtain recordings of people in various scenarios makes the problem of privacy protection increasingly important. Consequently, there is an increased interest in developing methods for de-identification, i.e. removing personally identifying features from publicly available or stored data. Most of related work focuses on de-identifying hard biometric...
Deep feedforward neural networks with piecewise linear activations are currently producing the state-of-the-art results in several public datasets (e.g., CIFAR-10, CIFAR-100, MNIST, and SVHN). The combination of deep learning models and piecewise linear activation functions allows for the estimation of exponentially complex functions with the use of a large number of subnetworks specialized in the...
This paper considers the problem of automatically extracting a foreground element with its alpha matte in green screen images by training a multi-layer perceptron (MLP) with the back-propagation algorithm. The classifier learns to identify green backgrounds, foreground object contours, and the corresponding alpha values for subsequent digital compositing. We developed our own dataset to train and...
This paper presents an accurate and automatic algorithm to recognize and count fish in the video footages of fishery operations. The unique character of the approach is that it combines machine learning techniques with statistical methods to fully make use the benefits of these algorithms. The approach consists of three major stages including video data preparation such as noise deduction, preliminary...
Most current approaches for recognition in RGB-D images fall in either the late fusion or the early fusion category. A drawback of the early fusion scheme is its inapplicability when one of the modalities is absent at test time. On the other hand, a late fusion of features does not allow the correlated nature of modalities to be exploited effectively. Recent approaches using Deep Learning are not...
To improve the performance of kinship verification, we propose a novel deep kinship verification (DKV) model by integrating excellent deep learning architecture into metric learning. Unlike most existing shallow models based on metric learning for kinship verification, we employ a deep learning model followed by a metric learning formulation to select nonlinear features, which can find the appropriate...
Matching observations captured by pedestrian detectors across the cameras with non-overlapping views, known as person re-identification, is challenging due to the appearance changes caused by pose, viewpoint and illumination variations, occlusions and cluttered background. Different from various hand-crafted features, this paper extract the features through the fine-tuned deep convolutional neural...
During the last decades, several different techniques have been proposed for computer recognition of human faces. A further step in the development of these biometrics is to implement them in portable devices, such as mobile phones. Due to this devices' features and limitations it is necessary to select, among the currently available algorithms, the one with the best performance in terms of algorithm...
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