This paper presents a general framework for live detection of broilers in poultry houses. The challenges for image recognition of broilers are posted by crowded scenes, poor image quality and difficulty in acquiring a...
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ISBN:
(纸本)9781538622193
This paper presents a general framework for live detection of broilers in poultry houses. The challenges for image recognition of broilers are posted by crowded scenes, poor image quality and difficulty in acquiring a benchmark of labeled samples. The proposed framework consists on the use of image thresholding, morphological transformations, feature engineering, in addition to supervised and unsupervised learning techniques. Results show the effectiveness of the proposed framework to detect individual broilers in a poultry house image. Descriptive attributes related to the spatial distribution and movement of the broilers can be extracted using the resultant detections. These attributes can be used by automated warning systems, for the detection of anomalous events and thermal stress conditions.
image dehazing can be described as the problem of mapping from a hazy image to a haze-free image. Most approaches to this problem use physical models based on simplifications and priors. In this work we demonstrate th...
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ISBN:
(纸本)9781538622193
image dehazing can be described as the problem of mapping from a hazy image to a haze-free image. Most approaches to this problem use physical models based on simplifications and priors. In this work we demonstrate that a convolutional neural network with a deep architecture and a large image database is able to learn the entire process of dehazing, without the need to adjust parameters, resulting in a much more generic method. We evaluate our approach applying it to real scenes corrupted by haze. The results show that even though our network is trained with simulated indoor images, it is capable of dehazing real outdoor scenes, learning to treat the degradation effect itself, not to reconstruct the scene behind it.
We present a simple representation for periodic tilings of the plane by regular polygons. Our approach is to represent explicitly a minimal subset of the vertices from which we systematically generate all vertices in ...
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We present a simple representation for periodic tilings of the plane by regular polygons. Our approach is to represent explicitly a minimal subset of the vertices from which we systematically generate all vertices in the tiling by translations. We then deduce the edges and the faces using the constraint that all edges have the same length. Our representation can be used to synthesize tilings manually and automatically from images.
Deep Learning methods are currently the state-of-the-art in many computer Vision and imageprocessing problems, in particular image classification. After years of intensive investigation, a few models matured and beca...
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ISBN:
(纸本)9781538606193
Deep Learning methods are currently the state-of-the-art in many computer Vision and imageprocessing problems, in particular image classification. After years of intensive investigation, a few models matured and became important tools, including Convolutional Neural Networks (CNNs), Siamese and Triplet Networks, Auto-Encoders (AEs) and Generative Adversarial Networks (GANs). The field is fast-paced and there is a lot of terminologies to catch up for those who want to adventure in Deep Learning waters. This paper has the objective to introduce the most fundamental concepts of Deep Learning for computer Vision in particular CNNs, AEs and GANs, including architectures, inner workings and optimization. We offer an updated description of the theoretical and practical knowledge of working with those models. After that, we describe Siamese and Triplet Networks, not often covered in tutorial papers, as well as review the literature on recent and exciting topics such as visual stylization, pixel-wise prediction and video processing. Finally, we discuss the limitations of Deep Learning for computer Vision.
Deep learning has revolutionized computer vision and other fields since its big bang in 2012. However, it is challenging to deploy Deep Neural Networks (DNNs) into real-world applications due to their high computation...
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ISBN:
(纸本)9781538643686
Deep learning has revolutionized computer vision and other fields since its big bang in 2012. However, it is challenging to deploy Deep Neural Networks (DNNs) into real-world applications due to their high computational complexity. Binary Neural Networks (BNNs) dramatically reduce computational complexity by replacing most arithmetic operations with bitwise operations. Existing implementations of BNNs have been focusing on GPU or FPGA, and using the conventional image-to-column method that doesn't perform well for binary convolution due to low arithmetic intensity and unfriendly pattern for bitwise operations. We propose BitFlow, a gemm-operator-network three-level optimization framework for fully exploiting the computing power of BNNs on CPU. BitFlow features a new class of algorithm named PressedConv for efficient binary convolution using locality-aware layout and vector parallelism. We evaluate BitFlow with the VGG network. On a single core of Intel Xeon Phi, BitFlow obtains 1.8x speedup over unoptimized BNN implementations, and 11.5x speedup over counterpart full-precision DNNs. Over 64 cores, BitFlow enables BNNs to run 1.1x faster than counterpart full-precision DNNs on GPU (GTX 1080).
In this survey, we present a review of methods and resources for texture recognition, presenting the most common techniques that have been used in the recent decades, along with current tendencies. That said, this pap...
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ISBN:
(纸本)9781538606193
In this survey, we present a review of methods and resources for texture recognition, presenting the most common techniques that have been used in the recent decades, along with current tendencies. That said, this paper covers since the most traditional approaches, for instance texture descriptors such as gray-level co-occurence matrices (GLCM) and Local Binary Patterns (LBP), to more recent approaches such as Convolutional Neural Networks (CNN) and multi-scale patch-based recognition based on encoding approaches such as Fisher Vectors. In addition, we point out relevant references for benchmark datasets, which can help the reader develop and evaluate new methods.
Automatic License Plate Recognition (ALPR) is an important task with many applications in Intelligent Transportation and Surveillance systems. As in other computer vision tasks, Deep Learning (DL) methods have been re...
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ISBN:
(纸本)9781538622193
Automatic License Plate Recognition (ALPR) is an important task with many applications in Intelligent Transportation and Surveillance systems. As in other computer vision tasks, Deep Learning (DL) methods have been recently applied in the context of ALPR, focusing on country-specific plates, such as American or European, Chinese, Indian and Korean. However, either they are not a complete DL-ALPR pipeline, or they are commercial and utilize private datasets and lack detailed information. In this work, we proposed an end-to-end DL-ALPR system for brazilian license plates based on state-of-theart Convolutional Neural Network architectures. Using a publicly available dataset with brazilian plates [1], the system was able to correctly detect and recognize all seven characters of a license plate in 63.18% of the test set, and 97.39% when considering at least five correct characters (partial match). Considering the segmentation and recognition of each character individually, we are able to segment 99% of the characters, and correctly recognize 93% of them.
computergraphics techniques for image generation are living an era where, day after day, the quality of produced content is impressing even the more skeptical viewer. Although it is a great advance for industries lik...
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ISBN:
(纸本)9781538622193
computergraphics techniques for image generation are living an era where, day after day, the quality of produced content is impressing even the more skeptical viewer. Although it is a great advance for industries like games and movies, it can become a real problem when the application of such techniques is applied for the production of fake images. In this paper we propose a new approach for computer generated images detection using a deep convolutional neural network model based on ResNet-50 and transfer learning concepts. Unlike the state-of-the-art approaches, the proposed method is able to classify images between computer generated or photo generated directly from the raw image data with no need for any pre-processing or hand-crafted feature extraction whatsoever. Experiments on a public dataset comprising 9700 images show an accuracy higher than 94%, which is comparable to the literature reported results, without the drawback of laborious and manual step of specialized features extraction and selection.
The image Foresting Transform (IFT) is a general framework to develop imageprocessing tools for a variety of tasks such as image segmentation, boundary tracking, morphological filtering, pixel clustering, among other...
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ISBN:
(纸本)9781538622193
The image Foresting Transform (IFT) is a general framework to develop imageprocessing tools for a variety of tasks such as image segmentation, boundary tracking, morphological filtering, pixel clustering, among others. The Differential image Foresting Transform (DIFT) comes in handy for scenarios where multiple iterations of IFT over the same image with small modifications on the input parameters are expected, reducing the processing complexity from linear to sublinear with respect to the number of pixels. In this paper, we propose an enhanced variant of the DIFT algorithm that avoids inconsistencies, when the connectivity function is not monotonically incremental. Our algorithm works with the classical and non-classifical connectivity functions based on root position. Experiments were conducted on a superpixel task, showing a significant improvement to a state-of-the-art method.
This study proposes a new tool based on Virtual Reality (VR) as a complement in the treatment of people diagnosed with Autism Spectrum Disorder (ASD). VR tools have been stablished in last years as a new option in lea...
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ISBN:
(数字)9781510626065
ISBN:
(纸本)9781510626065
This study proposes a new tool based on Virtual Reality (VR) as a complement in the treatment of people diagnosed with Autism Spectrum Disorder (ASD). VR tools have been stablished in last years as a new option in learning and practising new skills during the treatment. In this work, a VR application is developed simulating several environments corresponding to different types of emotions according to the Gestalt school of psychology. The VR application was tested in five male teenagers diagnosed with ASD of level one according to the DSM-5 during the therapy sessions. A qualitative evaluation of the VR application is carried out by the therapist during the session. It is observed and annotated which emotions have been detonated by the VR application giving to the therapist new information for the subsequent sessions.
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