Elderly health monitoring is important nowadays. This paper proposes to extract 3D skeleton for elderly for monitoring their daily behavior (such as walk, fall-down, etc.). Our technique uses two "Fully Convoluti...
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ISBN:
(纸本)9781538692141
Elderly health monitoring is important nowadays. This paper proposes to extract 3D skeleton for elderly for monitoring their daily behavior (such as walk, fall-down, etc.). Our technique uses two "Fully Convolutional Networks" (FCNs) to estimate 3D Human pose (skeleton) from the corresponding 2D pose (skeleton), which can be estimated from a single RGB-image. Our FCNs contain two-stages: the 1ststage is to estimate a 3D Anchor Pose (i.e., the most similar and frequently occurring pose in the dataset) from the 2D skeleton, while the 2nd stage is to further regress/refine the 3D anchor pose to its final state (parameters). We also apply global or object-centered normalization methods as a pre-processingstep so as to be applicable when cameras of different FOV (Field of View), focal length, or object distances, are encountered. According to experiments, our two-stage FCNs are capable of achieving an MPJPE (Mean per joint position error) of 38.84 mm (better than other methods in comparison, 45.5 mm) when a 2D ground truth pose is used as the input. When cascaded with other 2D pose estimator (e.g., stacked Hourglass model), the average MPJPE is about 67.71 mm. Tis can be further improved if a 17-joints human skeleton model is adopted and re-trained based on the dataset H36M.
Brain functional connectivity (FC) derived from resting-state functional MRI (rs-fMRI) data has become a powerful approach to measure and map brain activity. Using fMRI data, graph convolutional network (GCN) has rece...
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Facial attractiveness classification application has many various usabilities, including photo editing, photo beautification, photo grading and dataset labeling. While face attractiveness classification seems to be re...
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ISBN:
(纸本)9781538694220
Facial attractiveness classification application has many various usabilities, including photo editing, photo beautification, photo grading and dataset labeling. While face attractiveness classification seems to be related to personal preference, building a robust attractiveness classifier is not impossible. There are several studies that have developed a classification system of facial attractiveness using a convolutional neural network and provide satisfactory results. The use of image-net pre-trained convolutional neural network has been largely used by face-related research, yet none of them are related to facial attractiveness. This study aims to compare the famous deep learning architecture, such as VGG, Inception, and ResNet models. This research also aims to analyze the effect of using the Viola-Jones algorithm, as a preprocessing method, to the classification result of the built model. Viola-Jones algorithm will detect faces in image data, and 2 types of cropping will be done to extract the face region from the image, namely loose-crop and tight-crop. This research produces the highest accuracy value of 82.52% by using ResNet50 model and loose-crop preprocessing method.
In fMRI analysis, the scientist seeks to aggregate multi-subject fMRI data so that inferences shared across subjects can be achieved. The challenge is to eliminate the variability of anatomical structure and functiona...
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The Ministry of Marine Affairs and Fisheries (MMAF) carry out the responsibilities and features associated with the policy of marine and fisheries. MMAF carry out the responsibilities and features associated with mari...
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ISBN:
(纸本)9781538694220
The Ministry of Marine Affairs and Fisheries (MMAF) carry out the responsibilities and features associated with the policy of marine and fisheries. MMAF carry out the responsibilities and features associated with marine and fisheries policy. One of their duties and functions are organized marine and fishery statistics, in accordance to Law of The Republic of Indonesia Number sixteen of 1997 regarding statistics. The main problem is the number of enumerators at each Basis Landing Of Fish. This paper proposed a fish classification on fish images using transfer learning and Matlab as the firststage of tackling the problem. FishNet is a modification from AlexNet to classify Katsuwonus Pelamis (Skipjack tuna or Cakalang), Euthynnus Affinis (Tongkol) and Coryphaena Hippurus (Mahi-mahi) that caught by fishermen. There are 15.120 images of 3 type of fishes, 5.040 for each fish. Data is split into 70: 30 for training and validation set. The training process is done using Matlab 2018a on Windows 7 operating system in a notebook with single CPU i7 with 8 GB RAM for 124 minutes. The validation accuracy is 99.63%.
Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays an crucial role in clinical routines. ...
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ISBN:
(纸本)9781538637883
Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays an crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a chanllenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize on fully-conventional imageprocessing and novel machinelearning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.
Human body detection is a key technology in the fields of biometric recognition, and the detection in a depth image is rather challenging due to serious noise effects and lack of texture information. For addressing th...
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ISBN:
(纸本)9783030033354;9783030033347
Human body detection is a key technology in the fields of biometric recognition, and the detection in a depth image is rather challenging due to serious noise effects and lack of texture information. For addressing this issue, we propose the feature visualization based stacked convolutional neural network (FV-SCNN), which can be trained by a two-layer unsupervised learning. Specifically, the next CNN layer is obtained by optimizing a sparse auto-encoder (SAE) on the reconstructed visualization of the former to capture robust high-level features. Experiments on SZU Depth Pedestrian dataset verify that the proposed method can achieve favorable accuracy for body detection. The key of our method is that the CNN-based feature visualization actually pursues a data-driven processing for a depth map, and significantly alleviates the influences of noise and corruptions on body detection.
Deep neural networks are one of the most important branches of machinelearning that have been recently used in many fields of patternrecognition and machine vision applications successfully. One of the most famous n...
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ISBN:
(纸本)9781538695692
Deep neural networks are one of the most important branches of machinelearning that have been recently used in many fields of patternrecognition and machine vision applications successfully. One of the most famous networks in this area is convolutional neural networks which are biologically inspired variants of multi-layer perceptions. In these networks, activation function plays a significant role especially when the data come in different scales. Recently, there is an interest to adaptive activation functions which adapts their parameters to the input data during network training process. Therefore, in this paper, inspired from a successful convolutional neural network tuned for medical image classification, we have investigated the effect of applying adaptive activation functions in a modified convolutional network by combining basic activation functions in linear (mixed) and nonlinear (gated) ways. The effectiveness of using these adaptive functions is shown on a CT brain images dataset (as a complex medical dataset) and the well-known MN 1st hand-written digits dataset. The done experiments show that the classification accuracy of the proposed network with adaptive activation functions is higher compared to the ones using basic activation functions.
Inscriptions are major resources for studying the ancient history and culture of civilization in any country. Analyzing, recognizing and translating the ancient letters (Brahmi letters) from the inscription is a very ...
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ISBN:
(数字)9781728141701
ISBN:
(纸本)9781728141718
Inscriptions are major resources for studying the ancient history and culture of civilization in any country. Analyzing, recognizing and translating the ancient letters (Brahmi letters) from the inscription is a very difficult work for present generation. There is no any automatic system for translating Brahmi letters to Sinhala language. However, they are using manual method for translating inscriptions. The method that used in epigraphy is being taken a long period to decipher, analyze and translate the inscribed text in inscriptions. This research mainly focuses on recognition of ancient Brahmi characters written the time period between 3 rd B.C and 1 st A. D. First, we remove the noise, segment the letters from the inscription image and convert it into the binary image using imageprocessing techniques. Secondly, we recognize the correct Brahmi letters, broken letters and then identify the time period of the inscriptions using Convolution Neural Networks in deep learning. Finally, the Brahmi letters are translated into modern Sinhala letters and provide the meaning of the inscription using Natural Language processing. This proposed system builds up solution to overcome the existing problems in epigraphy.
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