This paper presents a low-complexity algorithm of non-intrusive real time eye-tracking by a single camera. The algorithm employs the feature-based approach, scanning a seven-segment rectangular filter over the input i...
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ISBN:
(纸本)9781467328210;9781467328203
This paper presents a low-complexity algorithm of non-intrusive real time eye-tracking by a single camera. The algorithm employs the feature-based approach, scanning a seven-segment rectangular filter over the input image to find the Between-The-Eyes pattern of human face and locating eyes within the pattern. Unlike related methods it neither uses computationally expensive cascades of Adaboost classifiers nor SVM template matching, while detecting eye with high accuracy. We implemented the algorithm and present the results of its experimental evaluation.
Spatial domain based super-resolution reconstruction techniques, which were designed for uncompressed video to produce high-resolution image or image sequences, may not work well when applied directly to compressed vi...
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ISBN:
(纸本)9781424410651
Spatial domain based super-resolution reconstruction techniques, which were designed for uncompressed video to produce high-resolution image or image sequences, may not work well when applied directly to compressed videos, especially to those with severe quantization errors. Here a reconstruction approach designed for videos or image sequences, which were compressed using DWT-based techniques, is presented. This method utilizes the theory of projection onto convex sets (POCS) with a new projection operator based on Discrete Wavelet Transformation (DWT) for reducing blurring artifacts. It also applies maximum a posteriori (MAP) estimation techniques to remove ringing noises from restored images. Experimental results show that such approach could effectively recover objects' edges and details that were blurred during compressing process while, expanding image's, size simultaneously.
Data pre-processing is the foremoststep employed in building any machinelearning (ML) model. It has a significant effect on the generalization performance of the model. In the present study, we have attempted to pre...
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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.
Development of a computer-aided diagnosis (CAD) system for early detection of leukemia is very essential for the betterment of medical purpose. In recent years, a variety of CAD system has been proposed for the detect...
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ISBN:
(纸本)9789811021046;9789811021039
Development of a computer-aided diagnosis (CAD) system for early detection of leukemia is very essential for the betterment of medical purpose. In recent years, a variety of CAD system has been proposed for the detection of leukemia. Acute leukemia is a malignant neoplastic disorder that influences a larger fraction of world population. In modern medical science, there are sufficient newly formulated methodologies for the early detection of leukemia. Such advanced technologies include medical imageprocessing methods for the detection of the syndrome. This paper shows that use of a highly appropriate feature extraction technique is required for the classification of a disease. In the field of imageprocessing and machinelearning approach, Discrete Cosine Transform (DCT) is a well-known technique. Nucleus features are extracted from the RGB image. The proposed method provides an opportunity to fine-tune the accuracy for the detection of the disease. Experimental results using publicly available dataset like ALL-IDB shows the superiority of the proposed method with SVM classifier comparing it with some other standard classifiers.
The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it shoul...
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As a kind of unsupervised learning model, the autoencoder is usually adopted to perform the pretraining to obtain the optimal initial value of parameter space, so as to avoid the local minimality that the nonconvex pr...
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ISBN:
(纸本)9783030033378
As a kind of unsupervised learning model, the autoencoder is usually adopted to perform the pretraining to obtain the optimal initial value of parameter space, so as to avoid the local minimality that the nonconvex problem may fall into and gradient vanishment of the process of back propagation. However, the autoencoder and its variants have not taken the statistical characteristics and domain knowledge of the train set and also lost plenty of essential representaions learned from different levels when it comes to imageprocessing and computer vision. In this article, we firstly add a sparsity-induced layer into the autoencoder to exploit and extract more representative and essential features exist in the input and then combining the ensemble learning mechanism, we propose a novel sparse feature ensemble learning method, named Boosting sparsity-induced autoencoder, which could make full use of hierarchical and diverse features, increase the accuracy and the stability of a single model. The classification results on different data sets illustrated the effectiveness of our proposed method.
The use of a genetic algorithm to select the optimal structuring element was discussed. An optimal structuring element extraction for mathematical morphology signature transform (Mst) based shapes was determined. The ...
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The use of a genetic algorithm to select the optimal structuring element was discussed. An optimal structuring element extraction for mathematical morphology signature transform (Mst) based shapes was determined. The new method was found to have the ability to find the global optimum, keeping the advantages of genetic algorithms, and tabu search. A brief description of the Mst, its shape, and the application of the structuring element were also discussed.
The Agriculture plays a major part in any nation for economy through the production of various crops and it's an essential source of income in India. The plant disease is one of the leading task of agriculture. Wh...
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