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Digital image processing techniques are commonly employed for food classification in an industrial environment. In this paper, we propose the use of supervised learning methods, namely multi-class support vector machines and artificial neural networks to perform classification of different type of almonds. In the process of defining the feature vectors, the proposed method has relied on the principal...
A spatial load forecasting method based on unstructured information processing and multi — attribute depth learning is proposed. In order to solve the problem that the unstructured attributes have great influence on the load density but can't be directly put into calculation, the natural language processing (NLP) technique is used to structure those attributes. In view of the on characterization of...
The study of network features is an important analysis method for the social networks, and prediction of network features is a research problem with many applications, particularly in decision making. In this paper, we propose a novel feature prediction method for temporal social networks, which estimates network measurements in the future based on a small window of measurements in the past. We utilized...
Traditional password based authentication has been proven inadequate and the use of biometrics have provided multiple solutions through the past years. One of the most recent approaches to biometric authentication is using Electrocardiograms (ECG), as they are closely related to unique characteristics of the heart of each person. In this paper a framework for efficient and usable user authentication,...
As the human eye on the image of different regions of the contrast sensitivity is different, it is particularly important to segment the image region more accurately in the image quality evaluation. Based on this, this paper presents a non-reference image region division method based on deep learning. Firstly, the Canny operator performs image edge detection at low threshold to obtain the strong edge...
Confidence measures aim at detecting unreliable depth measurements and play an important role for many purposes and in particular, as recently shown, to improve stereo accuracy. This topic has been thoroughly investigated by Hu and Mordohai in 2010 (and 2012) considering 17 confidence measures and two local algorithms on the two datasets available at that time. However, since then major breakthroughs...
Convolutional neural networks showed the ability in stereo matching cost learning. Recent approaches learned parameters from public datasets that have ground truth disparity maps. Due to the difficulty of labeling ground truth depth, usable data for system training is rather limited, making it difficult to apply the system to real applications. In this paper, we present a framework for learning stereo...
Recent studies have proposed a model using the supervised neural-network (NN) as the haptic model. However, this model has the problem in expressing human tactile sense. Therefore, we constructed a haptic stage model based on the model of human-tactile sense and cognitive process. Our NN-based model consists of supervised and unsupervised stages. Using surface-scanning results from our trial micro-tactile...
Encoding spatio-temporally varying textures is challenging for standardised video encoders, with significantly more bits required for textured blocks compared to non-textured blocks. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimize coding modes and performance. To this end, we examine the...
It is a simple task for humans to visually identify objects. However, computer-based image recognition remains challenging. In this paper we describe an approach for image recognition with specific focus on automated recognition of plants and flowers. The approach taken utilizes deep learning capabilities and unlike other approaches that focus on static images for feature classification, we utilize...
High resolution satellite imagery is a growing source of data with potential applications in many diverse domains. Efficient large scale analysis of this rich data can lead to unprecedented discoveries with societal impact. We present a new framework for organizing collections of satellite images into demographically relevant categories using unsupervised learning techniques. Our framework first extracts...
Target discrimination in wireless sensor networks remains challenging when sensors have structured electronic noise and deployment settings have variable in-situ clutter. Datadriven learning of discrimination functions is especially hard when deployment sites are remote or hazardous, necessitating reliance on surrogate environments for data collection. The challenge is exacerbated if sensors are resource...
Sign language is important since it permits insight into the deaf culture and allows more opportunities to communicate with those who are deaf or hard of hearing. In this paper, we show that Wi-Fi signals can be used to recognize sign language with sparsely labeled training dataset. The key intuition is that sign language introduces different multi-path distortions in Wi-Fi signals and generates different...
New and unseen network attacks pose a great threat to the signature-based detection systems. Consequently, machine learning-based approaches are designed to detect attacks, which rely on features extracted from network data. The problem is caused by different distribution of features in the training and testing datasets, which affects the performance of the learned models. Moreover, generating labeled...
Automated screening of diabetic retinopathy plays an important role in diagnosis of the disease in early stages and preventing blindness in patients with diabetes. Various machine learning approaches have been studied in literature with the purpose of improving the accuracy of the screening methods. Although the performance of the machine learning algorithm depends on the application and the type...
With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective...
Because of the worldwide aging population, more and more elders suffer from dementia. Nowadays, it is inconvenient and time-consuming for doctors to diagnose whether elders who live independently have dementia because lots of diagnostic questions on a checklist must be asked first, and part of them even require a long-term observation. In order to help doctors and make this diagnostic process easier,...
Partial observation can be avoided by extracting both modality specific features and common features from multimodal data. This paper proposes a framework of parameter shared multimodal deep autoencoders which uses complemental multimodal data in order to learn both modality specific and common features. The proposed model shares parameters of networks for each modality, while conventional multimodal...
Action recognition is still a challenging problem. In order to catch effective compact representation of the action sequences, the discriminative dictionaries could be learned by sparse coding. But sparse coding is needed in both the training and testing phases of the classifier framework. And it is also time consuming for the adoption of 1-norm sparsity constraint on the representation coefficients...
In this work, we adopt the use of deep learning method for no-reference image quality assessment. With the development of deep neural networks technology, foundational and deep features of images could be captured without much prior knowledge. So a sparse autoencoder (SAE) was trained to express a 32 × 32 pixels image into a feature vector. Then the original images were cut into serial sub-images...
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