131 Result(s)
within
Chapter
Ke** Zhang
Computer Communication Networks
Image Processing and Computer Vision
Information Systems Applications (incl. Internet)
Multimedia Information Systems
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Chapter and Conference Paper
In Defense of Fully Connected Layers in Visual Representation Transfer
Pre-trained convolutional neural network (CNN) models have been widely applied in many computer vision tasks, especially in transfer learning tasks. In transfer learning, the target domain may be in a differen...
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Chapter and Conference Paper
Data Reconstruction Based on Supervised Deep Auto-Encoder
Digital media information reconstruction has attracted much attention in machine learning, we propose a new method about this problem for supervising learning by using the classical unsupervised auto-encoders...
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Chapter and Conference Paper
Fast Circular Object Localization and Pose Estimation for Robotic Bin Picking
Detecting and localizing objects in three-dimensional space is essential for robotic manipulation. One practical task is known as “bin-picking”, where a robot manipulator picks objects from a bin of parts with...
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Chapter and Conference Paper
Hybrid Domain Encryption Method of Hyperspectral Remote Sensing Image
With the rapid development of remote sensing technology, hyperspectral remote sensing image as foundation data containing abundant sensitive information has been widely applied in many fields, such as agricult...
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Chapter and Conference Paper
A Dual-CNN Model for Multi-label Classification by Leveraging Co-occurrence Dependencies Between Labels
In recent years, deep convolutional neural network (CNN) has demonstrated its great power in image classification. In real world, there are many images contain abundant contents so that they have multiple labe...
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Chapter and Conference Paper
A Combined Feature Approach for Speaker Segmentation Using Convolution Neural Network
In this paper, a speaker segmentation algorithm is proposed based on a Combined feature approach using the Convolution Neural Network (CNN), which is used to deal with the speaker segmentation problem of dialo...
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Chapter and Conference Paper
Multi-scale Convolutional Neural Networks for Non-blind Image Deconvolution
Image deconvolution appears in many image-related problems. Previous works tried to train neural networks directly on blurry/clean pairs to restore clean images but failed. In this work, we propose a novel neu...
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Chapter and Conference Paper
An Obstacle Detection Method Based on Binocular Stereovision
As the main tasks of Advance Driver Assistance Systems (ADAS), obstacle detection has attracted extensive attention. Traditional obstacle detection methods on the basis of monocular vision will lose its effect...
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Chapter and Conference Paper
Integrating Visual Word Embeddings into Translation Language Model for Keyword Spotting on Historical Mongolian Document Images
In Bag-of-Visual-Words (BoVW) framework, there is lacking of the semantic relatedness between visual words. Therefore, a visual word embeddings approach has been proposed in this paper, which is similar to the...
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Chapter and Conference Paper
Robust Local Effective Matching Model for Multi-target Tracking
Occlusion is one of the main challenges in multi-target tracking, which causes fragments in tracking. In order to handle with fragments, various motion models were proposed. However, motion model has limited e...
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Chapter and Conference Paper
CRF-Based Reconstruction from Narrow-Baseline Image Sequences
Given an image sequence of a scene it is possible to recover a depth map. Though multiview stereo algorithms are well-studied, rarely are those algorithms considered in the context of narrow baseline. In this...
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Chapter and Conference Paper
Chinese Characters Recognition from Screen-Rendered Images Using Inception Deep Learning Architecture
Text recognition from images can substantially facilitate a wide range of applications. However, screen-rendered images pose great challenges to current methods due to its low resolution and low signal to noi...
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Chapter and Conference Paper
An Adaptive Tuning Sparse Fast Fourier Transform
The Sparse Fast Fourier Transform (SFFT) is a novel algorithm for discrete Fourier transforms on signals with the sparsity in frequency domain. A reference implementation of the algorithm has been proven to be...
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Chapter and Conference Paper
Automatic Foreground Seeds Discovery for Robust Video Saliency Detection
In this paper, we propose a novel algorithm for saliency object detection in unconstrained videos. Even though various methods have been proposed to solve this task, video saliency detection is still challengi...
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Chapter and Conference Paper
Mini Neural Networks for Effective and Efficient Mobile Album Organization
In this paper, we present an auto mobile album organization system, which can automatically classify daily photos in mobile devices into six daily categories, e.g., Baby, Food, Party, Scenery, Selfie, and Sport. ...
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Chapter and Conference Paper
Joint Dictionary Learning via Split Bregman Iteration for Large-Scale Image Classification
This paper aims at the hierarchical learning for large-scale image classification. Due to flexibility and capability, sparse representation is widely used in object recognition. The hierarchy is introduced to...
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Chapter and Conference Paper
AWCR: Adaptive and Weighted Collaborative Representations for Face Super-Resolution with Context Residual-Learning
Owing to the ill-posed nature of the image super-resolution (SR) problem, learning-based approaches typically employ regularization terms in the representation. Current local-patch based face SR approaches wei...
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Chapter and Conference Paper
The Intelligent Monitoring for the Elderly Based on WiFi Signals
With the increasing aging population, the intelligent monitoring for the elderly living alone has become a hot research topic. As the universal of WiFi, the intelligent monitoring system based on WiFi Channel...
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Chapter and Conference Paper
Single Image Dehazing Using Deep Convolution Neural Networks
Haze removal is urgently desired in multi-media system. A deep learning-based method, called dehazingCNN, is proposed to estimate an approximate clear image. The proposed learning model is different from tradi...
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Chapter and Conference Paper
Attention Window Aware Encoder-Decoder Model for Spoken Language Understanding
Slot filling task, which aims to predict the semantic slot labels for each specific word in word sequence, is one of the main tasks in Spoken Language Understanding (SLU). In this paper, we propose a variation...
