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Tracking-by-detection methods treat the target location as a classification problem in which the approach SVM + HOG shows a good performance. However, training a good SVM classifier is cost expensive. In this paper, we replace SVM by linear discriminant analysis (LDA) for classification where the mean and covariance of negative examples are evaluated only once. Not only the training is much cheaper,...
Neural networks have attracted significant interest in recent years due to their exceptional performance in various domains ranging from natural language processing to image identification and classification. Modern deep neural networks demonstrate state-of-the-art results in complex tasks such as epileptic seizure detection [1] and time series classification [2]. The internal architecture of these...
The problem of object localization in image appear ubiquitously in computer vision applications including image classification, object detection and visual tracking. Recently, it is shown that multiple-instance learning(MIL) which is regarded as the fourth machine learning framework compared with supervised learning, unsupervised learning and reinforce learning has been verified that will get good...
A comprehensive Arabic handwritten text database is an important resource for Arabic handwritten text recognition research. It is essential for training text recognition algorithms and vital for evaluating the performance of these algorithms. In this paper, we present a database that includes manuscripts from the Islamic heritage project (IHP), consisting of 333 historical manuscripts written by 302...
Planar spoofing is a well researched problem, wherein a high quality planar photograph can be replayed in front of a still camera as a substitute for another individual's face. Most modern day face recognition systems can be fooled by this process, as the perceptual information contained in a photo-of-a-photo, is virtually the same as that of a natural photograph of an individual. Current solutions...
The Eigenface method is a classic face recognition method. This article is based on the method of Eigen face to recognize the facial expression. The aim of this method is to recognize the facial expression stored in a database. It uses a set of single static image with different expression labels as the training database, projected the training image to subspaces. The similar face of the tested expression...
Inappropriate medication use such as wrong drug or wrong dose intake can be harmful to patients. In this work we present a method to automatically identify a pill from a single image using Convolutional Neural Network (CNN). We first localize the pill in the image by detecting the region with the highest concentration of edges. To overcome the challenge of minimal labeled training data and domain...
This paper proposes a semantic content analysis framework for reliable video event detection. In this work, we target to improve the concept detection results by feeding the learnt results from individual shallow learning models into a generic model to dig out of the similarities in deeper layers. Compared to the deep learning models, the shallow learning models are memorizing rather than understanding...
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image. In this paper, we propose a system for CRC tissue classification based on convolutional networks (ConvNets). We investigate the importance of stain...
With the prosperous development of the Internet, virtual identity is becoming more important to people in social life. As one key factor, attractiveness, is a significant part of virtual identity, where there is still room for different strategies to improve attractiveness. In this study, we focus on digital face image and make it a little bit smiling as a common way to improve attractiveness. The...
One of the main challenges of histological image analysis is the high dimensionality of the images. This can be addressed via summarizing techniques or feature engineering. However, such approaches can limit the performance of subsequent machine learning models, particularly when dealing with highly heterogeneous tissue samples. One possible alternative is to employ unsupervised learning to determine...
Wireless Capsule Endoscopy (WCE) is a novel diagnostic modality of endoscopic imaging which facilitates direct visualization of the gastrointestinal (GI) tract. Many computational methods that can automatically detect and/or characterize the abnormalities from WCE sequences are developed to support medical decision-making. This paper presents a new approach for automated segmentation of blood regions...
In the field of pathological image analysis based on machine learning, the generation of appropriate training data set is significant but difficult. As a solution, this paper addresses a novel unsupervised region proposal method for histopathological whole slide image based on Selective Search. Specifically, the method utilizes multiple magnifications, modifies the similarity measure for grouping...
In wireless capsule endoscopy (WCE), a swallowable miniature optical endoscope is used to transmit color images of the gastrointestinal tract. However, the number of images transmitted is large, taking a significant amount of the medical expert's time to review the scan. In this paper, we propose a technique to automate the abnormality detection in WCE images. We split the image into several patches...
Tongue manifestation is one of the most significant basic criteria for the diagnosis of Traditional Chinese Medicine (TCM). And tongue color recognition with high accuracy will contribute to the efficiency of TCM diagnosis. The drawbacks of traditional tongue diagnosis methods are that the features need to be designed artificially. While the feature acquisition from the deep learning is a process...
Tongue coating nature inspection is an essential part in the tongue diagnosis of Traditional Chinese Medicine (TCM). However, it has been depending on doctors' visual judgment. Although many researches have been done in this field, the issue remains challenging. The approaches are limited to image processing or shallow neural networks. In this paper, we propose to computerize tongue coating nature...
Object category and instance recognition have received much attention in this era of modern technologies. Advanced image sensing technologies provide high resolution color and depth synchronized videos such as RGB-D (Kinect style) camera. At present, various features extraction schemes are introduced to improve classification performance. Extracting useful features from both color and depth images...
Person re-identification is a critical yet challenging task in video surveillance which intends to match people over non-overlapping cameras. Most metric learning algorithms for person re-identification use symmetric matrix to project feature vectors into the same subspace to compute the similarity while ignoring the discrepancy between views. To solve this problem, we proposed an asymmetric cross-view...
Since the quality of depth maps produced by Time-of-Flight (TOF) cameras is low, color-guided recovery methods have been proposed to increase spatial resolution and suppress unwanted noise. Despite successful applications of deep neural networks in color image super-resolution (SR), their potential for depth map SR is largely unknown. In this paper, we present a deep neural network architecture to...
Conventional unsupervised image segmentation methods use color and geometric information and apply clustering algorithms over pixels. They preserve object boundaries well but often suffer from over-segmentation due to noise and artifacts in the images. In this paper, we contribute on a preprocessing step for image smoothing, which alleviates the burden of conventional unsupervised image segmentation...
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