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In this paper we propose a publicly available static hand pose database called OUHANDS and protocols for training and evaluating hand pose classification and hand detection methods. A comparison between the OUHANDS database and existing databases is given. Baseline results for both of the protocols are presented.
In this paper, we present a novel method for human action recognition using covariance features. Computationally efficient action features are extracted from the skeleton of the subject performing the action. They aim to capture relative positions of the joints and motion over time. These features are encoded into a compact representation using a covariance matrix. We evaluate the performance of the...
This paper proposes a novel patient-specific approach to channel selection and seizure detection based on estimating the histograms of multi-channel scalp electroencephalography (sEEG) signals. It consists of two main phases: training and testing. In the training phase, the signal is segmented into non-overlapping 10-second segments, with five histograms estimated for each segment. These histograms...
Physical measurement have been becoming increasingly helpful in monitoring the humans health status. Manual measurement of physical status is time consuming and may result in misdiagnosing, so an automatic method for identification the status of physical is urgently needed. This paper presents a novel feature extraction method based on using constrained high dispersal network for depth images and...
Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminate radio frequency interference or other noises. Recently, machine learning methods are adopted to automate this task, which saves human labor and makes it possible for processing millions...
In case when higher-order statistic is used for local feature aggregation, final descriptor can have very high dimensionality. In this paper different methods for descriptor dimensionality reduction are evaluated for land-use classification. Concretely, aerial image classification accuracy is compared for the cases when dimensionality reduction is made per band with fixed and variable sizes. For both...
This paper proposed a new Vehicle Make Recognition (VMR) method using the PCANet features extracted from vehicle front view images. The PCANet architecture processes every input vehicle image through only three very simple data processing components: cascaded principle component analysis (PCA), binary hashing, and block-wise histograms, and generates a sparse vector as the feature representation....
In recent years, the IoT application and the biometric-based authorization become popular. This paper proposes a face recognition system with high accuracy rate based on extended Local Binary Pattern, and applies it as an access control system on an IoT device which is always low-cost, low-power and small-footprint. The proposed face recognition system includes three parts, face detection, feature...
The paper presents a unique combination of texture feature extraction techniques which can be used in image texture analysis. Setting the prime objective of classifying different texture images, the Local Binary Pattern (LBP) and a modified form of Gray Level Run Length Matrix (GLRLM) are implemented initially. The next phase involves use of combination of the former two methods to extract improved...
People re-identification is a difficult problem in non-overlapping video surveillance, because pedestrian images contain variations in view angle, lighting, background clutter and occlusion. This paper presents an approach for person re-identification in surveillance system by taking pedestrian sequence (set) as processing element. The distance metric is learnt from relative and irrelative set-to-set...
Microcalcifications are very tiny deposits of calcium allocated in the breast tissue. Their gray level is similar to the dense normal breast tissue so its very difficult to differentiate between them. Once detected, its very difficult to between malign end benign microcalcifications. In this paper, we apply a new method to extract features of microcalcifications in order to classify them into malign...
Skin detection in computer vision is the basis of many novel human-computer interface applications. The goal of skin detection is to accurately highlight skin pixels in an input image, while discarding all non-skin pixels. This makes it possible to accurately locate regions pertaining to a user in an input frame. A number of approaches to skin detection have been proposed over a number of years, with...
Feature extraction and classification algorithm is the key to classification accuracy. Terrain recognition for off-road robot need higher real-time classification algorithm, while the traditional neural network training method is difficult to meet the requirements. Extreme learning machine is used to classify the terrain pictures collected by robot in real time. Experimental results show that the...
This paper addresses a new approach based on the Weber-face and singular value decomposition (SVD) methods to improve the recognition accuracy for a face recognition system using local binary patterns and local ternary patterns in an illumination variation environment. The face images are the first extracted illumination-invariant components by the Weber-face method. Secondly, SVD is applied to the...
In this paper, two neural network based methods were implemented for recognition of images showing 10 hand gestures. Images were available from 24 subjects and captured on two different backgrounds and with several space orientations. Firstly, Histogram of Oriented Gradients method was applied for feature extraction and training was performed with multilayer feed forward neural network with back propagation...
This paper investigates the robustness of two state-of-theart action recognition algorithms: a pixel domain approach based on 3D convolutional neural networks (C3D) and a compressed domain approach requiring only partial decoding of the video, based on feature description using motion vectors and Fisher vector encoding (MV-FV). We study the robustness of the two algorithms against: (i) quality variations,...
State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To reduce this dependency, we introduce a novel architecture called Iterative Hough Forest with Histogram of Control Points that is capable of estimating occluded and cluttered objects' 6D pose given a candidate 2D bounding box. Our Iterative Hough Forest...
Standards-based grading (SBG) provides students with feedback on their achievement of specific course learning objectives. What training students need to make meaning of and plan to act on this feedback is not known. This work in progress took place in a required first-year engineering course in which a SBG system is used. Following two problem, students were asked to respond to reflection prompts...
Fusing multiple features within one biometric modality has attracted increasing attention and interest among researchers during recent decades because the concept is useful in addressing a wide range of real world problems. In this paper, we propose a novel fusion approach that combines two feature extraction algorithms: Local Binary Pattern Histogram Fourier Features (LBP-HF) and Gabor filter technique...
Recently, several effective features were proposed for person re-identification, such as Weight Histograms of Overlapping Stripes (WHOS) and Local Maximal Occurrence (LOMO), but it still need to explore new effective feature to improve the precision for person re-identification. So, in this paper, we proposed a new Dual Channel Gradient feature, which can be fused with WHOS and LOMO by directly concatenating...
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