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Tensor linear discriminant analysis (LDA) is an effective feature extraction method for images, but it just considers the globally discriminative information of the data and neglects to preserve the local structure. In this paper, we propose a feature extraction approach based on tensor globally and locally discriminative information preserving projections for SAR target configuration recognition...
As an important component of the driver assistance system or autonomous vehicle, traffic-sign recognition can provide drivers or vehicles with safety and alert information about the road. This paper proposes a new method for the task of traffic-sign recognition by employing extreme learning machine (ELM) whose infrastructure is a single-hidden-layer feed-forward network. This method includes two stages:...
In this paper a proposed method for gesture recognition using depth map image is presented. Three different feature extraction methods are presented too. All of them are based on Radon transform. First method reduces the feature vector of Radon transform by averaging its values. Next approach uses discrete cosine transform on radon graph, thus the energy of graph is reorganized. This approach concentrates...
This paper proposes a robust hand posture recognition system based on RGBD images. While much research has focused on human body posture recognition, this work investigates skeleton-free hand detection, tracking and posture recognition. This work consists of two different parts. In the first part, we utilize random forest to get pixel detection of hand and mean-shift to track hand based on RGBD images...
This paper presents an approach for automatic recognition of vehicle make from its logo in a front-view image using SIFT descriptor of interior structure and back-propagation neural network. The proposed method focuses on recognition of automobile make by integrating Top-Hat transformation with shape descriptor to locate the logo of an automobile from an image then uses back-propagation neural network...
In this paper, a complete logo detection/ recognition system for document images is proposed. In the proposed system, first, a logo detection method is employed to detect a few regions of interest (logo-patches), which likely contain the logo(s), in a document image. The detection method is based on the piece-wise painting algorithm (PPA) and some probability features along with a decision tree. For...
Sign language provides hearing and speech impaired people with an interface to communicate with society. Unfortunately most people do not understand sign language. For this, image processing and pattern recognition can provide with a vital tool to detect and translate sign language into vocal language. This work presents a method for detecting, understanding and translating sign language gestures...
Facial Expression analysis is an interesting and challenging problem and has applications in many areas such as human computer interaction and robotics. Deriving an effective facial representation from original face images is an important step for successful facial expression recognition. In this paper, we are evaluating 2DPCA and LBP+2DPCA for facial representation. The three stages of facial expression...
Biometric systems are widely used for accurate personal identification for access control. Unimodal systems have been well-developed and are being used extensively in different institutions, organizations and in industries. However, these systems are only capable to provide low to middle range of security feature. Thus, for enhancing security feature, the combination of two or more unimodal biometric...
Signature recognition is an important requirement of automatic document verification system. Many approaches for signature recognition are found in literature. A novel approach for offline signature recognition system is presented in this paper, which is based on powerful global and local wavelet features (Energy features). The proposed system functions in three stages. Pre-processing stage, which...
Human action recognition is very important in human computer interaction. In this article, we present a new method of recognizing human actions by using Microsoft Kinect sensor, k-means clustering and Hidden Markov Models (HMMs). Kinect is able to generate human skeleton information from depth images, in addition, features representing specific body parts are generated from the skeleton information...
Information describing the materials that make up scene constituents provides invaluable context that can lead to a better understanding of images. We would like to obtain such material information at every pixel, in arbitrary images, regardless of the objects involved. In this paper, we introduce visual material traits to achieve this. Material traits, such as "shiny," or "woven,"...
'Hubness' is a recently discovered general problem of machine learning in high dimensional data spaces. Hub objects have a small distance to an exceptionally large number of data points, and anti-hubs are far from all other data points. It is related to the concentration of distances which impairs the contrast of distances in high dimensional spaces. Computation of secondary distances inspired by...
This paper proposes a new approach to star image denoising, recognizing and centroiding for the airborne application, especially during the daytime. To extend attitude determination of aircraft to daytime, one prerequisite is to precisely obtain the centroid of the target star. To date, there has not been an adequate analytical model and experimental method to solve this problem effectively. Generally,...
This paper presents a simple strategy for perception-action of robots in indoor environments using Hierarchical Temporal Memory which is the theory of modeling the rationale of the neocortex. The main idea of the present study is that the input of the HTM network is images of objects that robot perceives in environment, and the output of HTM network is action, such as moving along the wall, moving...
Artificial touch sensing system for various Human Computer Interaction (HCI) applications is required to be capable of recognizing various parameters viz. object shape, size, texture and surface. However, only identifying object-shapes is not sufficient for object recognition. It is necessary to distinguish the object shapes according to their dimensions or sizes. Thus in the present work object shapes...
This paper quantifies existing techniques for feature detection in human action recognition. Four different feature detection approaches are investigated using Motion SIFT descriptor, a standard bag-of-features SVM classifier with x2 kernel. Specifically we used two popular feature detectors; Motion SIFT (MOSIFT) and Motion FAST (MOFAST) with and without Statis interest points. The system was tested...
The problem of plant seed identification is important for agricultural sector, such as maintaining seed quality and to prevent the spreading of weed species. Seed identification is currently performed by a human seed analyst; human must often search through many seed images before finding the desired seed. This process of manual identification is slow and posses a degree of subjectivity which is hard...
A hand shape recognition system was designed. In the system the ring lamp and blue background was used to improve the accuracy of the collected hand shape images. The closed box without retainer was designed to make the system more convenient and easier to carry. A novel algorithm based on the minimum match difference of the finger contour was proposed in the paper. Based on coordinate transformation,...
The Local Binary Patterns (LBP) feature extraction method is a theoretically and computationally simple and efficient methodology for texture analysis. The LBP operator is used in many applications such as facial expression recognition and face recognition. The original LBP is based on hard thresholding the neighborhood of each pixel, which makes texture representation sensitive to noise. In addition,...
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