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In this paper, a new action feature descriptor PEM (PCRM-EOH-MOH) is proposed for fast human action recognition. This descriptor is constructed based on three information channels: Pixel Change Ratio Map (PCRM), Edge Orientation Histogram (EOH) and Motion Orientation Histogram (MOH) features. A video sequence is first represented as a collection of PEM features. Then, video representations are constructed...
Color is one of the most important descriptor in image processing. Color histogram is the most commonly used color feature and has proved to be stable representation of an image, but it might be similar in different kinds of images because it describe the global intensity distribution of images. Inspired by human image classification behavior, in this paper, a new color feature representation method...
In this paper, we investigate an important but understudied problem, gender classification from human gaits. And we have proved the ability of using GEI (Gait Energy Image) as a representation of human gait for arbitrary view angles. Using GEI as a discriminative feature, we construct angle classifiers and gender classifiers from different approaches. Experiments show that our system achieved a good...
For fast and robust car/human classification, novel features, ELR and DELR, using triple directional edge property of objects and an efficient method based on two relations are proposed. The proposed feature is considerably less sensitive to distance, occlusion, and the existence of groups than other existing features. The proposed method using the temporal and spatial relation complements the temporary...
This paper describes a spectroscopic approach for hyperspectral imaging of Plasmodium Falciparum infected human red blood cells (RBCs). We have performed a broad-band hyperspectral microscope which has been used to acquire a number of images in 370 nm to 1100 nm range, from fresh human RBCs infected by Plasmodium Falciparum. These images have been analyzed using the fast computation of entropies and...
In this paper, a fusion of facial symmetry information method is developed for improving two-dimensional principal component analysis. The proposed method uses the characteristic of facial symmetry to generate odd-even symmetry images, by weighting odd-even symmetry matrix to replace original image matrix extracting features, and at last minimum distance classifier is used for classification. The...
In a content-based image retrieval (CBIR) system, rational and effective organization of the image database plays an important role in improving the performance of the system. In this paper, we propose a new method to classify the images database of CBIR system. Using SVM we attempt to construct a mapping between the low-level features and the semantically level in order to determine which category...
Several pixel-based people counting methods have been developed over the years. Among these the product of scale-weighted pixel sums and a linear correlation coefficient is a popular people counting approach. However most approaches have paid little attention to resolving the true background and instead take all foreground pixels into account. With large crowds moving at varying speeds and with the...
Detection and recognition of the moving objects in dynamic environment is difficult task. This paper presents a modified framework for the detection and recognition of moving people in videos. Detection part of the proposed method consists of average background model with supportive secondary model and an adaptive threshold selection model based on Gaussian distribution. The background model used...
In this paper, we propose a simple method for wine color characterization, classification and reproduction. The aim is to represent the colors of wines with limited number of hues that we call nuances. Burgundy wines (France) constitute the wine samples in this study but the method remains general. The method consists of four steps: spectral transmittance measures of a large number of wine samples...
Tracking problem can be formulated as the task of recovering the spatio-temporal trajectories for an unknown number of objects appearing and disappearing at arbitrary times. This work describes a modified mean shift clustering method for object detection. A human tracker based on the inter frame displacements of detected objects is proposed, where two different human classifiers based on size of detected...
In this paper we propose a new method for human action categorization by using an effective combination of a new 3D gradient descriptor with an optic flow descriptor, to represent spatio-temporal interest points. These points are used to represent video sequences using a bag of spatio-temporal visual words, following the successful results achieved in object and scene classification. We extensively...
Current clinical practice is increasingly moving in the direction of volumetric imaging. However, model observers for 3D images have been little explored so far. This study is investigating the task of detecting 2D signals in multi-slice simulated image data. We propose a novel design of a multi-slice model observer. To evaluate it, we compare three different model designs of the channelized Hotelling...
In this paper a multi-modal method for human identification that exploits the discrimination power of several movement types performed from the same human is proposed. Utilizing a fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA) based algorithm, an unknown movement is first classified, and, then, the person performing the movement is recognized from a movement specific person...
In this paper, we propose a large margin framework to learn the local instance-to-class distance function using local patch-based feature vectors, which satisfies the property that distance from instance to its own class should be less than the distance to other class. This instance-to-class distance is modeled as the weighted combination of the distance from every patch in test image to its nearest...
In this paper, we propose a novel method using gender information for achieving better performances of face recognition systems. Gender is one of the important factors for recognizing appearance of human faces and there are many studies on gender classifications such. However, the gender information is not actively applied in vision-based face recognition tasks, because we cannot find out human identity...
This paper presents an automatic system for morphological screening of the bladder cells. This system is intended to increase efficiency of the subsequent fluorescence in situ hybridization examination by limiting the number of suspicious cells. The system works in two major phases. The first phase is slide scanning. The second stage includes cells detection and morphological analysis. Both stages...
We present a biologically-motivated system to recognize human postures in realtime video sequences. The system employs event-based temporal difference image between video sequences as input and builds a network of bio-inspired Gabor-like filters to detect contours of the active object. The detected contours are organized into vectorial line segments. After feature extraction, a classifier based on...
Detecting humans and distinguishing them from natural fauna is an important issue in border security applications. In particular, it is important to detect and classify people who are walking in remote locations and transmit back detections over extended periods at a low cost and with minimal maintenance. Our simulation and measurement work has been relatively successful in providing a qualitative...
This paper addresses the problem of automatic pattern classification in real metallographic images from the steel plant ArcelorMittal Ostrava pic (Ostrava, Czech Republic). Images of manufactured metal plates contain dark dots, i.e. imperfections. We monitor the process quality in the steel plant by determining automatically the number and sizes of these dots which represent plates' imperfections...
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