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This paper presents a multiple classifier system (MCS) to identify plants species based on the texture and shape features extracted from leaf images. A diverse pool of SVM and Neural Network classifiers is trained on four different feature sets, namely, Local Binary Pattern (LBP), Histogram of Gradients (HOG), Speed of Robust Features (SURF) and Zernike Moments (ZM). Then, a static classifier selection...
Vehicle classification plays an important part in Intelligent Transport System. Recently, deep learning has showed outstanding performance in image classification. However, numerous parameters of the deep network need to be optimized which is time-consuming. PCANet is a light-weight deep learning network that is easy to train. In this paper, a new robust vehicle classification method is proposed,...
This paper describes a joint intensity metric learning method to improve the robustness of gait recognition with silhouette-based descriptors such as gait energy images. Because existing methods often use the difference of image intensities between a matching pair (e.g., the absolute difference of gait energies for the l1-norm) to measure a dissimilarity, large intrasubject differences derived from...
Offline signature identification and verification systems encounter several challenges such as the diversity of signatories and the limited number of references. To address these problems we propose a new writer-independent system for signature identification and verification. Besides, a new feature generation scheme is proposed by using the Histogram Of Templates (HOT). The identification and verification...
This paper presents an enhanced version of descriptor DPM-BCF (Depth Projection Maps-based Bag of Contour Fragments). Named as eDPM, it modified the projection method by converting the depth cloud into three grayscale projected maps in three orthogonal planes. Then we extract Bag of Contour Fragments (BCF) descriptor and Histogram of Oriented Gradient (HOG) descriptor from the three grayscale projected...
The detection of abrupt shot boundary is a fundamental task of video analytics and content-based video retrieval. The traditional methods tend to take much time in frame processing. In this paper, a GPU-accelerated abrupt shot boundary detection algorithm is proposed. This algorithm takes into account of both global feature and local feature of the video frames, in which the block HSV histograms and...
Starting from an object's location in a video frame, tracking-by-detection methods find the location of that object in a subsequent video frame. The tracker's detection step may produce multiple false positives during short-term occlusions, which can result in loss of track. We propose a tracking-by-detection method that is robust to short-term occlusions and false positives. Here, we extend the Struck...
We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from...
This paper presents a study on hand gesture distinguish ability between Speeded Up Robust Features(SURF) and Scale Invariant Feature Transform(SIFT) feature descriptors of hand images. Then bag of visual words are to map these descriptors to a dimension vector and support vector machine(SVM) classifer is trained to recognize hand gesture. Experimental results demonstrate that SURF feature descriptors...
Detecting infrared pedestrian in outdoor smart video surveillance is always a challenging and difficult problem. Although there have been many methods based on histograms of oriented gradients (HOG) to solve this problem, they would probably fail because of shelter and poor quality of image. To overcome this problem, we propose a robust feature to describe pedestrian which is called entropy-edge weighted...
An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT...
Indirect immunofluorescence (IIF) imaging is an important technique for detecting antinuclear antibodies in HEp-2 cells and therefore employed in the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells are often categorised into six groups (homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and centromere cells), which...
Real-time human detection in crowded and dynamic environments poses a significant challenge, due to complex background, occlusion and different human poses. In this paper, we propose a two-staged approach using color and depth data taken by an RGB-D camera. The first stage is to find plausible head-top locations quickly in depth image. The second stage is to extract effective discrimination features...
Feature extraction methods have an important role in image classification. In this paper, a hybrid texture feature descriptor is proposed by utilizing the attributes of two complementary features, PRICoLBP and LPQ. PRICoLBP performs well in the case of geometric and photometric variations however it does not properly express the local texture of an image, while LPQ method performs well for the local...
In this document an algorithm is proposed to identify the state (available/occupied) of the parking spaces in outdoor areas. The algorithm was developed based on two features: the average local entropy, and the standard deviation of the average entropies of subregions of each parking space. The algorithm delivers a binary map, which contains the number of each parking space with its attributes such...
Image classification is a crucial task in Computer Vision. Feature detection represents a key component of the image classification process, which aims at detecting a set of important features that have the potential to facilitate the classification task. In this paper, we propose a Genetic Programming (GP) approach to image feature detection. The proposed method uses the Speeded Up Robust Features...
The proposed system comes in the context of intelligent parking lots management and presents an approach for vacant parking spots detection and localization. Our system provides a camera-based solution, which can deal with outdoor parking lots. It returns the real time states of the parking lots providing the number of available vacant places and its specific positions in order to guide the drivers...
The problem of incorporating spatial information to the bag-of-visual-words model for image classification is addressed in this letter. To incorporate such information, we propose to encode the global geometric relationships of the visual words in the image plane in a scale- and rotation-invariant manner. This is established by measuring scale- and rotation-invariant geometrical properties given...
LBP (Local Binary Pattern) is a commonly used operator to extract LBPH (LBP histogram) of an image for local texture description. For gender classification, we proposed an innovative method by extracting multi-scale LBPH in DoG (Difference of Gaussian) space in this paper. Given a facial image, we firstly preprocess it meticulously to avert the local variations of images which probably be caused by...
A novel and robust biometric face identification algorithm for access control applications is proposed. The key contribution is the design of a high discriminative feature descriptor for depth imagery, called Depth Spatiogram of Local Quantized Patterns, which is used as input of a bank of Support Vector Machine classifiers.
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