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We propose a robust real-time person detection system, which aims to serve as solid foundation for developing solutions at an elevated level of reliability. Our belief is that clever handling of input data correlated with efficacious training algorithms are key for obtaining top performance. We introduce a comprehensive training method based on random sampling that compiles optimal classifiers with...
Plants are fundamental for human beings, so it's very important to catalog and preserve all the plants species. Identifying an unknown plant species is not a simple task. Automatic image processing techniques based on leaves recognition can help to find the best features useful for plant representation and classification. Many methods present in literature use only a small and complex set of features,...
Histograms of Oriented Gradients (HOG) feature has been successfully used in pedestrian detection and achieves high accuracy. This paper introduces a content retrieval algorithm based on improved HOG. The method has two steps which are adjusting the HOG structure by scanning the image with a sliding HOG window and reducing feature dimension by principle component analysis (PCA) technique. The experimental...
This paper proposes a methodology for recognition of plant species by using a set of statistical features obtained from digital leaf images. As the features are sensitive to geometric transformations of the leaf image, a pre processing step is initially performed to make the features invariant to transformations like translation, rotation and scaling. Images are classified to 32 pre-defined classes...
Action recognition is a challenging task due to intra-class motion variation caused by diverse style and duration in performed action videos. Previous works on action recognition task are more focused on hand-crafted features, treat different sources of information independently, and simply combine them before classification. In this paper we study action recognition from depth sequences captured...
In this paper, we present a grid structured morphological pattern spectrum based approach for off-line signature verification. The proposed approach has three major phases: preprocessing, feature extraction and verification. In the feature extraction phase, the signature image is partitioned into eight equally sized vertical grids and grid structured morphological pattern spectra for each grid is...
In this paper, Krawtchouk invariant moments are used as features for object recognition. For hand images, the performance of Krawtchouk moments in terms of recognition accuracy, rotational invariance, scale invariance, computational time and feature vector size, has been analysed. A user independent dataset for 21 subjects under varying illumination conditions is created. A comparative analysis with...
In this paper, Histograms of Orientation Gradient (HOG) algorithm is used to identify the static hand gestures. Experimental results show that HOG descriptor is a better shape descriptor than existing feature sets for gesture recognition. The overall algorithm has only three main steps; pre-processing, feature extraction and classification. It completely omits the segmentation phase. SVM is used for...
Efficient and accurate extraction of physically-relevant features from measured radar data is desirable for automatic target recognition (ATR). In this paper, we present an estimation technique to find credible sets of parameters for any given feature model. The proposed approach provides parameter estimates along with confidence values. Maximum a posteriori (MAP) estimates provide a single (vector)...
This paper presents new class of time-frequency (T-F) features for automatic detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals in different spectral sub-bands. The proposed features based on image descriptors are extracted from the T-F representation...
Accurate and automatic detection and delineation of cervical cells are two critical precursor steps to automatic Pap smear image analysis and detecting pre-cancerous changes in the uterine cervix. To overcome noise and cell occlusion, many segmentation methods resort to incorporating shape priors, mostly enforcing elliptical shapes (e.g. [1]). However, elliptical shapes do not accurately model cervical...
Plant leaves provide sufficient features to distinguish them among other species. Identification of plants using leaf images is a classic problem in digital image processing. Usually those image processing systems use shape based digital morphological features for leaf identification task. Even there are number of studies on leaf based plant identification, very few of them are for mobiles. In this...
The automatic recognition of planes in aerial images is an important application in the image analysis field. However, it remains a problem despite many years of work due to the arbitrary original poses and the variation in the shapes of planes. This paper proposes a novel approach for automatic aircraft detection based on statistical theory and common features of different kinds of planes. Experiments...
Character recognition techniques for printed documents are widely used for English language. However, the systems that are implemented to recognize Asian languages struggle to increase the accuracy of recognition. Among other Asian languages (such as Arabic, Tamil, Chinese), Sinhala characters are unique, mainly because they are round in shape. This unique feature makes it a challenge to extend the...
Preprocessing and fusion techniques for finger vein recognition are investigated. An experimental study involving a set of preprocessing approaches shows the importance of selecting the appropriate single technique and the usefulness of cascading several different preprocessing methods for subsequent feature extraction of various types. Score level fusion is able to significantly improve recognition...
Development of Optical Character Recognition (OCR) for printed Roman script is still an active area of research. Automatic Style Identification (ASI) can be used to improve the performance of OCR system and keyword spotting techniques for printed Roman script. This paper proposes a two stage font invariant technique for detection of italic, bold, underlined, normal and all capital styled words for...
Face recognition is an important technique for Natural User Interface (NUI) and Human Robot Interaction (HRI) and many of the current state-of-the-art face recognition techniques are based on the local features which are extracted from a face alignment method like Constrained Local Model (CLM). But, in a real world environment, face alignment methods often fail to correctly localize the features because...
Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (Conv Net) features. We introduce a range of condition variations to explore the robustness of these features, including: translation,...
EEG based upper limb rehabilitation has limitation on the control commands of neuro-prosthetics cannot deal with human's real movements. To resolve this problem, it is important to know about neural correlation of the directions of arm movement. Previous studies classified the directions of arm movement, using center-out task, only including y-z-axis movement. In this research, 4 subjects participated...
Many plants are facing the risk of extinction due to unplanned urbanization and over growth of population. Digital databases of plants should be maintained for proper tracking of local flora and making data-driven policies/decisions for their preservation. Plant identification is important for medical as well as educational purposes but maintaining an exhaustive digital database is a challenging task...
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