The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper we apply particle swarm optimization (PSO) feature selection to enhance Hidden Markov Model (HMM) states and parameters for face recognition systems. Ideal Feature selection for face images based on the idea of collaborative behavior of bird flocking to reduce the feature size and hence recognition time complicity. The framework has been inspected on 400 face pictures of the Olivetti...
In this paper we present a novel way of applying Zernike moments for image matching. Zernike moments are obtained from projecting image information under a circumscribed circle to Zernike basis function. However, the problem is that the power of discrimination may be reduced because hand images include lots of overlapped information due to their shape characteristic. On the other hand, in the pose...
Branch Retinal Vein Occlusion (BRVO) is one of the most common retinal diseases that could impair people's vision seriously if it is not timely diagnosed and treated. It would save a lot of time and money for both medical institutions and patients if BRVO could be well recognized automatically. In this paper, we propose to exploit Convolutional Neural Networks (CNN) for BRVO recognition. We propose...
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...
Compared to image representation based on low-level local descriptors, deep neural activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation, but poorer in geometric invariance properties. In this paper, we present a straightforward framework for better image representation by combining the two approaches. To take advantages of both representations, we extract a fair...
In this paper, we examined the effectiveness of deep convolutional neural network (DCNN) for food photo recognition task. Food recognition is a kind of fine-grained visual recognition which is relatively harder problem than conventional image recognition. To tackle this problem, we sought the best combination of DCNN-related techniques such as pre-training with the large-scale ImageNet data, fine-tuning...
A key development in the design of visual object recognition systems is the combination of multiple features. In recent years, various popular optimization based feature combination methods have been proposed in the literatures. However, those methods obtain tiny performance improvement at the cost of enormous computation consumption. In this paper, we propose an improved averaging combination (IAC)...
Digital Out Of Home (DOOH) applications which exploit computer vision algorithms to automatically collect soft biometrics of people in front a smart screen are of great interest for industry. In the last years many gender recognition pipelines have been proposed in literature. Different benchmark datasets have been introduced and used for testing purpose. This paper gives an overview of the state-of-the-art...
Convolutional Neural Network (CNN) is efficient in learning hierarchical features from large image datasets, but its model complexity and large memory foot prints are preventing it from being deployed to devices without a server back-end support. Modern CNNs are always trained on GPUs or even GPU clusters with high speed computation capability due to the immense size of the network. A device based...
Improvement of character recognition technology brings us various character recognition applications for mobile camera. However, many low-resolution and poor-quality character images exist due to the performance of the camera or the influence of environment, and existing methods are not good at recognizing those low-resolution characters. Therefore, we develop a character recognition system for ultra-low...
Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Although Bangla is a major language in Indian subcontinent and is the first language of Bangladesh study regarding Bangla handwritten numeral recognition (BHNR) is very few with respect to other major languages such Roman. The existing BHNR methods uses distinct feature extraction...
Face recognition is the process of identification of a person by their facial images. This technique makes it possible to use the facial image of a person to authenticate him into a secure system. Face is the main part of human being to be distinguished from one another. Face recognition system mainly takes an image as an input and compares this image with a number of images stored in database to...
As modern technology evolves, the use of face recognition system is scattering in different sectors of commercial markets rather than in security purposes only. Various approaches are introduced for face recognition system, among them principal component analysis is one of the simplest and efficient method. To improve the performance of face recognition, choosing a threshold value and minimum number...
Blood vessel extraction from retinal fundus images is an important task in developing the computer-aided diagnostic system for ophthalmologists. In this paper we have presented an algorithm for extraction of blood vessels of retinal fundus images and comparison of different moment invariants used for the extraction of features for the vessel pixels. The algorithm uses neural networks for distinguishing...
The paper is focuses on using hybridization of multiple features with different classifiers for the purpose of recognition of isolated handwritten Gurmukhi character images. We have tested four different types of features named as Histogram Oriented Gradient (HOG), Distance Profile, Background Directional Distribution (BDD) and Zonal Based Diagonal (ZBD). HOG feature is computed by information of...
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...
Character segmentation plays an important role in Optical Character Recognition (OCR) for Indian Script. The image to be segmented consists of various characters and is broken down into different segmented images of individual characters. These segmented characters are then fed to the Recognition phase of OCR. This paper covers only the segmentation of Devnagari script, as it consists of vowels, consonants...
Hierarchical Temporal Memory (HTM) serves as a practical implementation of the memory prediction theory. In order to obtain the optimum accuracy in pattern recognition, it is crucial to apply an appropriate learning algorithm for the feature extraction step of the HTM. This study proposes the use of neocognitron learning in extracting features of the pattern for image recognition. The integration...
A sound signal produces a unique texture which can be visualized using a spectrogram image and analyzed for automatic sound recognition. In this paper, we explore the use of a well-known image texture analysis technique called the gray-level co-occurrence matrix (GLCM) for sound recognition in an audio surveillance application. The GLCM captures the distribution of co-occurring values at a given offset...
In this paper, a simple and effective approach for the recognition of hand gestures from very low resolution images is proposed. Enhancement of the low resolution images has always been the key focus in the processing of the digital images. Images with resolution as low as [50×50 pixels] are also considered for recognition. The gestures under consideration here are the number of fingers (one, two,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.