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Discriminative features of 3-D meshes are significant to many 3-D shape analysis tasks. However, handcrafted descriptors and traditional unsupervised 3-D feature learning methods suffer from several significant weaknesses: 1) the extensive human intervention is involved; 2) the local and global structure information of 3-D meshes cannot be preserved, which is in fact an important source of discriminability;...
The amount of data produced every day on the internet increases every day and with the increasing popularity of the social networks the number of published photos are huge, and those pictures contain several implicit or explicit brand logos. Detecting this logos in natural images can provide information about how widespread is a brand, discover unwanted copyright distribution, analyze marketing campaigns,...
Retrieving images depend of specific features In Content Based Image Retrieval (CBIR). A common approach is to divide retrieval process into two stages; the first one is based on high-level features followed by the second that is based on low-level features. We focus primarily on medical images, and follow the above approach but make the following two basic contributions: a) introduce the gray cluster...
Text segmentation is an important problem in document analysis related applications. We address the problem of classifying connected components of a document image as text or non-text. Inspired from previous works in the literature, besides common size and shape related features extracted from the components, we also consider component images, without and with context information, as inputs of the...
The quality of life of people is increasing together with the developing technologies. One of the most important factors affecting daily life is smart cities. The quality of life of people is positively affected by emerging this concept in recent years. Autonomous vehicles confront with the term of the smart city and have become even more popular in recent years. In this study, a system of traffic...
The frequent occurrence of road congestion and traffic accidents has affected people's travel efficiency and travel safety. Traffic sign recognition has become one of the key research objects in intelligent transportation system. This paper studies the identification of road traffic signs based on video images. First of all, collected image will be image preprocessing with image reduction, brightness...
Digital image processing techniques are commonly employed for food classification in an industrial environment. In this paper, we propose the use of supervised learning methods, namely multi-class support vector machines and artificial neural networks to perform classification of different type of almonds. In the process of defining the feature vectors, the proposed method has relied on the principal...
Cell nucleus characterization is essential for pathological image analysis. In this article, we propose a shape characterization of the cell nucleus based on some selected shape descriptors. In order to separate automatically the different cell nucleus shapes, we focus our effort on the feature extraction and classification processes. The main contribution of our work is the creation of a new shape...
Different types of traffic signs has different colors and shapes located in uncontrolled traffic environments. The detection of different types of traffic signs is a difficult problem in pattern recognition and computer vision. In our study, a region of interest (ROI) extraction method is proposed to extract ROI using color contrast in local regions. We utilize the high contrast in local regions to...
The idea of lip reading as a visual technique which people may use to translate lip movement into phrases without relying on speech itself is fascinating. There are numerous application areas in which lip reading could provide full assistance. Although there may be a downside to using the lip reading system, whether it may range from problems such as time constraint to minor word recognition mistakes,...
Our objective is to create a system that enables users to interact with surrounding surfaces by using touch interactions. In this work, we propose a touch detection method that utilizes the shadows of a finger for use with a system featuring an infrared (IR) camera and two IR lights. Since the shape of a finger's shadow varies depending on the distance between the surface and the finger, the system...
Automated segmentation of cell nuclei is crucial for the early diagnosis of cancer as the characteristics of the cell nuclei are mainly associated with the assessment of malignancy. Only a few research work has been done on automated segmentation of cell nuclei on cytology pleural effusion images, which is poorly handled by previous methods. In addition, cytology pleural effusion image itself is still...
Because visually impaired persons are not able to confirm the appearance of their own face, they are afraid of and uneasy about makeup. We have been developing a system that assists makeup application through verbal feedback according to the appearance of the user's face. The system encourages social communication by helping the user feel confident. In this paper, we introduce a new method of using...
This paper proposes a novel method for fire and smoke detection using video images. The ViBe method is used to extract a background from the whole video and to update the exact motion areas using frame-by-frame differences. Dynamic and static features extraction are combined to recognize the fire and smoke areas. For static features, we use deep learning to detect most of fire and smoke areas based...
3D face recognition is a popular research area due to its vast application in biometrics and security. Local feature-based methods gain importance in the recent years due to their robustness under degradation conditions. In this paper, a novel high-order local pattern descriptor in combination with sparse representation based classifier (SRC) is proposed for expression robust 3D face recognition....
Gender recognition from face images is a challenging problem with applications in various knowledge domains, such as biometrics, security and surveillance, human-computer interaction, among others. In this work, we propose and evaluate a novel method for gender recognition based on a geometric descriptor constructed from a pre-defined face shape model. The proposed approach, tested on four different...
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...
This paper deals with classification algorithms as one of the basic principles of pattern recognition. We analyze their effect to a feature space and compare the type and the shape of the separating and decision surface, respectively. We proposed a novel classification approach based on Cumulative Fuzzy Membership Function that creates a decision surface in a different way as an MF ARTMAP neural network...
This work proposes an off-line handwritten signature identification system using the Histogram of Symbolic Representation (HSR). The HSR is considered as one-class classifier which has the ability to generate a model for each writer using only its own reference signatures. This method allows also modeling the writing style of each writer by taking into account the variability of signatures. To evaluate...
Person re-identification in public areas (such as airports, train stations and shopping malls) has recently received increased attention within computer vision research due, in part, to the demand for enhanced levels of security. Re-identifying subjects within non-overlapped camera networks can be considered as a challenging task. Illumination changes in different scenes, variations in camera resolutions,...
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