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In the article a vision system for shape and colour recognition of dishes (plates, bowls, mugs), which can be used to automate the process of customer service in a self-service canteen is described. In consists of three basic components: object segmentation using so-called background model subtraction, shape recognition using geometric invariant moments and SVM classifier, as well as colour recognition...
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,...
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...
This work investigates the geometric object-shape classification using the echoes generated by various kinds of obstacles in a cellular automata based virtual environment for ultra-sound propagation. The virtual environment is implemented as a JAVA platform [1] capable of emulate sound propagation in a controlled 2D environment. The echoes are preprocessed by a Feature Processor Vector Unit (FVPU)...
This paper presents a method for touch-based gesture recognition that can be used in human-centered interfaces for ambient intelligence applications. Gestures are associated with shapes and they are represented using Fourier coefficients. Neural Networks, Decision Trees, Naïve Bayes and a set of classifiers (based on Linear Discriminant Analysis) are tested for gesture recognition. All these methods...
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...
This paper proposes a promising new approach to detect underwater threats in side scan sonar (SSS) images without machine learning procedure. Although object detection requires high reliability, the maritime environment changes unpredictably and dynamically. In order to accomplish high reliability for object detection systems, a huge number of the samples under various different environments are required...
Studying cortical anatomy by examining the deepest part of cortical sulci, the sulcal pits, has recently raised a growing interest. In particular, constructing structural representations from patterns of pits has proved a promising approach. This study follows up in this direction and brings two main contributions. First, we introduce a graph kernel adapted to sulcal pit graphs, in order to perform...
In this paper, we are mostly interested in investigating how the study and discovery of the human visual cortex could be utilised to improve the computational models for visual recognition by computer vision. Many of the brain perceptual abilities in vision have corresponding algorithms exist in computer vision, and in this paper we discuss three such models. First we present a model that has the...
Statistical shape models generally characterize shape variations linearly by principal component analysis (PCA), which assumes that the non-rigid shape parameters are drawn from a Gaussian distribution. This practical assumption is often not valid. Instead, we propose a constrained local model based on independent component analysis (ICA) and use kernel density estimation (KDE) for non-parametrically...
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...
Hidden Markov Models (HMM) are used in handwritten strokes recognition task. The two design parameters of HMM are the number of states and number of mixtures in each state. There are two approaches for finding the number of states, namely, equal number of states and variable number of states. Since the shape of strokes will be different, variable number of states approach should be beneficial. This...
Achieving sub-pixel accuracy with face alignment algorithms is a difficult task given the diversity of appearance in real world facial profiles. To capture variations in perspective, occlusion, and illumination with adequate precision, current face alignment approaches rely on detecting facial landmarks and iteratively adjusting deformable models that encode prior knowledge of facial structure. However,...
The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset...
This paper presents the detection and localization methods of entrance and staircase markers for the team E-Mobile in TechX Challenge 2013. Autonomous vehicles are required to detect and locate traffic cones beside the indoor entrance and staircase. One big challenge is from the unpredictable lighting conditions and environment. Different practical techniques such as color space selection, segmentation,...
Over the past few decades, a considerable amount of literature has been published on shape classification. Since classification of well-segmented shapes has become easy to achieve, a number of recent studies have emphasized the importance of robustness to noise and deformations. So in this paper, we undertake the task of classifying similar & noisy binary shape images, using a biologically inspired...
In this paper, we study the learning mechanisms that facilitate autonomous discovery of an effective affordance prediction structure with multiple actions of different levels of complexity. A robot can benefit from a hierarchical structure where pre-learned basic affordances are used as inputs to bootstrap learning of complex affordances. In a developmental setting, links from basic affordances to...
Gender recognition has important applications in apparel design, social security, and human-computer interaction systems. In this paper, we investigate gender-recognition technologies using 3-D human body shape. The front and side silhouettes from 459 female subjects and 107 male subjects were extracted and then modeled using normalized Elliptic Fourier descriptors. Principal Component Analysis (PCA)...
The localization of eye centers technique is used in several applications. In this paper, we combine the face shape model to eye center location method to obtaining robustness in eye center localization from a low-resolution image. We make use of shape regression approach and combine it to the eye-center localization using isophote curvature features which has shown its advantage from previous work...
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