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This paper presents a comparison between the performances of LBP and Haar features in a Cascade Classifier, which aims automatically to count immature whiteflies (nymphs). The relevance of counting these nymphs is related to pest management, which leads to an increase in food production. Evaluate differences in weak classifiers can lead to better understanding and implementation of object detection...
The problem of optimizing classifiers for object detection has already been discussed in several publications. In order to achieve better results, it was decided to use genetic algorithms to optimize the classifiers. By applying this approach optimization is automatic in respect to image (or group of images). For test issues the haar-like object detection features were used. Genetic model has been...
This paper presents a fuzzy traffic controller that in an autonomous, centralized and optimal way, manages traffic flow in a group of intersections. The system obtains information from a network of cameras and through machine vision algorithms can detect the number of vehicles in each of the roads. Using this information, the fuzzy system selects the sequence of phases that optimize traffic flow globally...
The problem of object detection in image and video has been treated by a large number of researchers. Many design factors degrade the reliability of the problem solutions, such as manual modeling of the object, manual features selection, handcrafting architecture, and learning algorithm selection. Here, a generalized object detection and localization system is presented. It has the ability to learn...
We report on an image classification task originated from the video observation of beehives. Biologists desire to have an automatic support to identify individual bees which are labelled with badges. Current state of the art in object detection and evaluation of classifiers is briefly reviewed. Different algorithms are evaluated. ROC- as well as precision-recall analysis show that a gradient based...
Object categorization has become active in the field of pattern recognition. There are two main factors which affect the performance of classification. One is the representation of images, and the other is the design of classifier. The representation of images based on bag-of-word (BOW) has become a popular method because of its simpleness and high efficiency. This paper aims to compare some state-of-the-art...
The system presented in this paper finds images and line-drawings in scanned pages; it is a crucial processing step in the creation of a large-scale system to detect and index images found in books and historic documents. Within the scanned pages that contain both text and images, the images are found through the use of SIFT-based local-features applied to the complete scanned-page. This is followed...
Typical object detection systems work by training a classifier on features extracted at different scales of an object. In this paper we investigate the performance of an object detection system in which different classifiers which are trained at various scales of an object are combined and compare the performance with a typical object detection system where a single classifier is trained for all the...
In this paper we discuss the issue of classifiers combined with histogram of oriented gradients (HOG) descriptors for human detection. And we present a method that combines AdaBoost learning with HOG descriptors. The weak learners used in our algorithm are based on weighted modified quadratic discriminant functions (MQDF) which is a parametric model. We evaluate our algorithm on the INRIA person dataset...
Object detection can be challenging when the object class exhibits large variations. One commonly-used strategy is to first partition the space of possible object variations and then train separate classifiers for each portion. However, with continuous spaces the partitions tend to be arbitrary since there are no natural boundaries (for example, consider the continuous range of human body poses)....
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