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In today world the necessity for the autonomous mobile robots and vehicles is increasing. The safety autonomous moving demands the reliable and fast detection algorithms. The Histogram of Oriented Gradients (HOG) descriptors show significantly outperforms the existing feature sets for a human detection. Though the given method has a lot of type I errors. The amount of these errors can be decreased...
In this paper we propose an automatic marine life monitoring system. First task in the monitoring process is to detect underwater moving objects as fishes. Second Task is to identify the species of the detected fish. Third task is to track the detected fish to avoid multiple counting and record their activities. Detection is performed using GMM based background subtraction method, classification is...
The performance of pattern classifiers depends on the separability of the classes in the feature space — a property related to the quality of the descriptors — and the choice of informative training samples for user labeling — a procedure that usually requires active learning. This work is devoted to improve the quality of the descriptors when samples are superpixels from remote sensing images. We...
Autonomous monitoring of fruit crops based on mobile camera sensors requires methods to segment fruit regions from the background in images. Previous methods based on color and shape cues have been successful in some cases, but the detection of textured green fruits among green plant material remains a challenging problem. A recently proposed method uses sparse keypoint detection, keypoint descriptor...
This paper presents a classification system for video lectures and conferences based on Support Vector Machines (SVM). The aim is to classify videos into four different classes (talk, presentation, blackboard, mix). On top of this, the system further analyses presentation segments to detect slide transitions, animations and dynamic content such as video inside the presentation. The developed approach...
Knots as well as their density have a huge impact on the mechanical properties of wood boards. This paper addresses the issue of their automatic detection. An image processing pipeline which associates low level processing (contrast enhancement, thresholding, mathematical morphology) with bag-of-words approach is developed. We propose a SVM classification based on features obtained by SURF descriptors...
Logo identification and classification have received considerable attention from both the machine learning and computer vision communities. Vehicle logo recognition (VLR) is used to recognise accurately the manufacturer of a vehicle by using its iconic logo. A VLR system in addition to license plate recognition aims to increase the confidence of vehicle monitoring systems in private environments such...
Kernel-based Support Vector Machine (SVM) is widely used in many fields (e.g. image classification) for its good generalization, in which the key factor is to design effective kernel functions based on efficient features. In this paper, we propose a new approach that uses a combination of global and local image features to represent images and learns Support Vector Machine classifier with a new and...
This paper is concerned with multi-kernel extreme learning machine (MK-ELM) which adapts the multi-kernel learning (MKL) framework to extreme learning machine (ELM). MK-ELM approach iteratively determines the combination of kernels by gradient descent wrapping a standard optimization method based ELM. Such MKL methods are very useful in information fusion research and applications. MK-ELM's performance...
In this paper, we propose a novel approach based on two ideas, one using the trajectory extracted from videos as the local descriptors, and, second, using a similarity constrained latent support vector machine approach, to enforce the consistency of the selection of regions of interest generated by such trajectory based local extractors. We compare the performance of the proposed approach with other...
Young children need their parents' love and care but their parents are not always available to tell them stories. This motivates our proposal of a Smart Teddy Bear, a vision-based story teller, to assist young children in their social-emotional growth. When a young child wants to listen to a story, he or she simply opens any page in that book before a Smart Teddy Bear and the system automatically...
Because the trade-off between discriminative power and invariance varies from task to task, no single kind of feature is optimal in all situations. In this paper we build a generic scene recognition engine based on multiple cues which capture the different characteristics of the images. Our method can be equally applicable to a range of tasks including scene and object category recognition tasks....
In many visual classification tasks the spatial distribution of discriminative information is (i) non uniform e.g. person ‘reading’ can be distinguished from ‘taking a photo’ based on the area around the arms i.e. ignoring the legs and (ii) has intra class variations e.g. different readers may hold the books differently. Motivated by these observations, we propose to learn the discriminative spatial...
The problem of tracking people using multiple cameras is of much current interest as a means of providing cues for audiovisual blind source separation in dynamic environments. Here we investigate the use of one of the current state-of-the-art techniques in object recognition combined with one of the most popular methods of modelling object motion, particle filters, for tracking people. The dictionary...
Two methods to efficiently train kernelized support vector machines are introduced. Both of them apply stochastic gradient descent in the primal space. Different from previous fast stochastic kernel machines method [9] which drops old support vectors directly, one of the algorithms exploits the efficient representation of the histogram intersection kernel, the other one approximates the discarded...
The bag-of-keypoints representation started to be used as a black box providing reliable and repeatable measurements from images for a wide range of applications such as visual object recognition and texture classification. This order less bag-of-keypoints approach has the advantage of simplicity, lack of global geometry, and state-of-the-art performance in recent texture classification tasks. In...
This paper studies a method for learning a discriminative visual codebook for various computer vision tasks such as image categorization and object recognition. The performance of various computer vision tasks depends on the construction of the codebook which is a table of visual-words (i.e. codewords). This paper proposed a learning criterion for constructing a discriminative codebook, and it is...
Object recognition systems need effective image descriptors to obtain good performance levels. Currently, the most widely used image descriptor is the SIFT descriptor that computes histograms of orientation gradients around points in an image. A possible problem of this approach is that the number of features becomes very large when a dense grid is used where the histograms are computed and combined...
Most learning-based video semantic analysis methods require a large training set to achieve good performances. However, annotating a large video is laborintensive. This paper introduces how to construct the training set and reduce user involvement. There are four selection schemes proposed: clustering-based, spatial dispersiveness, temporal dispersiveness, and sample-based which can be used construct...
Traditional approaches in object class recognition utilize a large number of labeled visual examples in order to train classifiers to recognize the category of an object in a test image. However, the need for a large number of training data makes the scalability of this approach problematic. In this paper, we explore the recently proposed paradigm of attribute based category recognition for object...
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